phd in math and computer science

Graduate Degree in Computing + Mathematical Sciences

The Computing and Mathematical Sciences (CMS) PhD program is a unique, new, multidisciplinary program at Caltech involving faculty and students from computer science, electrical engineering, applied math, economics, operations research, and even the physical sciences. The program sets high standards for admission and graduation, and boasts a broad collection of world-class faculty (any faculty at Caltech from any of the areas above can advise students).

Disciplines across the information sciences are experiencing an unprecedented convergence. As different areas interact, new fields are emerging. For example, combining Computer Science with...

...Optimization and Statistics has led to machine learning, "big data," and the field of data science. ...Control and Electrical Engineering has led to the smart grid, smart buildings, and the internet of things. ...Physics has led to quantum computing and quantum information theory. ...Economics has led to algorithmic game theory, privacy, and the field of network science. ...Biology and Electrical Engineering has led to bioinformatics, molecular programming, and biomolecular circuits.

Because of this convergence, a new intellectual core is emerging in the information sciences. The core contains material from a spectrum of disciplines: algorithms, networks, machine learning, statistics, optimization, signal processing, and the underlying mathematics. But each area is enriched by the broader context. For instance, the study of algorithms now encompasses the traditional discrete problems of computer science, the continuous problems of applied mathematics, as well as worst-case and average-case perspectives.

The CMS PhD program is designed around the new information science core. This core provides the ideal foundation for future applications across the sciences, engineering, and beyond. Our approach requires the mastery of the following ways of thinking about information science:

Interpret "information" and "computation" broadly. We study mechanisms that communicate, store, and process information. These structures might be etched in silicon and called hardware or written in code and called software. But the same mechanisms may be expressed in nucleotides and called DNA. They also arise in our society, where they are called social networks or markets. Our view encompasses all of these manifestations.
Algorithmic thinking is the foundation. The modern world demands the ability to think algorithmically. Algorithms are not just the basis for advanced computer systems, but they help us understand biological organisms and auction design and more.
Data is central. Data is being collected at an unprecedented speed and scale. Every area of science and society will be transformed as researchers learn to use this data to develop and test new hypotheses. To unlock this potential, we need to develop reliable algorithms for extracting information and making decisions based on data.
Seek rigor and relevance. The CMS Program focuses on the theoretical core of information science. We believe that principled and rigorous methods provide the only solid basis for progress. But we also insist on research that is relevant to applications, and we train students to work at the interface of information science and other disciplines.

Students may select a research adviser from any of the 30+ faculty affiliated with the CMS Department, including specialists in Applied & Computational Mathematics, Biological Engineering, Computation & Neural Systems, Computer Science, Control & Dynamical Systems, Economics, Electrical Engineering, Mechanical Engineering, Philosophy, and Physics.

Graduate Program Details and Requirements

Requirements for the Computing and Mathematical Sciences graduate program are listed in the current Caltech Catalog .

Further details and advice can be found here: Navigating the Ph.D. Options in CMS

Graduate Options Administrator

Maria Lopez [email protected] (626) 395-3034

Graduate Option Representative

Yisong Yue Computing and Mathematical Sciences Option Representative

Academic Programs

CSE PhD Overview
Dept-CSE PhD Overview
CSE Doctoral Theses
Program Overview and Curriculum
For New CCSE Students
Terms of Reference

MIT Doctoral Programs in Computational Science and Engineering

The Center for Computational Science and Engineering (CCSE) offers two doctoral programs in computational science and engineering (CSE) – one leading to a standalone PhD degree in CSE offered entirely by CCSE (CSE PhD) and the other leading to an interdisciplinary PhD degree offered jointly with participating departments in the School of Engineering and the School of Science (Dept-CSE PhD).

While both programs enable students to specialize at the doctoral level in a computation-related field via focused coursework and a thesis, they differ in essential ways. The standalone CSE PhD program is intended for students who intend to pursue research in cross-cutting methodological aspects of computational science. The resulting doctoral degree in Computational Science and Engineering is awarded by CCSE via the the Schwarzman College of Computing. In contrast, the interdisciplinary CSE PhD program is intended for students who are interested in computation in the context of a specific engineering or science discipline. For this reason, this degree is offered jointly with participating departments across the Institute; the interdisciplinary degree is awarded in a specially crafted thesis field that recognizes the student’s specialization in computation within the chosen engineering or science discipline.

For more information about CCSE’s doctoral programs, please explore the links on the left. Information about our application and admission process is available via the ‘ Admissions ‘ tab in our menu. MIT Registrar’s Office provides graduate tuition and fee rates as set by the MIT Corporation and the Graduate Admissions section of MIT’s Office of Graduate Education (OGE) website contains additional information about costs of attendance and funding .

Department of Mathematics

Joint phd program in mathematics and computer science.

In Winter 2018, the Department of Mathematics and the Department of Computer Science launched a joint program through which participating students can earn the degree

The basic structure is that students must gain admission to both PhD programs and satisfy both sets of course requirements. They write a single dissertation that satisfies both programs.

While the program is open to all eligible students, we expect that at least initially it will be most popular among students working in CS Theory, Discrete Mathematics, and Mathematical Logic.

Each student in this program will have a primary program (either Mathematics or Computer Science). Throughout the course of studies, the primary program will provide administrative support to the student, including in matters regarding financial support.

To be admitted to the joint program, students will have to be admitted by both departments as follows.

Application before entering the PhD program

The applicant must apply to the primary program indicating in the application that he/she wishes to be considered for the joint program. If admitted to the primary program, the application will be automatically forwarded to the Graduate Admissions Committee of the secondary program. To be assisted in making the decision, the Graduate Admissions Committee of the secondary program may request from the applicant additional materials in accordance with customs and rules of its department.

Application after entering the PhD program

Students enrolled in either the Mathematics or the Computer Science PhD program may apply to the joint program during the first four years in their current program. If admitted to the joint program, their current program will be primary.

Such an applicant must submit the following material to the Director of Graduate Studies/Graduate Committee Chair of the intended secondary program, while notifying the Director of Graduate Studies/Graduate Committee Chair of the primary program:

statement of purpose, explaining why the joint program is the right program for the applicant
statement of coursework and research done so far
statement of a schedule how the applicant proposes to satisfy the secondary program's requirements
advisor's recommendation, including endorsement of the applicant's statements (items 2, 3, 4 above)
if the application occurs during the first year of the primary program, the applicant needs to provide two additional letters of recommendation and his/her undergraduate transcript. It is permitted to reuse material from the application to the primary program.

Requirements, monitoring

Course requirements.

Students enrolling in the joint program will need to satisfy the course requirements of both departments. They will have to satisfy the course requirements of their primary program on the schedule of that program, and satisfy the course requirements of their secondary program by the end of their fifth year in the primary program.

According to current rules, two of the CS electives can be courses offered by the Mathematics department. These courses are permitted to overlap with the Mathematics course requirements.

Exam Requirements

Students in the joint program shall fulfill the examination requirements of the primary program; the current list of requirements can be found at

Computer Science Requirements
Mathematics Requirements

For students participating in the joint program, the deadlines for these exams can be relaxed by petitioning the Director of Graduate Studies/Graduate Committee Chair of the primary program.

Monitoring student progress

Students' annual progress reports go to both departments' Director of Graduate Studies/Graduate Committee Chair in accordance with each department's format.

PhD dissertation and defense

Subject of the dissertation.

The dissertation is expected to be in an area relevant to both fields.

PhD thesis defense

The scheduling of the PhD Thesis defense follows the Mathematics Department's custom as follows.

A nearly final draft of the thesis is made accessible to faculty at least two weeks prior to the defense, either in hard copy in the departmental office or, preferably, by posting on the internet.
The dissertation is reviewed in writing by two readers, one of whom is typically the thesis advisor.
The reports by the first and the second readers are circulated among faculty of both departments, along with the Thesis abstract and the following information: the location (physical or virtual) where the thesis can be viewed, the planned time and location of the defense, and the names and affiliations of the thesis committee members.
There is a two-week period for comments by faculty before the admission of the thesis for defense.

The thesis defense itself consist of a 50-minute public presentation of the main results and methods of the dissertation, followed by a public question-answer period, followed by a closed-session question-answer period.

Oversight, committees

The program proceeds under joint Math-CS oversight, exercised by the Director of Graduate Studies/Graduate Committee Chair of each department.

Examination committees

The following rules apply to all examination committees (Qualifying/Topic Exam, Master's, Candidacy, and PhD). The committee will consist of at least three members, including the student's advisor(s). It will include at least one member of each department, and will either be chaired by a joint appointee of the two departments or co-chaired by a member of each department. Each department shall publicize these exams in accordance with its established customs.

Computer Science, PhD

Computer science phd degree.

In the Computer Science program, you will learn both the fundamentals of computation and computation’s interaction with the world. Your work will involve a wide range of areas including theoretical computer science, artificial intelligence and machine learning, economics and computer science, privacy and security, data-management systems, intelligent interfaces, operating systems, computer graphics, computational linguistics, robotics, networks, architectures, program languages, and visualization.

You will be involved with researchers in several interdisciplinary initiatives across the University, such as the Center for Research on Computation and Society , the Data Science Initiative , and the Berkman Klein Center for Internet and Society .

Examples of projects current and past students have worked on include leveraging machine learning to solve real-world sequential decision-making problems and using artificial intelligence to help conservation and anti-poaching efforts around the world.

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Computer Science Degree

Harvard School of Engineering offers a Doctor of Philosophy (Ph.D) degree in Computer Science , conferred through the Harvard Kenneth C. Griffin Graduate School of Arts and Sciences. Prospective students apply through Harvard Griffin GSAS; in the online application, select “Engineering and Applied Sciences” as your program choice and select "PhD Computer Science" in the Area of Study menu.

In addition to the Ph.D. in Computer Science, the Harvard School of Engineering also offers master’s degrees in Computational Science and Engineering as well as in Data Science which may be of interest to applicants who wish to apply directly to a master’s program.

Computer Science Career Paths

Graduates of the program have gone on to a range of careers in industry in companies like Riot Games as game director and Lead Scientist at Raytheon. Others have positions in academia at University of Pittsburgh, Columbia, and Stony Brook. More generally, common career paths for individuals with a PhD in computer science include: academic researcher/professor, industry leadership roles, industry research scientist, data scientist, entrepreneur/startup founder, product developer, and more.

Admissions & Academic Requirements

Prospective students apply through the Harvard Kenneth C. Griffin Graduate School of Arts and Sciences (Harvard Griffin GSAS). In the online application, select “Engineering and Applied Sciences” as your program choice and select "PhD Engineering Sciences: Electrical Engineering." Please review the admissions requirements and other information before applying. Our website also provides admissions guidance , program-specific requirements , and a PhD program academic timeline . In the application for admission, select “Engineering and Applied Sciences” as your degree program choice and your degree and area of interest from the “Area of Study“ drop-down. PhD applicants must complete the Supplemental SEAS Application Form as part of the online application process.

Academic Background

Applicants typically have bachelor’s degrees in the natural sciences, mathematics, computer science, or engineering.

Standardized Tests

GRE General: Not Accepted

Computer Science Faculty & Research Areas

View a list of our computer science faculty and computer science affiliated research areas . Please note that faculty members listed as “Affiliates" or "Lecturers" cannot serve as the primary research advisor.

Computer Science Centers & Initiatives

View a list of the research centers & initiatives at SEAS and the computer science faculty engagement with these entities .

Graduate Student Clubs

Graduate student clubs and organizations bring students together to share topics of mutual interest. These clubs often serve as an important adjunct to course work by sponsoring social events and lectures. Graduate student clubs are supported by the Harvard Kenneth C. Griffin School of Arts and Sciences. Explore the list of active clubs and organizations .

Funding and Scholarship

Learn more about financial support for PhD students.

How to Apply

Learn more about how to apply or review frequently asked questions for prospective graduate students.

In Computer Science

First-Year Exploration
Concentration Information
Secondary Field
Senior Thesis
AB/SM Information
Student Organizations
PhD Timeline
PhD Course Requirements
Qualifying Exam
Committee Meetings (Review Days)
Committee on Higher Degrees
Research Interest Comparison
Collaborations
Cross-Harvard Engagement
Lecture Series
Clubs & Organizations
Centers & Initiatives
Alumni Stories

Department of Mathematics

Joint math/cs phd program.

In Winter 2018, the Department of Mathematics and the Department of Computer Science launched a joint program through which participating students can earn the degree “Ph. D. in Mathematics and Computer Science.”

The basic structure is that students must gain admission to both PhD programs and satisfy both sets of course requirements. They write a single dissertation that satisfies both programs.

While the program is open to all eligible students, we expect that at least initially it will be most popular among students working in CS Theory, Discrete Mathematics, and Mathematical Logic.

To be admitted to the joint program, students will have to be admitted by both departments as follows.

Application after entering the PhD program

statement of purpose, explaining why the joint program is the right program for the applicant
statement of coursework and research done so far
statement of a schedule how the applicant proposes to satisfy the secondary program's requirements
advisor's recommendation, including endorsement of the applicant's statements (items 2, 3, 4 above)
if the application occurs during the first year of the primary program, the applicant needs to provide two additional letters of recommendation and his/her undergraduate transcript. It is permitted to reuse material from the application to the primary program.

Requirements, monitoring

Course requirements.

According to current rules, two of the CS electives can be courses offered by the Mathematics department. These courses are permitted to overlap with the Mathematics course requirements.

Exam Requirements

Students in the joint program shall fulfill the examination requirements of the primary program; the current list of requirements can be found at

Computer Science Requirements
Mathematics Requirements

For students participating in the joint program, the deadlines for these exams can be relaxed by petitioning the Director of Graduate Studies/Graduate Committee Chair of the primary program.

Monitoring student progress

Students' annual progress reports go to both departments' Director of Graduate Studies/Graduate Committee Chair in accordance with each department's format.

PhD dissertation and defense

Subject of the dissertation.

The dissertation is expected to be in an area relevant to both fields.

PhD thesis defense

The scheduling of the PhD Thesis defense follows the Mathematics Department's custom as follows.

A nearly final draft of the thesis is made accessible to faculty at least two weeks prior to the defense, either in hard copy in the departmental office or, preferably, by posting on the internet.
The dissertation is reviewed in writing by two readers, one of whom is typically the thesis advisor.
The reports by the first and the second readers are circulated among faculty of both departments, along with the Thesis abstract and the following information: the location (physical or virtual) where the thesis can be viewed, the planned time and location of the defense, and the names and affiliations of the thesis committee members.
There is a two-week period for comments by faculty before the admission of the thesis for defense.

Oversight, committees

The program proceeds under joint Math-CS oversight, exercised by the Director of Graduate Studies/Graduate Committee Chair of each department.

Examination committees

Georgia Institute of Technology College of Sciences

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Doctoral Programs

PhD in Computational Sciences and Engineering

The PhD in CSE is a highly interdisciplinary program designed to provide students with practical skills and theoretical understandings needed to become leaders in the field of computational science and engineering. The program emphasizes the integration and application of principles from mathematics, science, engineering and computing to create computational models for solving real-world problems. Applicants to the CSE PhD program might want to consider applying to the FLAMEL program .

Curricular Requirements. Students are required to complete at least 31 hours of coursework, as follows.

CSE 6001 (Introduction to CSE, 1 hour),
CSE core courses (12 hours),
Computation specialization (9 hours),
Application specialization (9 hours).

To complete the core course requirement students must complete four of the five courses making up the core curriculum: CSE/ Math 6643 (Numerical Linear Algebra), CSE 6140 (CSE Algorithms), CSE 6730 (Modeling and Simulation: Fundamentals & Implementation), CSE/ISYE 6740 (Computational Data Analysis), CSE 6220 (High Performance Computing).

The computational specialization includes at least 9 hours of courses that increase the student's depth of understanding of computational methods in a specific area, as approved by the student's academic advisor. These courses must go beyond "using computers" to deepen understanding of computational methods, preferably in the context of some application domain.

The application specialization includes at least 9 hours of courses that increase depth of understanding in an application field; these need not be computation-focused courses. At least nine hours of PhD courses must be courses that do not carry the CS/CSE course designation. Hours taken as part of the computation and/or application specialization can be used to fulfill this requirement.

Math Students. Students who choose Mathematics as their home unit are required to take at least 9 hours having the MATH course designation (and not cross listed with other departments), and are expected to have a strong background in Mathematics.

Qualifying Exam. A written qualifying examination must be attempted by the end of the second year of enrollment in the CSE doctoral program (normally taken after the student completes CSE core coursework). The oral qualifying exam part may be combined with the Oral Comprehensive exam, with the approval of the CSE Math home unit coordinator. If there are concerns with the written qualifying exam (determined by the Math home unit coordinator), the student is required to have a separate oral qualifying exam following the format as CSE.

Oral Qualifying Exam. This covers both a computational artifact and the student's specialization area of Applied and Computational Mathematics. The specialization part of the exam follows the Oral Comprehensive Exam format of the Math PhD program. A part of the oral presentation should have a section on computational artifacts. The computational artifact part will follow the same format as for all CSE PhD students.

Doctoral Thesis. Students are required to complete a doctoral thesis reporting the results of independent research that advance the state-of-the-art in the CSE discipline. The dissertation must be successfully defended to the students dissertation research committee.

For further details, please see the CSE Graduate Student Handbook .

$phd in math and computer science$

Ph.D. Program Overview

Description.

The graduate program in the field of mathematics at Cornell leads to the Ph.D. degree, which takes most students five to six years of graduate study to complete. One feature that makes the program at Cornell particularly attractive is the broad range of interests of the faculty . The department has outstanding groups in the areas of algebra, algebraic geometry, analysis, applied mathematics, combinatorics, dynamical systems, geometry, logic, Lie groups, number theory, probability, and topology. The field also maintains close ties with distinguished graduate programs in the fields of applied mathematics , computer science , operations research , and statistics .

Core Courses

A normal course load for a beginning graduate student is three courses per term.

There are no qualifying exams, but the program requires that all students pass four courses to be selected from the six core courses. First-year students are allowed to place out of some (possibly, all) of the core courses. In order to place out of a course, students should contact the faculty member who is teaching the course during the current academic year, and that faculty member will make a decision. The minimum passing grade for the core courses is B-; no grade is assigned for placing out of a core course.

At least two core courses should be taken (or placed out) by the end of the first year. At least four core courses should be taken (or placed out) by the end of the second year (cumulative). These time requirements can be waived for students with health problems or other significant non-academic problems. They can be also waived for students who take time-consuming courses in another area (for example, CS) and who have strong support from a faculty; requests from such students should be made before the beginning of the spring semester.

The core courses are distributed among three main areas: analysis, algebra and topology/geometry. A student must pass at least one course from each group. All entering graduate students are encouraged to eventually take all six core courses with the option of an S/U grade for two of them.

The six core courses are:

MATH 6110, Real Analysis

MATH 6120, Complex Analysis

MATH 6310, Algebra 1

MATH 6320, Algebra 2

MATH 6510, Introductory Algebraic Topology

MATH 6520, Differentiable Manifolds.

Students who are not ready to take some of the core courses may take MATH 4130-4140, Introduction to Analysis, and/or MATH 4330-4340, Introduction to Algebra, which are the honors versions of our core undergraduate courses.

"What is...?" Seminar

The "What Is...?" Seminar is a series of talks given by faculty in the graduate field of Mathematics. Speakers are selected by an organizing committee of graduate students. The goal of the seminar is to aid students in finding advisors.

Schedule for the "What Is...?" seminar

Special Committee

The Cornell Graduate School requires that every student selects a special committee (in particular, a thesis adviser, who is the chair or the committee) by the end of the third semester.

The emphasis in the Graduate School at Cornell is on individualized instruction and training for independent investigation. There are very few formal requirements and each student develops a program in conjunction with his or her special committee, which consists of three faculty members, some of which may be chosen from outside the field of mathematics.

Entering students are not assigned special committees. Such students may contact any of the members on the Advising Committee if they have questions or need advice.

Current Advising Committee

Analysis / Probability / Dynamical Systems / Logic: Lionel Levine Geometry / Topology / Combinatorics: Kathryn Mann Probability / Statistics: Philippe Sosoe Applied Mathematics Liaison: Richard Rand

Admission to Candidacy

To be admitted formally to candidacy for the Ph.D. degree, the student must pass the oral admission to candidacy examination or A exam. This must be completed before the beginning of the student's fourth year. Upon passing the A exam, the student will be awarded (at his/her request) an M.S. degree without thesis.

The admission to candidacy examination is given to determine if the student is “ready to begin work on a thesis.” The content and methods of examination are agreed on by the student and his/her special committee before the examination. The student must be prepared to answer questions on the proposed area of research, and to pass the exam, he/she must demonstrate expertise beyond just mastery of basic mathematics covered in the core graduate courses.

To receive an advanced degree a student must fulfill the residence requirements of the Graduate School. One unit of residence is granted for successful completion of one semester of full-time study, as judged by the chair of the special committee. The Ph.D. program requires a minimum of six residence units. This is not a difficult requirement to satisfy since the program generally takes five to six years to complete. A student who has done graduate work at another institution may petition to transfer residence credit but may not receive more than two such credits.

The candidate must write a thesis that represents creative work and contains original results in that area. The research is carried on independently by the candidate under the supervision of the chairperson of the special committee. By the time of the oral admission to candidacy examination, the candidate should have selected as chairperson of the committee the faculty member who will supervise the research. When the thesis is completed, the student presents his/her results at the thesis defense or B Exam. All doctoral students take a Final Examination (the B Exam, which is the oral defense of the dissertation) upon completion of all requirements for the degree, no earlier than one month before completion of the minimum registration requirement.

Masters Degree in the Minor Field

Ph.D. students in the field of mathematics may earn a Special Master's of Science in Computer Science. Interested students must apply to the Graduate School using a form available for this purpose. To be eligible for this degree, the student must have a member representing the minor field on the special committee and pass the A-exam in the major field. The rules and the specific requirements for each master's program are explained on the referenced page.

Cornell will award at most one master's degree to any student. In particular, a student awarded a master's degree in a minor field will not be eligible for a master's degree in the major field.

Graduate Student Funding

Funding commitments made at the time of admission to the Ph.D. program are typically for a period of five years. Support in the sixth year is available by application, as needed. Support in the seventh year is only available by request from an advisor, and dependent on the availability of teaching lines. Following a policy from the Cornell Graduate School, students who require more than seven years to complete their degree shall not be funded as teaching assistants after the 14th semester.

Special Requests

Students who have special requests should first discuss them with their Ph.D. advisor (or with a field member with whom they work, if they don't have an advisor yet). If the advisor (or field faculty) supports the request, then it should be sent to the Director of Graduate Studies.

Computational and Data Science

Interdisciplinary Ph.D. in Computational & Data Science. Research-intensive, programming, communication skills.

Home » Program » Computational and Data Science, Ph.D.

Computational and Data Science, Ph.D.

The Computational and Data Science Ph.D. is an interdisciplinary program that includes faculty from Agriculture, Biology, Chemistry, Computer Science, Engineering Technology, Geosciences, Mathematical Sciences, and Physics and Astronomy.

The program is research-intensive and applied in nature, seeking to produce graduates with competency in the following three key areas:

Mastery of the mathematical methods of computation as applied to scientific research investigations coupled with a firm understanding of the underlying fundamental science in at least one disciplinary specialization.
Deep knowledge of programming languages, scientific programming, and computing technology so that graduates can adapt and grow as computing systems evolve
Effective written and oral communication skills so that graduates may assume leadership positions in academia, national labs, and industry.

The Computational and Data Science Ph.D. program is for students who are working toward their doctoral degrees. However, with a few extra courses and requirements, most students in the program can complete a Master's degree in mathematics, Computer Science, or Data Science before they graduate.

Requirements

Information.

News Briefs

Alum find success in field after graduation

Dr. Robert Michael began his graduate studies at MTSU in 2008 in the Department of Mathematics. In 2014, he completed his Ph.D. in Computational Science and his master’s degree in Computer Science. His dissertation focused on computational chemistry.

After graduating, he became an HPC specialist at St. Jude Children’s Research Hospital, after which he became Oak Ridge National Laboratory’s Chief Data Architect. He is currently an HPC System Architect at Roche Sequencing Solutions.

Michael, J. R. (2014). Analysis of Thermal Motion Effects on the Electron Density via Computational Simulations (Order No. 3668039). Available from Dissertations & Theses @ Middle Tennessee State University; ProQuest Dissertations & Theses Global. (1647473197).

Alum's research focuses on mathematical models of tumor growth

Dr. Richard Ewool began his undergraduate studies in Ghana. After joining the Ph.D. program, his research and undergraduate studies focused on Mathematical models of tumor growth. After graduating, he became an Assistant Professor of Mathematics at Baptist Health Services University in Memphis.

Ewool, R. C. (2016). Mathematical modeling and simulation of a multiscale tumor induced angiogenesis model (Order No. 10146829). Available from Dissertations & Theses @ Middle Tennessee State University; ProQuest Dissertations & Theses Global. (1829637016).

Related Media

Since computational and data science involves using computers to solve scientific problems, graduates can work as research scientists in almost any field of science or engineering in industry or government, or at a university. MTSU’s program has focus areas in bioinformatics, biological modeling, computational chemistry, computational graph theory, computational physics, engineering and differential equations, high performance computing, and machine learning and remote sensing. In each of these areas, MTSU faculty and students are working on cutting-edge research projects that cut across traditional departmental boundaries.

Employers of MTSU alumni include

Our first graduates from the Computational and Data Science Ph.D. program have found jobs or received offers in companies and academic positions at universities including:

St. Jude's Children's Hospital
John Hopkin's University
Southern Arkansas State University
Texas A&M
Oak Ridge National Laboratory
Duke University

REQUIREMENTS

Dr. John Wallin

Dr. Rafet Al-Tobasei

Dr. Vishwas N Bedekar

Dr. Song Cui

Dr. Wandi Ding

Dr. Misa Faezipour

Dr. Don Hong

Dr. Steve Howard

Dr. Abdul Khaliq

Dr. Jing Kong

Dr. Rachel N. Leander

Dr. Yeqian Liu

Dr. Preston J. MacDougall

Dr. Vajira Manathunga

Dr. Lei Miao

Dr. Mina Mohebbi

Dr. Henrique Momm

Dr. Joshua L. Phillips

Dr. Khem Poudel

Dr. Jaishree Ranganathan

Dr. Ramchandra Rimal

Dr. William Robertson

Dr. Emmanuel Rowe

Dr. Arpan Man Sainju

Dr. Suk Jai Seo

Dr. David Chris Stephens

Dr. Hanna Terletska

Dr. Anatoliy Volkov

Dr. Donald M. Walker

Dr. Donglin Wang

Dr. Lu Xiong

Dr. Xin Yang

Dr. Dong Ye

Dr. Xiaoya Zha

Dr. Hongbo Zhang

Dr. Zhou (Joe) Zhang

INFORMATION

Assistantships.

Research and teaching assistantships, with stipends beginning at $20,100, are available on a competitive basis to full-time students in the COMS program. In addition to the stipend, the university also pays all tuition and most fees for assistantship holders. Non-Tennessee residents who are awarded a graduate assistantship are not required to pay out-of-state fees. To learn more about the types of graduate assistantships and to download an application, visit the Graduate Studies Assistantship page.

The College of Graduate Studies also awards a limited number of scholarships. For additional information and applications, visit the Graduate Studies Finance page.

In addition to assistantships and scholarships, MTSU's Office of Financial Aid assists graduate students seeking other forms of financial support while in school.

Student Forms

COMS Student Handbook
COMS Degree Plan (.docx)
COMS Degree Plan (.pdf)
COMS Qualifying Exam Proposal
COMS Qualifying Exam Results
MTSU Graduate Student Forms
Travel and Reimbursement Forms
25Live for Scheduling Rooms

Research in Computational and Data Science

Computation is now regarded as an equal and indispensable partner, along with theory and experiment, in the advance of scientific knowledge. Numerical simulation enables the study of complex systems and natural phenomena that would be too expensive or dangerous, or even impossible, to study by direct experimentation. The quest for increasing levels of detail and realism in such simulations requires enormous computational capacity, and has provided the impetus for dramatic breakthroughs in computer algorithms and architectures. Due to these advances, computational scientists can now solve large-scale problems that were once thought intractable.

Computational Science is in a rapidly growing multidisciplinary area with connections to the sciences, mathematics, and computer science. The program focuses on the development of problem-solving methodologies and robust tools for the solution of scientific problems.

The Computational and Data Science (COMS) program is a broad multidisciplinary area that encompasses applications in science, applied mathematics, numerical analysis, and computer science. Computer models and computer simulations have become an important part of the research repertoire, supplementing (and in some cases replacing) experimentation. Going from application area to computational results requires domain expertise, mathematical modeling, numerical analysis, algorithm development, software implementation, program execution, analysis, validation, and visualization of results. The COMS program comprises all of the above.

MTSU’s program and research includes elements from computer science, applied mathematics, and science. The COMS program focuses on the integration of knowledge and methodologies from all of these disciplines, but is also distinct from the rest.

It is hard to capture how broad the program is without looking at some of the publications recently submitted. They are from across virtually every discipline. However, the common theme is the use of computers to solve cutting-edge scientific problems.

Publications

Publications of the Computational and Data Science Faculty 2018-Present

Bold indicates a faculty author.

Al-Tobasei, Rafet, Ali Ali, Andre L.S. Garcia, Daniela Lourenco, Tim Leeds, and Mohamed Salem. 2021. “Genomic Predictions for Fillet Yield and Firmness in Rainbow Trout Using Reduced-Density SNP Panels.” BMC Genomics 22 (1). https://doi.org/10.1186/s12864-021-07404-9 .

Ali, Ali, Rafet Al-Tobasei , Brett Kenney, Timothy D. Leeds, and Mohamed Salem. 2018. “Integrated Analysis of LncRNA and MRNA Expression in Rainbow Trout Families Showing Variation in Muscle Growth and Fillet Quality Traits.” Scientific Reports 8 (1). https://doi.org/10.1038/s41598-018-30655-8 .

Ali, Ali, Rafet Al-Tobasei , Daniela Lourenco, Tim Leeds, Brett Kenney, and Mohamed Salem. 2019. “Genome-Wide Association Study Identifies Genomic Loci Affecting Filet Firmness and Protein Content in Rainbow Trout.” Frontiers in Genetics 10 (May). https://doi.org/10.3389/fgene.2019.00386 .

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Zhang, Ning, Zhou Yu, and Qiang Wu . 2018. “Overlapping Sliced Inverse Regression for Dimension Reduction.” Analysis and Applications 17 (5): 715–36.

Zhang, Yi, R. Nelson, K.-M. Tam, W. Ku, U. Yu, N. S. Vidhyadhiraja, H. Terletska , J. Moreno, M. Jarrell, and T. Berlijn. 2018. “Origin of Localization in Ti-Doped Si.” Physical Review B 98 (17). https://doi.org/10.1103/PhysRevB.98.174204 .

Zhang, Yi, Hanna Terletska , Ka-Ming Tam, Yang Wang, Markus Eisenbach, Liviu Chioncel, and Mark Jarrell. 2019. “Locally Self-Consistent Embedding Approach for Disordered Electronic Systems.” Physical Review B 100 (5). https://doi.org/10.1103/PhysRevB.100.054205 .

Zheng, Shao-Liang, Yu-Sheng Chen, Xiaoping Wang, Christina Hoffmann, and Anatoliy Volkov . 2018. “From the Source: Student-Centred Guest Lecturing in a Chemical Crystallography Class.” Journal of Applied Crystallography 51 (3). https://doi.org/10.1107/S1600576718004120 .

Zheng, Xiaoqing, Hongwei Sun, and Qiang Wu . 2021. “Regularized Least Square Kernel Regression for Streaming Data.” Communications in Mathematical Sciences 19 (6). https://doi.org/10.4310/CMS.2021.v19.n6.a4 .

Zou, Jiancheng, Zhengzheng Li, Zhijun Guo, and Don Hong . 2019. “Super-Resolution Reconstruction of Images Based on Microarray Camera.” Computers, Materials & Continua 60 (1). https://doi.org/10.32604/cmc.2019.05795 .

Zou, Jiancheng, Na Zhu, Bailin Ge, and Don Hong . 2021. “Elderly Fall Detection Based on Improved SSD Algorithm.” Journal of New Media 3 (1). https://doi.org/10.32604/jnm.2021.017763 .

Research Groups

The Faculty in the Computational Science Program at MTSU have a diverse set of research interests that cross between traditional departmental boundaries. The groups below outline some of the core research interests of our faculty. In some cases, faculty straddle two or more of the areas below. However, for simplicity, faculty are only associated with a single group on this page.

Bioinformatics
	Asst. Prof.	615-898-2397	Computer Science
Biological Modeling
R. Stephen Howard	Professor	615-898-2044	Biology
	Asst. Prof.	615-494-8936	Mathematics
Rachel Leander	Asst. Prof.	615-494-5422	Mathematics
Computational Chemistry
	Assoc. Professor	615-494-7623	Chemistry
	Assoc. Professor	615-494-8655	Chemistry
Preston MacDougall		615-898-5265	Chemistry
Computational Graph Theory
	Professor	615-904-8168	Computer Science
	Chair of Mathematics and Professor	615-494-8957	Mathematics
	Asst. Prof.	615-494-8957	Mathematics
	Professor	615-898-2494	Mathematics
Computational Physics, Engineering And Differential Equations
	Professor	615-494-8889	Mathematics
William Robertson	Professor	615-898-5837	Physics & Astronomy
Vishwas Bedekar	Asst. Prof.	615-494-8741	Engineering
High Performance Computing
	Asst. Prof	615-904-8238	Computer Science
Machine Learning And Remote Sensing
	Professor	615-904-8168	Computer Science
Qiang Wu	Asst. Prof.	615-898-2026	Mathematics
	Professor	615-904-8339	Mathematics
Song Cui	Asst. Prof.	615-898-5833	Agriculture
	Assoc. Prof.	615-904-8372	Geosciences
	Professor & Director	615-494-7735	Physics & Astronomy

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Computer Science
Graduate Studies

The doctoral program is designed to provide the highest level of academic study and research in computer science, in specializations corresponding to our graduate faculty and including cybersecurity, graphics, AI, evolutionary computation, etc. The degree is one of the options in the Ph.D. in Mathematical and Computational Science program. Our graduates have successfully transitioned to academic, industrial, and government careers.

The Ph.D. degree is conferred in recognition of both breath of competence in computer science, and in technical research abilities as evidenced by production of an acceptable dissertation. The required work consists of advanced studies in preparation for specialized research, and in the completion of original research resulting in a significant contribution to the body of knowledge in the area.

The entire program can be completed full time or part time, will all necessary coursework available in the evening in rotation.Moreover, many courses are available online or in hybrid mode requiring only one visit to class per week.

When planning advancement towards the Ph.D. degree, one should not neglect the fact that independent research for the actual dissertation requires large amounts of time to be devoted by the candidate. A thoughtful planning of outside obligations, especially for the period of dissertation research, should therefore be carefully performed. Special dissertation fellowships can help to compensate for financial responsibilities.

Program Director: Sanjiv Bhatia

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The department admits students for the Fall and Spring semesters on a rolling basis. For international students, the deadline depends on the country of origin due to visa processing, and it is typically May 31 for the Fall semester and October 31 for the Spring semester.

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Dept of Math, Stat, & Comp Sci

College of liberal arts and sciences.

Doctor of Philosophy in Mathematics

The PhD in Mathematics is designed to provide the highest level of training for independent research. Students may apply with or without a Masters degree. For those with a previous Masters degree in mathematics (or related field) the PhD is typically 5 years in duration, whereas for those without a previous Masters degree it is typically 6 years.

To earn the PhD, the student must fulfill the Graduate College requirements specified in the Graduate College catalogue as well as departmental requirements detailed in the MSCS Graduate Handbook , which includes:

Provide proof of an equivalent MS degree or earn a high pass on the Department's written Master's Examination.
Fulfill the doctoral preliminary examinations and minor sequence requirement.
Pass the doctoral oral examination (Probability and Statistics students only).
Produce and defend a thesis that makes a contribution to original research.
Earn 96 semester hours of graduate credit including:
32 credit hours for a previously earned master's degree (requires DGS approval), or earn a high pass on the Department's Master's Exam
40 credit hours of departmental 500-level courses which may include 500-level courses taken from the MS degree earned in residence but may NOT include thesis research (MATH 599, STAT 599, or MCS 599)
32 hours of thesis research (MATH 599, STAT 599, or MCS 599)

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Ph.D. programme in Mathematics and Computer Science

Course information, topics of interest.

Artificial Intelligence, Knowledge Representation and Reasoning, Answer Set Programming, Logic Programming, Constraint Satisfaction Problems.
Databases, Data and Knowledge Integration, Database Theory, Data and Text-Mining, Knowledge Management.
Theoretical Computer Science, Game theory, Decidability and Complexity Theory.
Grid Computing, Computational Science, Cellular Automata.
Bioinformatics and biomedical informatics.
Optimization Models and Methods for solving problems in Transport and Logistics.
Combinatorics
Study of canonical models of surfaces of general type. Algebraic curves and their moduli spaces, Groups and Graphs.
Relationships between algebra and geometry, and geometry and mathematical physics in modern and contemporary art. Historical developments of the theory of decisions. History of mathematics in education. Mathematics Education with particular reference to the role and use of technology in teaching and learning
"Calculus of variations, Variationals methods in Nonlinear Analysis, Critical points Theory and applications to nonlinear partial differential equations, Geometry of Banach spaces. Ordinary differential equations, Differential problems with local and non-local conditions, Implicit and explicit approximation methods, Minimum point problems and projections, Equilibrium problems of mixed or generalized type, Uniform distribution and discrepancy, Almost Monte Carlo integration methods."
Random processes and fields, namely: percolation, statistical mechanics, interacting particle systems, dynamical systems, quantitative finance, stochastic simulation and mathematical statistics.
Problems of Quantum Theory. Group-theoretic approach to the theory of the interaction. Semiclassical models and quantum for the transport of current and heat in semiconductors. Problems coupling devices-electrical networks.
Sequences of Sheffer polynomials and approximation of operators involved. Numerical approximation of solutions of partial differential equations of high order and with boundary conditions that initial. Approximations for Scattered Data Interpolation and rational. Applications of mathematics to engineering problems and statistics.

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PhD opportunities

Research degree opportunities in computing science and mathematics.

A PhD in computing or mathematics can be the first step into an academic career and a passport to some of the most interesting technology jobs in the world. We are welcoming students for study towards a PhD or MPhil degree in data science, artificial intelligence, mathematics, biological modelling and other areas of computer science.

We have a limited number of funded places each year, and these are advertised on FindAPhD.

We also welcome students from the UK and abroad who have their own funding or who wish to develop a proposal to apply for a scholarship. We will help you develop your research question and your proposal with a view to you studying at Stirling. You may have your own ideas for a research question and we would be happy to help you shape them into a high quality PhD proposal. Alternatively, you may find one of our existing research projects the perfect fit for your own interests. We also offer a professional doctorate programme, in which you can work on a project for your employer (who covers the costs) and earn a PhD at the same time.

The list below describes some PhD opportunities that are available right now. If you have a scholarship opportunity or private funding, please contact the supervisor listed for the project that interests you.

Computing Science PhD opportunities

Title: synthetic data for trustworthy ai.

Supervisor: Dr Paulius Stankaitis

Synthetic data has great potential in addressing data scarcity issues in the AI domain. One challenge of generating good synthetic data using physics-simulators for training deep learning models is ensuring that the generated synthetic data sufficiently captures the complexity and variability of real-world scenarios to effectively train the neural networks. This project would investigate the use of realistic synthetic data and optimisation techniques for improving the trustworthiness of AI models.

Title: Deepfake and Fake news detection

Supervisor: Dr Leonardo Bezerra

Due to the growing presence of social media or social networking sites people are digitally connected more than ever. This also empowers citizens to express their views in multitude of topics ranging from Government policies, events in everyday life to just sharing their emotions. However, the growing influence experience by the propaganda of fake news is now cause for concern for all walks of life. Election results are argued on some occasions to have been manipulated through the circulation of unfounded and sometime doctored stories on social media. In addition to fake text, there has been huge growth of AI based image/media manipulation algorithms commonly known as ‘deepfake’. Near realistic fake videos are being generated that contributes significantly to spreading misinformation. This project will research on developing new algorithms that combines deep learning based Natural Language Processing (NLP) and Computer Vision (CV) techniques to detect fake news and prevent misinformation spreading.

Title: Predicting the Performance of Backtracking While Backtracking

Supervisor: Dr Patrick Maier

Backtracking is a generic algorithm for computing optimal solutions of many combinatorial optimisation problems such as travelling salesman or vehicle routing. Unfortunately, the time a backtracking solver requires to find an optimal solution, to prove optimality, or to prove infeasibility is very hard to predict, which limits the practicality of such solvers for real-world problems.

Research in algorithms has mainly focused on specific problem classes and on identifying characteristic features of hard problem instances. Instead, this project aims to mine a generic backtracking solver for performance data at runtime (that is, while solving a particular problem instance) and to build statistical models that can be used to estimate the future performance of the solver on the current problem. Interesting estimates include: How likely is it that the current solution is optimal? Assuming the current solution is optimal, how long will it take to prove optimality? Can the search be parallelised, and if so, how many CPUs would be required to get the answer in one hour?

Topic: The application of cognitive computational methods to enhance vocational rehabilitation

Supervisor: Dr Sæmundur Haraldsson

Vocational Rehabilitation (VR) is a field within healthcare which aims to assist long term sick-listed and unemployed individuals to enter the workforce or education [2]. VR has yet to fully embrace the use of cognitive computer systems, including Artificial Intelligence (AI) approaches. As such it offers indefinite avenues of research for inquisitive minds, e.g., predicting future regional demand for VR, optimising VR pathways for maximum probability of success, and many more. Potential PhD candidates would collaborate with international partners of the ADAPT consortium to exploit state-of-the-art AI and Data Science methods to improve decision making and planning in VR. The projects would form the foundation for the field of VR informatics with international real-world impact on people's health and wellbeing as well as current societal issues.

Topic: Bio inspired Peer-to-Peer Overlay algorithms

Supervisor: Dr Mario Kolberg

Peer-to-Peer (P2P) overlay networks are self-organising, self-managing, and hugely scalable networks without the need for a centralised server component. Utilizing inspiration from biological processes to construct and maintain P2P overlays has attracted some research interest to date. The majority of related solutions focus on providing efficient resource discovery mechanisms using swarm intelligence techniques. In fact such techniques have proven performance benefits in regard to routing and scheduling in dynamic networks, while they have also inherent support for adaptability and robustness in light of node failures. Conversely, except for very few examples, using such techniques for topology management has not really been exploited. This project will investigate the use of bio-inspired solutions for topology management addressing some of the techniques’ challenges such as relatively high computational and messaging complexity.

Topic: Machine Learning approaches to tackle Cyber Attacks

The range of internet services has increased dramatically in recent years, however, at the same time cyber-attacks have grown both in number and sophistication endangering user trust and uptake of such services. Thus there is a need for researchers to develop solutions to these evolving cyber-attacks. However, these attacks are evolving as attackers keep changing their approaches.

Security measures such as firewalls are put in place as the first line of network defense to safeguard these networks but attackers are still able to exploit vulnerabilities in these networks. Intrusion Detection Systems (IDS) have shown potential to be a successful counter measure against potential attacks. However, there are still many open issues, such as their efficiency and effectiveness in the presence of large amount of network traffic. Several IDS have been proposed that can differentiate between attacks and benign network traffic and raise an alarm when a potential threat is detected. However, these systems must be able to analyse large quantity of data in real time to be applicable in modern networks. Unfortunately the larger the data quantity, the more irrelevant information stored. One solution may be to extract key features and apply Machine Learning (ML) techniques to detect attacks. This project will investigate using ML approaches to detect intrusion attacks at runtime.

Topic: Understanding and Visualising the Landscape of Multi-objective Optimisation Problems

Supervisor: Prof. Gabriela Ochoa

In commerce, industry and science, optimisation is a crosscutting, ubiquitous activity. Optimisation problems arise in real-world situations where resources are constrained and multiple criteria are required or desired such as in logistics, manufacturing, transportation, energy, healthcare, food production, biotechnology and others. Most real-world optimisation problems are inherently multi-objective. For example, when evaluating potential solutions, cost or price is one of the main criteria, and some measure of quality is another criterion, often in conflict with the cost. The analysis of multi-objective optimisation surfaces is thus of paramount importance, yet it is not well developed. This project will look at developing and applying network-based models of fitness landscapes and search trajectories to multi-objective optimisation problems. The ultimate goal is to provide a better understanding of algorithms and problems and demonstrate that better knowledge leads to better optimisation across a number of domains

Topic: Artificial Intelligence Sight Loss Assistant

Supervisor: Dr Kevin Swingler

The Artificial Intelligence Sight Loss Assistant (AISLA) project aims to use state of the art computer vision and artificial intelligence to develop personal assistant technology for people with sight loss. Topics within the project include computer vision, natural language processing and human-AI interfaces. A PhD in AI and computer vision can lead to an academic career or jobs in industries such as automotive, building self driving cars, digital assistant design or security. Companies like Google, Amazon and Facebook are at the forefront of commercial AI.

Topic: Interpretable Machine Learning for Time Series Analysis

Supervisor: Dr Yuanlin Gu

In challenging scenarios marked by strong uncertainty or limited data size, the performance and reliability of predictive model can be negatively affected. This project aims to develop interpretable machine learning models along with method for generating and selecting explainable features. This will uncover the relationship between system outputs and the complex changing impacts of inputs, facilitating easier model fine-tuning based on the insights and knowledge gained. The developed methods will be applied in multidisciplinary areas such as engineering, finance, environment, etc

Topic: Efficient search techniques for large-scale global optimisation problems in the real world

Supervisor: Dr Sandy Brownlee

Optimisation problems become very difficult at the large scales: like allocating thousands of skilled engineers to jobs, or prioritising where to spend public money in improving energy efficiency of thousands of homes. This project will look at how to learn the structure of these problems, allowing us to intelligently divide them up so they can be solved efficiently, and how to present the outcomes intuitively to decision makers so they can make informed choices.

Topic: Search-based software improvement

Software is everywhere, and more efficient software has enormous benefits (i.e., more responsive mobile apps; reducing environmental impact of datacentres). In many cases there is even a trade-off between functionality and efficiency, yet improving existing code is difficult because it is easy to break functionality and there is a lot of noise when we measure performance, whether run time, memory consumption, or energy use. This project will explore how search-based approaches like genetic algorithms can be integrated with the latest large language models and best practice from software engineering to improve the efficiency of code, accounting for these difficulties.

Topic: Building Smaller but More Efficient Language Models

Supervisor: Dr Burcu Can Buglalilar

Large Language Models (LLMs) are data hungry and require massive computational and energy resources. This has two implications. First, they are less effective when applied to low-resource languages that lack data resources for building Natural Language Processing (NLP) models, leaving out a large part of the world's population. Second, training such LLMs is currently extremely energy intensive, which has a negative impact on the environment. In this research, we aim to build smaller but more efficient small language models using theories from other fields, including but not limited to linguistics, psycholinguistics and cognitive science.

Topic: Small Data Learning

Supervisor: Dr Keiller Nogueira

The recent impressive results of methods based on deep learning for computer vision applications brought fresh air to the research and industrial community. Although extremely important, deep learning has a relevant drawback: it needs a lot of labelled data in order to learn patterns. However, some domains do not usually have large amounts of labelled data available which, in turn, makes the use of such technique unfeasible. This project will research strategies to better and efficiently exploit deep learning using few annotated samples, including self-supervised learning, meta-learning, and so on.

Topic: Automatic Open-World Segmentation

Semantic segmentation is the task of assigning a semantic category to every pixel in an image. Current methods for dealing with this task can learn in a simple way, but cannot replicate the human ability to learn progressively and continuously. This project will research novel segmentation approaches capable of evolving over time by automatically identifying, pseudo-labelling, and incrementally learning from samples of unknown classes seen during the inference.

Mathematics PhD opportunities

Topic: sports statistics using generalized bradley-terry likelihood methods.

Supervisor: Dr Robin Hankin

Many sports such as football, test cricket, and chess have the possibility of a draw and dealing with this statistically is not straightforward. This project will compare and contrast different methods of addressing draws in the context of sports statistics including reified Bradley-Terry and weighted likelihood functions.

Topic: Born rigidity

In classical mechanics, an object's being "rigid" has a very clear definition. This definition needs to be altered when relativistic considerations become important, the relevant concept being 'Born rigidity'. This project will generalize Born rigidity to cover inelastic string under various kinematic scenarios. One application of these ideas might be to understand the behaviour of light inelastic string in the Kerr metric.

Topic: Stability in eco-evolutionary meta-community models

Supervisor: Dr Gavin Abernethy

In theoretical ecology, meta-community models simulate the interactions between several discrete populations of multiple species in different patches on a spatial environment. We can study how the spatial patterns of species occurrence depend on the physical environment and dispersal behaviour, and how interactions by competitors or predators and prey can enable co-existence or lead to extinctions. Simulated experiments predict the biodiversity impact of habitat destruction, climate change or habitat fragmentation to reveal principles for conservation and landscape management. Eco-evolutionary modelling further incorporates rules for speciation, so that in this project you will explore how evolutionary, ecological, and spatial mechanisms inform each other to shape the emergent ecosystem and influence its stability against perturbation.

Topic: Optimising disease control measures for novel outbreaks

Supervisor: Dr Anthony O'Hare

This project will use census, demographic, and travel network data to model a disease outbreak in a country given some high-level input such as incubation period and R0 value and use Artificial Intelligence to determine the optimal disease control measures, e.g. closing schools, closing rail lines etc. Also, for a given amount of vaccine, you will determine the optimal distribution of the use of the vaccine.

Title: Modelling Pathologies in Cardiac Cells

Supervisor: Dr Anya Kirpichnikova

Cardiac modelling serves as a crucial tool in comprehending the mechanisms of pathophysiology in both healthy and afflicted hearts. The prospective PhD project centres around cardiac modelling, specifically focusing on creating and examining models of both healthy and diseased ventricular cells. As part of this project, the candidate will acquire proficiency in sophisticated techniques for model development and analysis. These techniques will encompass virtual population methodology and sensitivity analysis, aimed at identifying cardinal cellular attributes influencing disease manifestation.

Title: Designing obstacles for the Network Simulator 3

In the realm of network simulation, the process of integrating obstacles plays a pivotal role in achieving realistic and reliable results. This project is a combination of various methods of wave propagation techniques in the presence of obstacles together with the implementation of the results in coding, i.e. implementing obstacles within Network Simulator 3 (NS-3), a popular tool utilized extensively for network research and development. The approach considers the physical characteristics of real-world barriers and their impact on signal propagation, effectively enabling more accurate simulations of varied environmental conditions. By incorporating variables such as material type, size, and location of obstacles, the model should emulate their interference in signal strength, reflection, refraction, diffraction, and absorption.

Title: Novel methods for ECG classification

Electrocardiogram (ECG) classification is critical in diagnosing cardiac abnormalities, offering an essential tool in preventative health measures. Despite advancements in this field, there remain significant opportunities for improving the accuracy and reliability of ECG classification methods. This research proposal aims to explore novel mathematical and signal processing techniques to enhance ECG classification and support timely intervention for cardiac patients.

Topic: How to avoid tipping points in the food system

Supervisor: Prof Rachel Norman

The way we produce, distribute and purchase food is referred to as the food system and has many non-linearities in it. In this project we will look at the role of tipping points, and in particular ways in which we could avoid them. The project will use mathematical models to describe aspects of the food system and we will take a theoretical approach to the analysis alongside considering particular case studies of previous tipping points, for example the collapse of some cod populations.

Topic: When does a new infectious disease outbreak occur and when does it die out?

There have been a significant number of emerging infections which are either diseases which we have not seen before or ones which enter a new region. For example, Covid-19 had not been seen until the end of 2019 and it seems to have come from wildlife. However, we are challenged with these new infections more frequently as the way we interact with our environment changes. This project will use mathematical models to look at what features cause an outbreak to occur or the disease to die out. This project will use stochastic SIR type models which are coupled non-linear differential equations to understand what happens at the start of an outbreak. If a small number of individuals get infected (for example if a pathogen passes from wildlife into humans), what pathogen characteristics are more likely to result in an outbreak? The approach will be a combination of theoretical exploration of parameter space for different models and consideration of specific diseases.

Topic: Sparse multidimensional exponential analysis in computational science and engineering

Supervisor: Dr Wen-shin Lee

Exponential analysis might sound remote, but it touches our lives in many surprising ways, even if most people are unaware of just how important it is. For example, a substantial amount of effort in the field of signal processing is essentially dedicated to the analysis of exponential functions of which the exponents are complex. The analysis of exponential functions whose exponents are very near each other is directly linked to super-resolution imaging. As for exponential functions with real exponents, they are used to portray relaxation, chemical reactions, radioactivity, heat transfer, and fluid dynamics.

Since exponential models are vital to being able to describe physical as well as biological phenomena, their analysis plays a crucial role in advancing science and engineering. The proposal investigates several multidimensional uses of exponential analysis in practice.

Topic: Exponential analysis meets Wavelet theory

In the past years, sparse representations have been realised as linear combinations of several trigonometric functions, Chebyshev polynomials, spherical harmonics, Gaussian distributions and more. Recently also the paradigm of dilation and translation was introduced for use with these basis functions in sparse exponential analysis or sparse interpolation. As a result, high-resolution models can be constructed from sparse and coarsely sampled data, and several series expansions (Fourier, Chebyshev, ...) can be compactified. The above are available in one as well as higher dimensions. The similarity with wavelet theory remains largely unexplored.

Topic: Improving Antibiotic Dosage Regimens: Mitigating for risks associated with Varying Patient Compliance

Supervisor: Dr Andy Hoyle

The rise of antibiotic resistance is putting increasing pressure on our health service, and it is estimated that over 30,000 deaths per year across the EU are associated with resistant bacteria. Yet, there is little movement away from conventional antibiotic regimens, whereby we apply a constant daily dosage, e.g. X mg (or 1 tablet) per day for N days. This project will combine mathematical modelling and Artificial Intelligence, and aims to find optimal antibiotic regimens which maximise host survival, minimise the emergence of resistance and mitigate against uncertain patient compliance.

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Computer science - phd at waterloo, program information.



Admit term(s)	Fall (September - December) Winter (January - April) Spring (May - August)
Application and document submission deadline(s)	December 1 (for admission in September of the following year) June 1 (for admission in January of the following year) October 1 (for admission in May of the following year)
Delivery mode	On-campus
Program type	Doctoral, Research
Length of program	48 months (full-time)
Registration option(s)	Full-time, Part-time
Study option(s)	Thesis
	Algorithms and Complexity Artificial Intelligence Bioinformatics Computer Algebra and Symbolic Computation Computer Graphics Cryptography, Security and Privacy Databases Formal Methods Health Informatics Human-Computer Interaction Information Retrieval Machine Learning Programming Languages Quantum Computing Scientific Computing Software Engineering Systems and Networking

Watch the How to apply to Waterloo graduate studies video

What does it take to get in?

Minimum admission requirements.

A Master's degree in Computer Science with a 78% average.
Student with an undergraduate degree in Computer Science may apply for admission directly to the PhD program. Successful applicants will have an outstanding academic record, breadth of knowledge in computer science, and strong letters of recommendation.
PhD applicants may be admitted into the Master of Mathematics (MMath) program. Like all MMath students, they will have the option to transfer into the PhD program before completing the master's thesis if their performance warrants.

Supervisors

Review the finding a supervisor resources
Applicants do not need to have a confirmed supervisor before applying. If offered admission, a supervisor will be assigned at that time

Application materials

The SIF contains questions specific to your program, typically about why you want to enrol and your experience in that field. Review the application documents web page for more information about this requirement
If a statement or letter is required by your program, review the writing your personal statement resources for helpful tips and tricks on completion

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Three references are required; at least two academic
TOEFL 93 (writing 22, speaking 22), IELTS 6.5 (writing 6.0, speaking 6.5)

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Use the student budget calculator to estimate your cost and resources
Visit the graduate program tuition page on the Finance website to determine the tuition and incidental fees per term for your program
Review the study and living costs
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What can you expect at Waterloo?

Review the degree requirements in the Graduate Studies Academic Calendar, including the courses that you can anticipate taking as part of completing the degree
Check out profiles of current graduate students to learn about their experience at Waterloo
Check out Waterloo's institutional thesis repository - UWspace to see recent submissions from the David R. Cheriton School of Computer Science graduate students
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Review the David R. Cheriton School of Computer Science website to see information about supervisors, research areas, news, and events

This program page is effective September 2023; it will be updated annually. Any changes to the program page following this date will be indicated with a notation.

We strive to provide you with the necessary information on each of our program pages. Was there something you found helpful? Was there anything missing? Share your thoughts .

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Joint PhD between mathematics and computer science

Hey guys what do you think about a joint PhD program between mathematics and computer science like for example this one https://cse.unl.edu/joint-phd-computer-science-and-mathematics

I am interested in AI research but also in the field of pure mathematics. 1.Does such a degree provide the necessary courses in both fields? 2. What are the advantages and disadvantages of such a joint program

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PhD in computer science and work as a mathematician

I want to ask if it is possible to be a mathematician (while also being a computer scientist reseaching in a mathematical related field).

I really love mathematics, specifically analytic number theory. For example I would like to create algorithms to solve problems in number theory and I already talked with my professor who supervises my PhD (which is in computer science field) about this. The professor seems okay with the choice. I would like to ask if it is possible or even okay to be both a mathematician and a computer scientist in this sense.

The mathematics part that I would like to go deep into are mathematical theorems about number theory. That may include finding large primes (from the use of an analytic approach) in computer part, and also maybe the study of the proof or the calculation to verify the elementary theorem involving Riemann's hypothesis.(This one is what interests me the most)

I need to further study about mathematical theory that may not have any application in the real world (even in computing). So I am not sure if it is a good choice to start and if it is possible to be such a person by attending a PhD in computer science as I mentioned.

mathematics
computer-science
changing-fields

3 Answers 3

What exactly do you mean by 'working as a mathematician'? From your post you are currently writing your PhD thesis in a computer science department. You can definitely write a very math heavy thesis, especially if your advisor supports it. You can also submit papers to mathematical journals, provided the topics of your articles are suitable. Journals care for the content of your papers not your academic credentials. You can also collaborate with mathematicians, do joint projects and write joint mathematical papers.

Once you have your PhD thesis you can apply for postdoc or lecturer positions in math departments. If you research fits to their needs a computer science PhD will be just as good as a math PhD. It might be a little less obvious why you would be a good fit for a position but if you can argue that you are they wouldn't exclude you because of a PhD with a different title.

Thank you. Actually I mean what you mentioned (which I forgot to cover). The publication in math journal and working with other mathematicians, etc. that you provided. – Saksit Suwanteerankul Commented Mar 12, 2022 at 14:18

The professor seems okay with the choice.

This is very important, but I think there is an equally important issue you need to pay close attention to. To successfully do a PhD in a topic X, especially at the level that would position you to pursue an academic career, it is really really important to have an advisor who is not just “okay” with your choice but who actually has the knowledge and expertise in topic X so that they are able to guide you towards good research problems and successfully completing work on them.

So, if you want to specialize in analytic number theory (even if it’s mainly computational aspects of number theory), and your advisor is not an expert in this subject, and you don’t have anyone else aside from your advisor who does have that expertise and who is able to mentor you in a way that involves a significant time commitment (for example as a co-adviser), then your plan is not a good plan .

Summary: yes you can be a mathematician with a specialty in analytic number theory, and whether your formal PhD is in computer science or math is in a sense immaterial. But what is a lot more critical is if you have access to a mentor/advisor who has the knowledge to train you in this specialty. If you don’t, then even if you were in a math graduate program I would advise you to rethink your plan.

Actually, during the discussion he seemed interesting about the topic and a sketch proof. Your point is my primary concern indeed, as it is CS department so there might not be so many experts in the field. There was a mathematician who taught at the university before, in the math department in the field I interested but he left to other university and other country. – Saksit Suwanteerankul Commented Mar 13, 2022 at 2:14
Though I am not sure if he had students teahing there or even I can access to them. May be I need to contact the professor together with my advisor if this is possible. – Saksit Suwanteerankul Commented Mar 13, 2022 at 2:15

Actually, anyone can "work as" a mathematician. You don't need any degree at all. You can also publish as degrees aren't required for that. But if you want to get paid for that work then you need to get hired into an appropriate position and while CS overlaps with math, the overlap is actually quite small.

Unless your CS education, generally, was unusual in some way then there are probably vast parts of mathematics that you don't have any experience with. This makes it less likely that you have general insight into math, though it isn't impossible.

If you only want to work in those parts of math that are covered by the overlap, then getting hired for a CS position will serve about as well as any other, and you can, as an intelligent person, expand your understanding of the rest of math - which is a vast landscape. People do change fields and some are competent experts in more than one.

Going the other way is a bit easier, actually, since a deep understanding of math gives an equally deep understanding of some important parts of CS, such as, say algorithmics and computability. Much of the rest can be learned.

So, I'm not sure I understand you correctly but maybe you suggest me to get hired in the CS position and then expand my understanding of math myself? I do agree with your point about anyone can work as a mathematician, even without degree. I think I can learn math more by myself, but the lack of professional network and decent collaborations or disscussions may be the big obstacles. – Saksit Suwanteerankul Commented Mar 12, 2022 at 16:35
So, I suppose that it is okay to work in CS department with the publications on the small overlaps with math together with even unapplicable pure maths (maybe with some collaborations) that you recommended? – Saksit Suwanteerankul Commented Mar 12, 2022 at 16:36
1 I recommend you get the job you can. I think CS is more likely to have a home for you. Note that the market at the moment is especially tight and you are competing with a lot of highly trained mathematicians for any math position. Just don't neglect applying for CS openings. – Buffy Commented Mar 12, 2022 at 16:37
Note that the "small overlaps" aren't unimportant. – Buffy Commented Mar 12, 2022 at 16:38
Thank you. I didn't treat it as unimportant. Actually, they are very meaningful to me and I really want to work on these (and other pure ones as I mentioned). – Saksit Suwanteerankul Commented Mar 12, 2022 at 16:43

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University of Hawai‘i ® at Mānoa 2024-2025 General Catalog

College of natural sciences: information and computer sciences.

College of Natural Sciences
Information and Computer Sciences
Mathematics
School of Life Sciences

College of Natural Sciences POST 317 1680 East-West Road Honolulu, HI 96822 Tel: (808) 956-7420 Fax: (808) 956-3548 Web: ics.hawaii.edu

* Graduate Faculty

*S. P. Robertson, PhD (Chair)—human-computer interaction, sociotechnical systems, civic tech, digital government and digital democracy *K. Baek, PhD—computer vision, machine learning, bioinformatics *M. Belcaid, PhD—data science education, big data approximation, probabilistic programming in genomics *E. Biagioni, PhD—networks, systems, languages *K. Binsted, PhD—artificial intelligence, software design for mobile devices, human-computer interaction, human space exploration *H. Casanova, PhD—high performance computing, distributed systems *M. E. Crosby, PhD—human-computer interaction, cognitive science, augmented cognition *B. Endicott, PhD—cyber-security *P. Johnson, PhD—software engineering, serious games, renewable energy *J. Leigh, PhD—big data visualization, virtual reality, high performance networking, human augmentics, video game design *D. Li, PhD—security, privacy and performance in systems, software, networks and databases *C. A. Moore, PhD—software engineering, application development: software quality *M. B. Ogawa, PhD—educational specialist *D. Pavlovic, PhD—security, software, search and networks, quantum computation *A. Peruma, PhD—software quality, software maintenance and evolution, program comprehension, identifier naming, mobile application quality *G. Poisson, PhD—bioinformatics *P. Sadowski, PhD—machine learning and artificial intelligence, deep learning in the natural sciences *P-M. Seidel, DrEng habil—formal methods, computer arithmetic, computer architecture, algorithms *N. Sitchinava, PhD—algorithms and data structures, parallel and distributed computation, I/O- and cache-efficient computation *D. Suthers, PhD—human-computer interaction, computer-supported collaborative learning, technology for education, socio-technical networks and online communities *P. Washington, PhD—digital health, precision health, data science, machine learning, human-centered computing, biomedical informatics

Cooperative Graduate Faculty

R. Gazan, PhD—social aspects of information technology F. N. Kazman, PhD—software architecture design and analysis, software engineering economics S. Still, PhD—machine learning, information theory F. Zhu, PhD—dynamics and control, robotics, intelligent systems

Affiliate Graduate Faculty

L. Altenberg, PhD—computational intelligence, theoretical evolutionary biology B. Auerhheimer, PhD—software engineering A. Koniges, PhD—high performance computing, machine learning D. R. Stoutemyer, PhD—computer algebra, mathematical software D. Streveler, PhD—medical informatics

Emeritus Faculty

D. Chin, PhD—user modeling, natural language processing, AI for games V. Harada, PhD—school library administration, information literacy S. Itoga, PhD—database system, expert system and logic programming D. Pager, PhD—compilers

Degrees Offered: BBS (including minor) in computer science, Undergraduate Certificate in Creative Computational Media, Undergraduate Certificate in Data Science, MS in computer science, PhD in computer science, and PhD in communication and information sciences (interdisciplinary)

The Academic Program

Information and computer sciences (ICS) is the study of the description and representation of information and the theory, design, analysis, implementation, and application of algorithmic processes that transform information. Students majoring in ICS will learn to use computer systems, a valuable skill which can be applied in all fields of study. Students will also learn the scientific principles and technology required to develop new computer systems and applications. The curriculum covers all major areas of computer science with special emphasis on software engineering, computer networks, artificial intelligence, human-computer interaction, bioinformatics, security science (UH Mānoa is an NSA/DHS designated Center of Academic Excellence in Cyber Defense Research), data science, machine learning, and areas uniquely suited to Hawai‘i’s role as a multicultural and geographical center of the Pacific.

Undergraduate Study

Bachelor’s degree.

To be admitted into the program, first-year students entering UH Mānoa directly from high school must first be admitted into the College of Natural Sciences. For continuing students, a cumulative GPA of at least 2.0 is required for admission.

The minimum required grade for prerequisites is a grade of C (not C-) or better, unless otherwise specified.

For information on a Bachelor Degree Program Sheet, go to programsheets/ .

BA in Information and Computer Sciences

Requirements.

Students pursuing these degrees are required to submit a short proposal listing the courses they intend to take to complete their ICS major. An ICS faculty advisor must approve this proposal in writing. Samples of course proposals are available at the ICS department office.

Students must complete the following related courses for all BA and BS degrees: (MATH 215 or 241 or 251A) and (MATH 216 or 242 or 252A).

There are two BA degree options you can choose from:

Bachelor of Arts in Information and Computer Sciences, Security Science (SecSci) Track

Students must complete the following courses (51 credits):

Core: ICS 111, 141, 211, (212 or 215), 241, 311, 314, 321, 332
Track: ICS 222, 355, (ICS 351 or 451)
Four electives from: ICS 423, 425, 426, 428, 455, 495, ECE 406

Substitution allowed: ECE 367 for ICS 311.

Bachelor of Arts in Information and Computer Sciences, Creative Computational Media (M) Track

Students must complete the following courses (61-62 credits):

Core ICS 110D, 111, 211, 212, 235, 311, 314, 321, 355, 369, 481, 487
MATH 301, (307 or 311), 372
AOC: Four electives from: ICS 464, 482, 484, 485, 486, 488, 489, 496 in CCM

Substitution allowed: (ICS 141 and 241) can be a substitution for (MATH 301 and 372). Substitution allowed: ECE 367 for ICS 311.

BS in Computer Science

Substitutions are permitted with the written approval of an ICS faculty advisor. Waiver of certain requirements, such as by Advanced Placement CS Exam, must be approved by the ICS faculty advisor.

There are three BS degree options you can choose from:

Bachelor of Science in Computer Science

Students must complete the following courses (57 credits)

ICS 111, 141, 211, 212, 241, 311, 314, 321, 332, 355, 496, (MATH 307 or MATH 372) (if students take MATH 307, then they should take MATH 242 as Calculus II prerequisite)
Two of (ICS 312 or 331), (ICS 313 or 361), (ICS 351 or 451)
At least four ICS or other approved courses at the 400 level or above

Substitution allowed: (MATH 301 and 372) can be a substitution for (ICS 141 and 241). In that case, students must take MATH 307. Substitution allowed: ECE 367 for ICS 311.

Bachelor of Science in Information and Computer Science, Creative Computational Media Track

ICS 110D, 111, 211, 212, 235, 311, 314, 321, 355, 369, 481, 487, 488, 496 in CCM
Two electives (400-level or above) in an area relevant to CCM. The courses may include ICS courses or courses from other departments as long as they are approved by an ICS advisor and meet the minimum total of 6 credit hours

Bachelor of Science in Computer Science, Security Science (SecSci) Track

Students must complete the following courses (54 credits):

ICS 111, 141, 211, 212, 241, 311, 314, 321, (312 or 331 or 332), (MATH 307 or 372) (If students take MATH 307, then they should take MATH 242 as calculus II prerequisite)

Bachelor of Science in Computer Science, Data Science Track

Students must complete the following courses (57 credits):

ICS 111, 211, 212, 235, 311, 314, 321, 355, 434, 435, 438, 484
MATH 301, 307, 372
Three electives (400-level or above) in an area relevant to Data Science. The courses may include ICS courses or courses from other departments as long as they are approved by an ICS advisor and meet the minimum total of 9 credit hours.

Substitution allowed: (ICS 141 and 241) can be a substitution for MATH 301 in the Data Science Track only. Substitution allowed: ECE 367 for ICS 311.

A cumulative GPA of at least 2.0 and a grade of C (not C-) or higher in ICS 111 are required for admission.

Students must complete ICS 211, 212, and 241 and their prerequisites, 111 and 141, and three ICS courses at the 300 level and above with a grade of C (not C-) or better.

Undergraduate Certificate in Creative Computational Media

The Undergraduate Creative Computational Media (CCM) Certificate Program provides students and industry professionals with training necessary to enter exciting and lucrative immersive media job markets, such as video game and eSports design and development, digital film production and special effects, new media theatre and dance performance, interactive digital media installation development, and exhibit design for museums, theme parks, or marketing/advertising.

CCM Certificate is offered in collaboration with ACM: The School of Cinematic Arts (CINE) and the Department of Theatre & Dance (Arts and Humanities), the Department of Electrical Engineering (College of Engineering), and the Department of Information and Computer Sciences (ICS) (College of Natural Sciences).

Students must complete 18 credits of required and elective courses with a minimum of 9 credits from upper division courses and a cumulative GPA of 2.5 for the certificate courses taken.

Prerequisites (3 credits)

ICS 110 (Alpha) or ICS 111 or ECE 160

Required Courses (9 credits)

CINE 215, ICS/ECE 369, ICS 486/CINE 419

Elective Courses (9 credits)

CINE 216, 255, 315, 316B, 317, 321, 325, ICS 464, ICS/ CINE/DATA 484, ICS 485/CINE 487, DNCE 362, 673

Additional electives identified by students may be considered through a petitioning process, whose approval can be conducted in collaboration with the affected departments.

Undergraduate Certificate in Data Science

The Undergraduate Data Science (DS) Certificate program provides students and industry professionals with training in modern computational tools for manipulating, visualizing, and extracting insights from data. This programming-intensive program prepares students to work in the high-demand, lucrative field of data science.

The DS Certificate is offered by the Department of Information and Computer Sciences (ICS), in collaboration with the Hawai‘i Data Science Institute and other data-intensive departments at UH Mānoa.

Prerequisites and Eligibility

Applicants must have completed a calculus course that covers limits, derivatives, partial derivatives, and integrals.
Applicants must have completed a programming course that covers basic data types, program control structure, and functions.
Applicants must have a minimum GPA of 3.0.
Applicants who already have a BS in Computer Science with the DS specialization or a BS in Mathematics with the DS specialization are ineligible for the certificate. Students with a certificate in Data Science from UH Hilo are similarly ineligible.

Students must complete 18 credits of required and elective courses.

Required Courses (12 credits)

ICS 235, 434, 435, 484

At the discretion of the DS Program Committee, students who demonstrate proficiency in the topics covered in the required courses may substitute those courses with elective courses.

Elective Courses (6 credits)

ATMO/CEE/SUST 449, BIOL/MBBE 483, PHYS 305, MATH 372 or MATH 472 (Due to overlap, cannot use both), ECON 425, 427, ICS/DATA 422, ICS/DATA 438

Combined Bachelor of Arts in Information & Computer Sciences and Master of Library and Information Science (MLISc)

The combined BA/MLISc is intended to allow students who wish to apply their technical skills to professional information service environments to complete the BA in ICS and the MLISc in Library & Information Science in 5 years, plus one summer course. To be admitted into the program, students must submit the Graduate Admissions Application as well as all required program admission materials specified in the “Graduate Study” section by the start of their junior year (5th semester).

Students pursuing this combined degree should meet the degree requirements for the BA in ICS and MLISc.

Gateway course: ICS 311, with a grade of B or better.

The following courses can be double-counted in BA in ICS and MLISc. The minimum grade requirement for LIS 601 is B (not B-) or better.

LIS 601, 605, 630

Combined Bachelor of Science and Master of Science in Computer Science

The combined BS/MS degree pathway is intended to allow students the opportunity to complete both a Bachelor of Science and Master of Science in Computer Science in 5 years. To be admitted into the program, students must submit the Graduate Admissions Application and fee as well as all required program admission materials by the deadline. Applications should be submitted in the spring of their junior year (6th semester), with admission to the BAM program commencing in the fall of their senior year (7th semester).

Students pursuing this degree should meet the degree requirements for regular Master of Science in Computer Science. Gateway course: ICS 311 with a grade B or higher. The minimum grade requirement is B (not B-) or higher.

There are three pathways students can take depending on their BS degree option. Each pathway differs in the set of courses that can be double-counted for both the bachelor’s and master’s degree.

BS and MS in Computer Science

The following courses can be double-counted in BS in Computer Science and MS in Computer Science.

ICS (414 or 435 or 451 or 466), 621, 635

BS in Computer Science in Data Science and MS in Computer Science

The following courses can be double-counted in BS in Computer Science in Data Science track and MS in Computer Science.

ICS (422 or 475 or 483 or 496), 621, 635

BS in Computer Science in Security Science and MS in Computer Science

The following courses can be double-counted in BS in Computer Science in Security Science track and MS in Computer Science.

ICS (426 or 455 or 495), 621, 623

Graduate Study

The department offers the MS degree in computer science, and the PhD degree in computer science. The department is one of four academic programs that cooperate in an interdisciplinary doctoral program in communication and information sciences (see the “Communication and Information Sciences” section for more information).

Applicants from foreign countries must be academically qualified, proficient in English (TOEFL or IETLS with scores above the minimum required by Graduate Division, with the additional requirement that TOEFL scores be 580/237/92 or above for admission to the MS program, and 600/250/100 or above for admission to the PhD program, where scores are listed as paper/computer/internet), and sufficiently financially supported.

The department offers three forms of financial aid: teaching assistantships, research assistantships, and tuition waivers. The department offers a limited number of assistantships each semester, most of which are teaching assistantships. Teaching and research assistants work approximately 20 hours per week under the supervision of a faculty member and receive a stipend as well as a tuition waiver. Teaching assistants support instruction and research assistants support extramurally funded research projects. Teaching assistantships are awarded to those applicants who can best support the instructional program. Similarly, research assistantships are awarded to those applicants who can best assist faculty with their research projects. Applicants accepted for admission may be eligible for partial financial aid in the form of a tuition waiver from Graduate Division and foreign applicants from Pacific or Asian countries may be eligible for Pacific-Asian Scholarships. Prior to submitting a tuition waiver application form, foreign applicants must submit TOEFL/IETLS scores and documentation of financial support for expenses other than tuition to Graduate Division Student Services. To apply for any of these forms of support, students should submit the ICS Financial Aid Application (form on the ICS website) in addition to other required application materials. Because we can offer assistance to only a small fraction of applicants, we highly encourage students to also seek other forms of support, such as the EastWest Center or other scholarships or forms of employment.

Master’s Degree

The master’s program is intended for students planning to specialize in computer science or to apply computer science to another field. Applicants who do not possess an undergraduate degree in computer science from an accredited institution will need to complete equivalent course work.

Plan A (thesis) and Plan B (non-thesis) are available. A minimum of 31 credit hours is required under both plans. A minimum B average must be maintained in all courses.

Plan A (Thesis) Requirements

At least six ICS graduate courses, i.e. courses with numbers between ICS 600 and 691, with the exception of ICS 690;
Two additional elective 600-level courses must be taken either from the ICS department or some related discipline on a topic related to computer science. Elective courses must have prior approval from the ICS graduate chair as to the suitability prior to enrollment in the courses;
Up to two of the graduate courses may be replaced by regular ICS 400-level courses (not ICS 499), taken after enrolling in the ICS graduate program;
Thesis research taken as 6 credits of ICS 700 is required for the degree. These credits are typically taken close to or during the final semester in the program;
ICS 690 (taken for CR/NC) in the first year of the program.

Plan B (Non-thesis) Requirements

At least six ICS graduate courses, i.e. courses with numbers between ICS 600 and ICS 691, with the exception of ICS 690.
A final project ending with a required written report, taken as ICS 699 (a maximum of six credits is counted toward the degree) under the supervision of a faculty member;

The administrative procedures for the program include the following:

The student must meet with the graduate program chair during the first semester;
Upon completion of at least 12 credit hours of courses applicable to the degree, students are encouraged to propose a degree plan by selecting Plan A (Thesis) or Plan B (NonThesis) options;
Plan A students are encouraged to choose a thesis topic and committee upon completion of 18 credit hours of applicable courses; and
All requests for changes in degree plan must be submitted in writing by the student and approved by the graduate program chair before the diploma application is filed.

PhD in Computer Science

The department offers a PhD in computer science that prepares students for creative research, teaching, and service. There are two programs leading to the PhD degree, one designed for the applicant entering with bachelor’s degrees, and the other for those who already have master’s degrees. Students may begin their program either in the fall or spring semesters.

Applicants with bachelor’s degrees must first satisfy the admission and degree requirements of the master’s degree in computer science. Advantages to this route are (1) students are admitted at an early stage to the PhD program; (2) the MS portion of the program will prepare students for their qualifying examination; and (3) students who have completed the MS requirements will have the option of obtaining a master’s degree even if they do not continue with the PhD program.

Applicants with master’s degrees in areas other than computer science may be admitted to the program, but will be required to fulfill their program deficiencies with additional course work.

Requirements for students to complete the PhD program are:

Passing a qualifying examination demonstrating core competency in computer science no later than the end of the first year of their PhD studies;
Preparing a portfolio showing research readiness by the end of the second year of their PhD studies;
Passing the proposal defense;
Passing the dissertation defense.

Interdisciplinary Doctoral Degree Program

The ICS department participates in an interdisciplinary program in Communication and Information Sciences (CIS) that integrates computer science, library science, communication and management information systems. Due to the broad knowledge base required to support the program, it draws on a variety of majors such as behavioral science, economics, engineering, and political science. The computer science program is one of four academic programs (COM, ICS, ITM, and LIS) that support this degree. See the “Interdisciplinary Program” section for more information on this program.

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Information and Computer Sciences

University of Hawai‘i at Mānoa

Ph.D. in Computer Science

The Ph.D. Program in Computer Science is designed for students who want to contribute to the study of the description and representation of information, and the theory, design, analysis, implementation, and application of algorithmic processes that transform information.

Students receive advanced training in the scientific principles and technology required to develop and evaluate new computer systems and applications. Our curriculum covers all major areas of computer science, with active research in algorithms, artificial intelligence, bioinformatics, data science, high-performance computing, human-computer interaction, software engineering, security science, machine learning, and computer systems.

More Information

Prospective Ph.D. Students
Current Ph.D. Students

Student Outcomes

Master core computer science theoretical concepts, practices and technologies.
Identify, formulate and solve problems employing knowledge within the discipline.
Contribute effectively to collaborative team oriented activities.
Communicate effectively about computer science topics using appropriate media.
Demonstrate advanced knowledge in an area of specialization within the discipline.
Engage in significant research in their area of specialization within the discipline and/or in projects that respond to community and industry needs.
Develop a research portfolio that demonstrates the capacity to carry out original research in the field.
Become an expert in the area of specialization including mastery of the relevant research skills and methods, develop a research vision, and formulate a research plan that will lead to novel scientific contributions.
Execute a research plan and demonstrate original contributions to the field, as shown through findings and/or publications, culminating in a Ph.D. dissertation and oral defense.

Message from the Chair
New Opportunities at the Intersection of Graphics, Vision, & HCI
Reflections on Disinformation, Democracy, and Free Expression
Tech / Law Colloquium
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Is All Privacy Informational Privacy?
Stop Predicting? Machine Learning and Measurement
Towards a Theory of Proxies (for Race)
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1st-year Ph.D. Student Reimbursement for a Computer Purchase

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All new to Cornell first year Information Science Ph.D. students are allowed a reimbursement for up to $1,500 USD toward the purchase of a laptop computer. This is a one-time reimbursement and cannot be used towards any other expenses. Students are eligible to request a reimbursement only after they have matriculated, registered and enrolled in classes, which is typically at the end of August. Students have up to one year from the response deadline of April 15 to purchase a laptop computer and request a reimbursement. After this date the reimbursement offer is voided.

If the computer equipment total is less than $1,500 you will not be given the balance, and for equipment that is more than $1,500 you will be responsible for the amount over the $1,500 cap. All equipment must be purchased at one time, and the receipt(s) submitted all together. Receipts must be in English and if the item(s) are purchased using foreign currency, please convert the amount to US currency.

For reference, our students in the past have received a 13-inch MacBook Pro with Touch Bar (1.4GHz quadcore Intel Core i5 processor; 256GB SSD storage). This is just a suggestion on the type of laptop you may want to consider purchasing. Students should consult with their advisors if they have doubts on what specifications will be needed to support their research. We expect students to use this money to purchase equipment such as the items listed below:

Laptop computer
Desktop computer
Monitor for a computer
External Hard Drive
Noise Canceling Headphones

Items that we will not reimburse for are listed below, but this is not limited to this list. Again, please contact us if you are unsure before purchasing anything.

Parts to build your own computer
Replacement of a stolen or broken piece of technology
Service contracts (e.g., AppleCare)

A receipt with the total cost of the approved equipment and the laptop policy form need to be submitted to Seamus Buxton, [email protected], and the receipt(s) must be in English.

Note: Students who are currently enrolled in a Ph.D. program at Cornell and are admitted through the Change of Program petition process are not eligible for this reimbursement. Students should work with their advisor for any equipment purchases that are needed.

If you are interested in applying, and have questions not answered above, please contact

us at: [email protected] .

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Master of Science in Data Science

Join our innovative program, designed with input from industry leaders and available fully online.

Enter the Growing, In-Demand Field of Data Science

The University of Wisconsin-Eau Claire's master of science in data science is a fully online degree program intended for students with a bachelor’s degree in math, statistics, analytics, computer science, or marketing; or three to five years of professional experience as a business intelligence analyst, data analyst, financial analyst, information technology analyst, database administrator, computer programmer, statistician, or other related position.

The rigorous program is the first online master's degree in data science offered in the UW System and is helping fill a critical need for data scientists. Using analytics, statistics, programming, business and storytelling, data scientists have the unique and important job of transforming big data into actionable insights. The field is already growing at an incredible pace, and as today's world continues to generate more and more data, employers across the country are in consistent need of professionals who know how to understand and interpret data.

Designed with input from industry leaders, the data science program offers a comprehensive, multidisciplinary curriculum grounded in computer science, math and statistics, management and communication. Coursework throughout the degree will show you how to clean, organize, analyze and interpret data using current industry tools and analytical methods. Since data scientists must also understand privacy and security policies, part of the curriculum throughout the program focuses on how to appropriately handle data found in financial records, medical records, consumer patterns, internet searches and other real-world situations — knowledge that is needed in countless industries and organizations.

Graduates of the data science graduate program leave with the knowledge, skills and tools necessary to mine data sets, find patterns and communicate ways to make use of the findings. The intensive program prepares you for expertise in a number of specialized areas — including data mining and warehousing, predictive analytics, statistical modeling, database infrastructures and data management, machine learning, and analytics-based decision making — making you a versatile and highly sought-after employee.

Program Details

Accreditation information.

Wisconsin is a SARA state (State Authorization Reciprocity Agreement) and the University of Wisconsin-Eau Claire is a SARA-approved institution.

Throughout the data science program, you'll learn from diverse, distinguished faculty members from across six University of Wisconsin campuses and the University of Wisconsin-Extension. Their expertise, combined with UW Extended Campus' award-winning instructional and media design, ensures a rich and engaging educational experience that will prepare you well for your future career.

While working toward your degree, you'll have direct access to the field's latest technology, including powerful tools like SQL Server, R, Python, and Tableau. This knowledge and experience will give you a competitive advantage when applying for jobs or transitioning into more executive roles within your current organization.

students outdoors for math class on a nice day

Students in the data science program can take advantage of affordable tuition that compares favorably to competing graduate programs from other institutions. Like other collaborative online University of Wisconsin programs, students pay the same tuition whether they live in Wisconsin or elsewhere.

The data science degree was intentionally designed with significant input from businesses and industry leaders, ensuring curriculum aligns with employer needs. An industry advisory board consisting of leading organizations — including American Family, CUNA Mutual Group, Nicolet Bank, and TDS Telecom — provides further insights into what organizations are looking for and what the field needs right now.

Blugold Stories

Analyze data. Develop computer programs. Perfect your coding skills. With a master’s degree in data science, you’ll do all this and more. Our expert faculty will guide you as you learn more than you ever thought possible, alongside peers that offer their own real-world insight.

Just the facts

100% Online This program can be completed entirely online.

100% Employed or Continuing Education Every 2022-2023 graduate from this major is currently employed or continuing their education.

Where can the master of science in data science program lead me after graduation?

Data science graduates enter a quickly growing field where demand is high for professionals who know how to transform complex data sets into actionable information and competitive advantages. And because data scientists are needed in virtually every sector, our Blugolds have no problem finding jobs upon graduation. Explore opportunities in manufacturing, construction, transportation, warehousing, communication, science, healthcare, computer science, information technology, retail, sales, marketing, finance, insurance, education, government, law enforcement, security, and so much more.

Example Careers

Data scientist
Data or research analyst/manager
Data warehouse architect
Enterprise strategy consultant
Business intelligence manager/analyst
Hadoop engineer
Market intelligence analyst/manager

The master of science in data science is a 12-course, 36-credit online master's degree that prepares students for complex and fast-paced careers in data science and analytics.

Featuring a multidisciplinary curriculum that draws primarily from computer science, mathematics and statistics, management and communication, the program teaches students how to derive insights from real-world data sets — both structured and unstructured. Using the latest data science tools, analytical methods and sophisticated visualization techniques, graduates learn to communicate their data discoveries and recommendations clearly. A focus on building leadership and communication skills rounds out the degree.

Here are a few courses in Master of Science in Data Science at UW-Eau Claire.

Foundations of Data Science

Introduction to data science and its importance in business decision making.

Visualization and Unstructured Data Analysis

Covers various aspects of data analytics including visualization and analysis of unstructured data such as social networks.

Ethics of Data Science

Ethical issues related to data science, including privacy, intellectual property, security, and the moral integrity of inferences based on data.

Meet the Faculty

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Thinking about studying master of science in data science? You might also be interested in exploring these related programs.

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715-836-4636

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Is a PhD Worth it?
master’s degree in mathematics and computer science
How to select the best topic for your PhD in Computer Science?
PhD-Topics-in-Computer-Science-list.pdf
PhD in Computer Science: Specializations & Best Degrees
PhD in Mathematics

COMMENTS

Joint Math/CS PhD
In Winter 2018, the Department of Mathematics and the Department of Computer Science launched a joint program through which participating students can earn the degree "Ph. D. in Mathematics and Computer Science.". The basic structure is that students must gain admission to both PhD programs and satisfy both sets of course requirements.
Graduate Degree in Computing + Mathematical Sciences
The Computing and Mathematical Sciences (CMS) PhD program is a unique, new, multidisciplinary program at Caltech involving faculty and students from computer science, electrical engineering, applied math, economics, operations research, and even the physical sciences.
MIT Doctoral Programs in Computational Science and Engineering
The standalone CSE PhD program is intended for students who intend to pursue research in cross-cutting methodological aspects of computational science. The resulting doctoral degree in Computational Science and Engineering is awarded by CCSE via the the Schwarzman College of Computing. In contrast, the interdisciplinary CSE PhD program is ...
Joint PhD program in Mathematics and Computer Science
In Winter 2018, the Department of Mathematics and the Department of Computer Science launched a joint program through which participating students can earn the degree "Ph. D. in Mathematics and Computer Science." The basic structure is that students must gain admission to both PhD programs and satisfy both sets of course requirements.
Ph.D. Program
The degree of Doctor of Philosophy in Applied Mathematics and Computational Science is conferred in recognition of marked ability and high attainment in advanced applied and computational mathematics, including the successful completion of a significant original research project. The program typically takes four to five years to complete ...
PhD in Computer Science
Computer Science PhD Degree. In the Computer Science program, you will learn both the fundamentals of computation and computation's interaction with the world. Your work will involve a wide range of areas including theoretical computer science, artificial intelligence and machine learning, economics and computer science, privacy and security ...
Joint Math/CS PhD Program
In Winter 2018, the Department of Mathematics and the Department of Computer Science launched a joint program through which participating students can earn the degree "Ph. D. in Mathematics and Computer Science.". The basic structure is that students must gain admission to both PhD programs and satisfy both sets of course requirements.
PhD in Computational Sciences and Engineering
The PhD in CSE is a highly interdisciplinary program designed to provide students with practical skills and theoretical understandings needed to become leaders in the field of computational science and engineering. The program emphasizes the integration and application of principles from mathematics, science, engineering and computing to create computational models for solving real-world problems.
PhD Programs
Our PhD students build the skills to conduct research at the cutting edge of computer science, working with faculty to publish at leading conferences, develop new tools and approaches, and make bold new discoveries across research areas. ... In Winter 2018, the Department of Mathematics and the A program of the Departments of Mathematics and ...
Ph.D. Program Overview
Ph.D. students in the field of mathematics may earn a Special Master's of Science in Computer Science. Interested students must apply to the Graduate School using a form available for this purpose. To be eligible for this degree, the student must have a member representing the minor field on the special committee and pass the A-exam in the ...
Computational and Data Science, Ph.D.
The Computational and Data Science Ph.D. is an interdisciplinary program that includes faculty from Agriculture, Biology, Chemistry, Computer Science, Engineering Technology, Geosciences, Mathematical Sciences, and Physics and Astronomy. The program is research-intensive and applied in nature, seeking to produce graduates with competency in the ...
PhD in pure mathematics for a student in computer science
I'm a student currently pursuing my masters in computer science. My present area of research is computational algrebraic geometry (theory of grobner bases and tropical geometry). Afterwards, I'm interested in pursuing my PhD in pure mathematics. I would like know if I'll be elligible for applying to pure math PhD programs?
Doctorate in Mathematics and Computer Science
The PhD Program in Mathematics and Computer Science of the University of Barcelona Graduate School is designed to lead to a high level education and the completion of a doctoral dissertation in one of the main research areas of the program.Each student has a supervisor who guarantees the training activities and the quality of the research.. The program, which has been adapted to the European ...
Doctoral Programs in Computational Science and Engineering
Mathematics and Computational Science As with the standalone CSE PhD program, the emphasis of thesis research activities is the development of new computational methods and/or the innovative application of state-of-the-art computational techniques to important problems in engineering and science.
Ph.D. in Mathematical and Computational Sciences-Computer Science
The doctoral program is designed to provide the highest level of academic study and research in computer science, in specializations corresponding to our graduate faculty and including cybersecurity, graphics, AI, evolutionary computation, etc. The degree is one of the options in the Ph.D. in Mathematical and Computational Science program.
PhD
The PhD in Mathematics is designed to provide the highest level of training for independent research. Students may apply with or without a Masters degree. ... Department of Mathematics, Statistics, and Computer Science. 851 S. Morgan Street ,322 Science and Engineering Offices (MC 249) Chicago, IL 60607-7045. Phone: (312) 996-3041.
HomePage
The PhD in Mathematics and Computer Science is designed to train both researchers and highly skilled professionals in disciplines concerning Computer Science and Mathematics. The Doctors of Philosophy in these disciplines can work as researchers in universities or in public and private research institutions, or as experts with relevant skills ...
PhD opportunities in Computing Science and Mathematics
A PhD in computing or mathematics can be the first step into an academic career and a passport to some of the most interesting technology jobs in the world. We are welcoming students for study towards a PhD or MPhil degree in data science, artificial intelligence, mathematics, biological modelling and other areas of computer science.
Computer Science
Student with an undergraduate degree in Computer Science may apply for admission directly to the PhD program. Successful applicants will have an outstanding academic record, breadth of knowledge in computer science, and strong letters of recommendation. PhD applicants may be admitted into the Master of Mathematics (MMath) program.
Computer Science Ph.D.
Coursework requirements for the Ph.D. vary depending on whether the student enters with a B.S. or M.S. degree. Courses are intended to demonstrate breadth in computer science as well as experience in research. CPSC 6810/8810 MSCS Ready modules cannot be counted toward any Computer Science Ph.D. credit requirement. Ph.D. students with an M.S. Degree
Joint PhD between mathematics and computer science : r/mathematics
Say you get your PhD in math; you will be very well equipped and able to quickly learn comp sci relevant to your interests/career aspirations. The same will be true of mathematics if you pursue a degree in comp sci. There is also something to be said about working with people outside of your field; a pure mathematician can provide new insight ...
mathematics
From your post you are currently writing your PhD thesis in a computer science department. You can definitely write a very math heavy thesis, especially if your advisor supports it. You can also submit papers to mathematical journals, provided the topics of your articles are suitable. Journals care for the content of your papers not your ...
Ph.D in Mathematics
Exploring New Theories at the Forefront of Mathematics and its Applications. Doctoral studies form our core graduate program. The faculty in the department excel in numerous areas of applied mathematics and are well versed in many related disciplinary fields, thus they are highly qualified to train graduate students and mentor them in producing high-quality research and dissertations at the ...
Information and Computer Sciences
Students must complete the following related courses for all BA and BS degrees: (MATH 215 or 241 or 251A) and (MATH 216 or 242 or 252A). ... The department offers a PhD in computer science that prepares students for creative research, teaching, and service. There are two programs leading to the PhD degree, one designed for the applicant ...
Ph.D. in Computer Science
Our curriculum covers all major areas of computer science, with active research in algorithms, artificial intelligence, bioinformatics, data science, high-performance computing, human-computer interaction, software engineering, security science, machine learning, and computer systems. More Information. Prospective Ph.D. Students; Current Ph.D ...
1st-year Ph.D. Student Reimbursement for a Computer Purchase
All new to Cornell first year Information Science Ph.D. students are allowed a reimbursement for up to $1,500 USD toward the purchase of a laptop computer. This is a one-time reimbursement and cannot be used towards any other expenses. Students are eligible to request a reimbursement only after they have matriculated, registered and enrolled in classes, which is typically at the end of August ...
Master of Science in Data Science
Enter the Growing, In-Demand Field of Data Science. The University of Wisconsin-Eau Claire's master of science in data science is a fully online degree program intended for students with a bachelor's degree in math, statistics, analytics, computer science, or marketing; or three to five years of professional experience as a business intelligence analyst, data analyst, financial analyst ...
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