scientific computing phd programs

• Skip to Content
• Bulletin Home

• Degree Charts >
• Computational Science and Engineering (PhD)
• Around Campus
• Academic Program
• Administration
• Arts at MIT
• Campus Media
• Fraternities, Sororities, and Independent Living Groups
• Health Services
• Priscilla King Gray Public Service Center
• Religious Organizations
• Student Government
• Work-​Life and Family Resources
• Advising and Support
• Digital Learning
• Disability and Access Services
• Information Systems and Technology
• Student Financial Services
• Writing and Communication Center
• Major Course of Study
• General Institute Requirements
• Independent Activites Period
• Undergraduate Research Opportunities Program
• First-​Year Advising Seminars
• Interphase EDGE/​x
• Edgerton Center
• Grading Options
• Study at Other Universities
• Internships Abroad
• Career Advising and Professional Development
• Teacher Licensure and Education
• ROTC Programs
• Financial Aid
• Medical Requirements
• Graduate Study at MIT
• General Degree Requirements
• Other Institutions
• Registration
• Term Regulations and Examination Policies
• Academic Performance and Grades
• Policies and Procedures
• Privacy of Student Records
• Abdul Latif Jameel Clinic for Machine Learning in Health
• Abdul Latif Jameel Poverty Action Lab
• Art, Culture, and Technology Program
• Broad Institute of MIT and Harvard
• Center for Bits and Atoms
• Center for Clinical and Translational Research
• Center for Collective Intelligence
• Center for Computational Science and Engineering
• Center for Constructive Communication
• Center for Energy and Environmental Policy Research
• Center for Environmental Health Sciences
• Center for Global Change Science
• Center for International Studies
• Center for Real Estate
• Center for Transportation &​ Logistics
• Computer Science and Artificial Intelligence Laboratory
• Concrete Sustainability Hub
• D-​Lab
• Deshpande Center for Technological Innovation
• Division of Comparative Medicine
• Haystack Observatory
• Initiative on the Digital Economy
• Institute for Medical Engineering and Science
• Institute for Soldier Nanotechnologies
• Institute for Work and Employment Research
• Internet Policy Research Initiative
• Joint Program on the Science and Policy of Global Change
• Knight Science Journalism Program
• Koch Institute for Integrative Cancer Research
• Laboratory for Financial Engineering
• Laboratory for Information and Decision Systems
• Laboratory for Manufacturing and Productivity
• Laboratory for Nuclear Science
• Legatum Center for Development and Entrepreneurship
• Lincoln Laboratory
• Martin Trust Center for MIT Entrepreneurship
• Materials Research Laboratory
• McGovern Institute for Brain Research
• Microsystems Technology Laboratories
• MIT Center for Art, Science &​ Technology
• MIT Energy Initiative
• MIT Environmental Solutions Initiative
• MIT Kavli Institute for Astrophysics and Space Research
• MIT Media Lab
• MIT Office of Innovation
• MIT Open Learning
• MIT Portugal Program
• MIT Professional Education
• MIT Sea Grant College Program
• Nuclear Reactor Laboratory
• Operations Research Center
• Picower Institute for Learning and Memory
• Plasma Science and Fusion Center
• Research Laboratory of Electronics
• Simons Center for the Social Brain
• Singapore-​MIT Alliance for Research and Technology Centre
• Sociotechnical Systems Research Center
• Whitehead Institute for Biomedical Research
• Women's and Gender Studies Program
• Architecture (SB, Course 4)
• Architecture (MArch)
• Art and Design (SB, Course 4-​B)
• Art, Culture, and Technology (SM)
• Architecture Studies (SMArchS)
• Media Arts and Sciences
• Planning (SB, Course 11)
• Urban Science and Planning with Computer Science (SB, Course 11-​6)
• Aeronautics and Astronautics Fields (PhD)
• Aerospace Engineering (SB, Course 16)
• Engineering (SB, Course 16-​ENG)
• Biological Engineering (SB, Course 20)
• Biological Engineering (PhD)
• Chemical Engineering (Course 10)
• Chemical-​Biological Engineering (Course 10-​B)
• Chemical Engineering (Course 10-​C)
• Engineering (Course 10-​ENG)
• Engineering (Course 1-​ENG)
• Computation and Cognition (Course 6-​9)
• Computer Science and Engineering (Course 6-​3)
• Computer Science and Molecular Biology (Course 6-​7)
• Electrical Engineering with Computing (Course 6-​5)
• Electrical Engineering and Computer Science (MEng)
• Computer Science and Molecular Biology (MEng)
• Health Sciences and Technology
• Archaeology and Materials (Course 3-​C)
• Materials Science and Engineering (Course 3)
• Materials Science and Engineering (Course 3-​A)
• Materials Science and Engineering (PhD)
• Mechanical Engineering (Course 2)
• Mechanical and Ocean Engineering (Course 2-​OE)
• Engineering (Course 2-​A)
• Nuclear Science and Engineering (Course 22)
• Engineering (Course 22-​ENG)
• Anthropology (Course 21A)
• Comparative Media Studies (CMS)
• Writing (Course 21W)
• Data, Economics, and Design of Policy (MASc)
• Economics (Course 14-​1)
• Economics (PhD)
• Mathematical Economics (Course 14-​2)
• Global Studies and Languages (Course 21G)
• History (Course 21H)
• Linguistics and Philosophy (Course 24-​2)
• Philosophy (Course 24-​1)
• Linguistics (SM)
• Literature (Course 21L)
• Music (Course 21M-​1)
• Theater Arts (Course 21M-​2)
• Political Science (Course 17)
• Science, Technology, and Society/​Second Major (STS)
• Business Analytics (Course 15-​2)
• Finance (Course 15-​3)
• Management (Course 15-​1)
• Biology (Course 7)
• Chemistry and Biology (Course 5-​7)
• Brain and Cognitive Sciences (Course 9)
• Chemistry (Course 5)
• Earth, Atmospheric and Planetary Sciences (Course 12)
• Mathematics (Course 18)
• Mathematics (PhD)
• Mathematics with Computer Science (Course 18-​C)
• Physics (Course 8)
• Department of Electrical Engineering and Computer Science
• Institute for Data, Systems, and Society
• Chemistry and Biology
• Climate System Science and Engineering
• Computation and Cognition
• Computer Science and Molecular Biology
• Computer Science, Economics, and Data Science
• Humanities and Engineering
• Humanities and Science
• Urban Science and Planning with Computer Science
• African and African Diaspora Studies
• American Studies
• Ancient and Medieval Studies
• Applied International Studies
• Asian and Asian Diaspora Studies
• Biomedical Engineering
• Energy Studies
• Entrepreneurship and Innovation
• Environment and Sustainability
• Latin American and Latino/​a Studies
• Middle Eastern Studies
• Polymers and Soft Matter
• Public Policy
• Russian and Eurasian Studies
• Statistics and Data Science
• Women's and Gender Studies
• Advanced Urbanism
• Computational and Systems Biology

Computational Science and Engineering

• Design and Management (IDM &​ SDM)
• Joint Program with Woods Hole Oceanographic Institution
• Leaders for Global Operations
• Microbiology
• Music Technology and Computation
• Operations Research
• Real Estate Development
• Social and Engineering Systems
• Supply Chain Management
• Technology and Policy
• Transportation
• School of Architecture and Planning
• School of Engineering
• Artificial Intelligence and Decision Making (Course 6-​4)
• Nuclear Science and Engineering (PhD)
• School of Humanities, Arts, and Social Sciences
• Humanities (Course 21)
• Humanities and Engineering (Course 21E)
• Humanities and Science (Course 21S)
• Sloan School of Management
• School of Science
• Brain and Cognitive Sciences (PhD)
• Earth, Atmospheric and Planetary Sciences Fields (PhD)
• Interdisciplinary Programs (SB)
• Climate System Science and Engineering (Course 1-​12)
• Computer Science, Economics, and Data Science (Course 6-​14)
• Interdisciplinary Programs (Graduate)
• Biological Oceanography (PhD)
• Computation and Cognition (MEng)
• Computational Science and Engineering (SM)
• Computer Science, Economics, and Data Science (MEng)
• Engineering and Management (SM)
• Leaders for Global Operations (MBA/​SM and SM)
• Music Technology and Computation (SM and MASc)
• Real Estate Development (SM)
• Statistics (PhD)
• Supply Chain Management (MEng and MASc)
• Technology and Policy (SM)
• Transportation (SM)
• Aeronautics and Astronautics (Course 16)
• Aerospace Studies (AS)
• Architecture (Course 4)
• Biological Engineering (Course 20)
• Civil and Environmental Engineering (Course 1)
• Comparative Media Studies /​ Writing (CMS)
• Comparative Media Studies /​ Writing (Course 21W)
• Computational and Systems Biology (CSB)
• Computational Science and Engineering (CSE)
• Concourse (CC)
• Data, Systems, and Society (IDS)
• Earth, Atmospheric, and Planetary Sciences (Course 12)
• Economics (Course 14)
• Edgerton Center (EC)
• Electrical Engineering and Computer Science (Course 6)
• Engineering Management (EM)
• Experimental Study Group (ES)
• Global Languages (Course 21G)
• Health Sciences and Technology (HST)
• Linguistics and Philosophy (Course 24)
• Management (Course 15)
• Media Arts and Sciences (MAS)
• Military Science (MS)
• Music (Course 21M)
• Naval Science (NS)
• Science, Technology, and Society (STS)
• Special Programs
• Supply Chain Management (SCM)
• Theater Arts (21T)
• Urban Studies and Planning (Course 11)
• Women's and Gender Studies (WGS)

Doctoral Programs in Computational Science and Engineering

Doctor of philosophy in computational science and engineering, program requirements.

Programs Offered by CCSE in Conjunction with Select Departments in the Schools of Engineering and Science

The interdisciplinary doctoral program in Computational Science and Engineering ( PhD in CSE + Engineering or Science ) offers students the opportunity to specialize at the doctoral level in a computation-related field of their choice via computationally-oriented coursework and a doctoral thesis with a disciplinary focus related to one of eight participating host departments, namely, Aeronautics and Astronautics; Chemical Engineering; Civil and Environmental Engineering; Earth, Atmospheric and Planetary Sciences; Materials Science and Engineering; Mathematics; Mechanical Engineering; or Nuclear Science and Engineering.

Doctoral thesis fields associated with each department are as follows:

• Aerospace Engineering and Computational Science
• Computational Science and Engineering (available only to students who matriculate in 2023–2024 or earlier)
• Chemical Engineering and Computation
• Civil Engineering and Computation
• Environmental Engineering and Computation
• Computational Materials Science and Engineering
• Mechanical Engineering and Computation
• Computational Nuclear Science and Engineering
• Nuclear Engineering and Computation
• Computational Earth, Science and Planetary Sciences
• 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. In contrast to the standalone PhD program, however, this research is expected to have a strong disciplinary component of interest to the host department.

The interdisciplinary CSE PhD program is administered jointly by CCSE and the host departments. Students must submit an application to the CSE PhD program, indicating the department in which they wish to be hosted. To gain admission, CSE program applicants must receive approval from both the host department graduate admission committee and the CSE graduate admission committee. See the website for more information about the application process, requirements, and relevant deadlines .

Once admitted, doctoral degree candidates are expected to complete the host department's degree requirements (including qualifying exam) with some deviations relating to coursework, thesis committee composition, and thesis submission that are specific to the CSE program and are discussed in more detail on the CSE website . The most notable coursework requirement associated with this CSE degree is a course of study comprising five graduate subjects in CSE (below).

Computational Concentration Subjects

Note: Students may not use more than 12 units of credit from a "meets with undergraduate" subject to fulfill the CSE curriculum requirements

Print this page.

The PDF includes all information on this page and its related tabs. Subject (course) information includes any changes approved for the current academic year.

ICME celebrates two decades of groundbreaking research, innovation, and academic excellence. 

Join us for ICME’s 20th Anniversary Research Symposium and Celebration Event on November 21 & 22, 2024

Research Symposium Celebration Event

Ask Big Questions, Solve Big Problems

Computational mathematics is at the heart of many engineering and science disciplines

Learn about ICME PhD & MS Programs and How to Apply

From fundamental to applied

Discover how computational mathematics, data science, scientific computing, and related fields are applied across a wide range of domains.

Graduate Programs

ICME faculty and students conduct groundbreaking research, provide consulting, and teach courses in computational mathematics and scientific computing.

State-of-the-Art Access

From the HANA Immersive Visualization Environment (HIVE) to diverse HPC infrastructure, ICME offers access to advanced technologies and resources for innovation.

Events & Seminars

First year ms seminar (cme 299): icme graduate student & career success course.

Hewlett 102 United States

Bridging the Farm: AI for Science at SLAC and Stanford

Lunch and learn with intel, icme career forum 2024.

Stanford University - Huang Engineering Center 475 Via Ortega Basement Atrium and Mackenzie Room Stanford , CA 94305 United States

What drives Susan Athey

The economist weighs in on incremental innovation, data-driven impact, and how economics is evolving to include a healthy dose of engineering.

September 03, 2024

New center harnesses AI to advance autonomous exploration of outer space

Researchers at the Center for AEroSpace Autonomy Research, or CAESAR, say that AI could, among other things, optimize spacecraft navigation, enhance the performance of planetary ro

June 18, 2024

Spotlight - Adrienne Propp, PhD Candidate, Computational & Mathematical Engineering

April 23, 2024

The Student Experience

Current Research Alumni

A Center of Collaboration

ICME PhD & MS students’ research is diverse and interdisciplinary ranging from bioinformatics, geosciences, computational finance, and more.

Study at Cambridge

About the university, research at cambridge.

• Undergraduate courses
• Events and open days
• Fees and finance
• Postgraduate courses
• How to apply
• Postgraduate events
• Fees and funding
• International students
• Continuing education
• Executive and professional education
• Courses in education
• How the University and Colleges work
• Term dates and calendars
• Visiting the University
• Annual reports
• Equality and diversity
• A global university
• Public engagement
• Give to Cambridge
• For Cambridge students
• For our researchers
• Business and enterprise
• Colleges & departments
• Email & phone search
• Museums & collections
• Academic Programmes
• The Centre for Scientific Computing
• Academic Programmes overview
• MPhil in Scientific Computing
• PhD at the Centre for Scientific Computing overview

Participating Research Groups

• Centre for Doctoral Training in Computational Methods for Materials Science overview
• CDT in CMMS programme overview
• Continuous Professional Development overview
• High Performance Computing Autumn Academy overview
• Lecture modules
• Lecture timetable
• Lecturers and staff
• Fees and Registration
• Related Groups overview
• Laboratory for Scientific Computing
• Computational Continuum Mechanics
• High Performance Computing Service
• Lennard-Jones Centre for Computational Material Science
• The Gianna Angelopoulos Programme for Science Technology and Innovation
• Theory of Condensed Matter
• Events overview
• Past Events

PhD in Scientific Computing

• Centre for Doctoral Training in Computational Methods for Materials Science
• Continuous Professional Development

Information on how to enter our PhD programme

A common route for admission into our PhD programme is via the Centre’s MPhil programme in Scientific Computing. The MPhil is offered by the University of Cambridge as a full-time course and introduces students to research skills and specialist knowledge. Covering topics of high-performance scientific computing and advanced numerical methods and techniques, it produces graduates with rigorous research and analytical skills, who are formidably well-equipped to proceed to doctoral research or directly into employment in industry.

List of the groups who offer PhD positions in Scientific Computing and its applications

© 2024 University of Cambridge

• Contact the University
• Accessibility
• Freedom of information
• Privacy policy and cookies
• Statement on Modern Slavery
• Terms and conditions
• University A-Z
• Undergraduate
• Postgraduate
• Research news
• About research at Cambridge
• Spotlight on...

• Dean’s Office
• External Advisory Council
• Computing Council
• Extended Computing Council
• Undergraduate Advisory Group
• Break Through Tech AI
• Building 45 Event Space
• Infinite Mile Awards: Past Winners
• Frequently Asked Questions
• Undergraduate Programs

Graduate Programs

• Educating Computing Bilinguals
• Online Learning
• Industry Programs
• AI Policy Briefs
• Envisioning the Future of Computing Prize 2024
• SERC Symposium 2023
• SERC Case Studies
• SERC Scholars Program
• SERC Group Leaders
• Common Ground Subjects
• For First-Year Students and Advisors
• For Instructors: About Common Ground Subjects
• Common Ground Award for Excellence in Teaching
• New & Incoming Faculty
• Faculty Resources
• Faculty Openings
• Search for: Search
• MIT Homepage

Below is a list of the MIT Schwarzman College of Computing’s graduate degree programs. The Doctor of Philosophy (PhD) degree is awarded interchangeably with the Doctor of Science (ScD).

Prospective students apply to the department or program under which they want to register. Application instructions can be found on each program’s website as well as on the MIT Graduate Admissions website.

Center for Computational Science and Engineering

The Center for Computational Science and Engineering (CCSE) brings together faculty, students, and other researchers across MIT involved in computational science research and education. The center focuses on advancing computational approaches to science and engineering problems, and offers SM and PhD programs in computational science and engineering (CSE).

• Computational Science and Engineering, SM and PhD . Interdisciplinary master’s program emphasizing advanced computational methods and applications. The CSE SM program prepares students with a common core of computational methods that serve all science and engineering disciplines, and an elective component that focuses on particular applications. Doctoral program enables students to specialize in methodological aspects of computational science via focused coursework and a thesis which involves the development and analysis of broadly applicable computational approaches that advance the state of the art.
• Computational Science and Engineering, Interdisciplinary PhD. Doctoral program offered jointly with eight participating departments, focusing on the development of new computational methods relevant to science and engineering disciplines. Students specialize in a computation-related field of their choice through coursework and a doctoral thesis. The specialization in computational science and engineering is highlighted by specially crafted thesis fields. 

Department of Electrical Engineering and Computer Science

The largest academic department at MIT, the Department of Electrical Engineering and Computer Science (EECS) prepares hundreds of students for leadership roles in academia, industry, government and research. Its world-class faculty have built their careers on pioneering contributions to the field of electrical engineering and computer science — a field which has transformed the world and invented the future within a single lifetime. MIT EECS consistently tops the U.S. News & World Report and other college rankings and is widely recognized for its rigorous and innovative curriculum. A joint venture between the Schwarzman College of Computing and the School of Engineering, EECS (also known as Course 6) is now composed of three overlapping sub-units in electrical engineering (EE), computer science (CS), and artificial intelligence and decision-making (AI+D).

• Computation and Cognition, MEng*. Course 6-9P builds on the Bachelor of Science in Computation and Cognition to provide additional depth in the subject areas through advanced coursework and a substantial thesis.
• Computer Science, PhD
• Computer Science and Engineering, PhD
• Computer Science, Economics, and Data Science, MEng*. New in Fall 2022, Course 6-14P builds on the Bachelor of Science in Computer Science, Economics, and Data Science to provide additional depth in economics and EECS through advanced coursework and a substantial thesis.
• Computer Science and Molecular Biology, MEng*. Course 6-7P builds on the Bachelor of Science in Computer Science and Molecular Biology to provide additional depth in computational biology through coursework and a substantial thesis.
• Electrical Engineering, PhD
• Electrical Engineering and Computer Science, MEng* , SM* , and PhD . Master of Engineering program (Course 6-P) provides the depth of knowledge and the skills needed for advanced graduate study and for professional work, as well as the breadth and perspective essential for engineering leadership. Master of Science program emphasizes one or more of the theoretical or experimental aspects of electrical engineering or computer science as students progress toward their PhD.
• Electrical Engineer / Engineer in Computer Science.** For PhD students who seek more extensive training and research experiences than are possible within the master’s program.
• Thesis Program with Industry, MEng.* Combines the Master of Engineering academic program with periods of industrial practice at affiliated companies. 

* Available only to qualified EECS undergraduates. ** Available only to students in the EECS PhD program who have not already earned a Master’s and to Leaders for Global Operations students.

Institute for Data, Systems, and Society

The Institute for Data, Systems, and Society advances education and research in analytical methods in statistics and data science, and applies these tools along with domain expertise and social science methods to address complex societal challenges in a diverse set of areas such as finance, energy systems, urbanization, social networks, and health.

• Social and Engineering Systems, PhD. Interdisciplinary PhD program focused on addressing societal challenges by combining the analytical tools of statistics and data science with engineering and social science methods.
• Technology and Policy, SM . Master’s program addresses societal challenges through research and education at the intersection of technology and policy.
• Interdisciplinary Doctoral Program in Statistics . For students currently enrolled in a participating MIT doctoral program who wish to develop their understanding of 21st-century statistics and apply these concepts within their chosen field of study. Participating departments and programs: Aeronautics and Astronautics, Brain and Cognitive Sciences, Economics, Mathematics, Mechanical Engineering, Physics, Political Science, and Social and Engineering Systems.

Operations Research Center

The Operations Research Center (ORC) offers multidisciplinary graduate programs in operations research and analytics. ORC’s community of scholars and researchers work collaboratively to connect data to decisions in order to solve problems effectively — and impact the world positively.

In conjunction with the MIT Sloan School of Management, ORC offers the following degrees:

• Operations Research, SM and PhD . Master’s program teaches important OR techniques — with an emphasis on practical, real-world applications — through a combination of challenging coursework and hands-on research. Doctoral program provides a thorough understanding of the theory of operations research while teaching students to how to develop and apply operations research methods in practice.
• Business Analytics, MBAn. Specialized advanced master’s degree designed to prepare students for careers in data science and business analytics.

MIT Doctoral Program in Computational Science and Engineering

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

MIT Doctoral Program in Computational Science and Engineering (CSE PhD)

Program overview.

The standalone doctoral program in Computational Science and Engineering ( PhD in CSE)  enables students to specialize at the doctoral level in fundamental, methodological aspects of computational science via focused coursework and a thesis. The emphasis of thesis research activities is the development and analysis of broadly applicable computational approaches that advance the state of the art.

Students are awarded the Doctor of Philosophy in Computational Science and Engineering upon successful completion of the program requirements and defense of a thesis describing significant contributions to the CSE field. Program requirements include a course of study comprising nine graduate subjects and a graduate seminar. Core and concentration subjects cover six “ways of thinking” fundamental to CSE: (i) discretization and numerical methods for partial differential equations; (ii) optimization methods; (iii) statistics and data-driven modeling; (iv) high-performance computing and/or algorithms; (v) mathematical foundations (e.g., functional analysis, probability); and (vi) modeling (i.e., a subject that treats mathematical modeling in any science or engineering discipline). Subjects taken as part of an MIT SM program can be counted toward the coursework requirement provided they satisfy core, concentration, or elective requirements as set forth  here ; consultation and approval by the program director(s) and/or administrator regarding the application of such courses toward program credit is always required.

Students applying to this program are expected to have a degree in CSE, applied mathematics, or another field that prepares them for an advanced degree in CSE. More information about the application process, requirements, and relevant deadlines can be found on the  Admissions section .

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

Boston University Academics

Boston University

• Campus Life
• Schools & Colleges
• Degree Programs
• Search Academics

PhD in Computing & Data Sciences

For more information and to get in touch, please visit the Faculty of Computing & Data Sciences website .

The PhD program in Computing & Data Sciences (CDS) at Boston University prepares its graduates to make significant contributions to the art, science, and engineering of computational and data-driven processes that are woven into all aspects of society, economy, and public discourse, leading to solutions of problems and synthesis of knowledge related to the methodical, generalizable, and scalable extraction of insights from data as well as the design of new information systems and products that enable actionable use of those insights to advance scholarly as well as practical pursuits in a wide range of application domains.

Applicants to the PhD program in CDS are expected to have earned a bachelor’s or master’s degree in one of the methodological or applied disciplines relating to the computational and data-driven areas of scholarship in CDS. They are expected to possess basic mathematical and computational competencies, and demonstrable propensity for cross-disciplinary work. To accommodate a diversity of student backgrounds and preparations, a holistic admission review is utilized. As such, GRE tests and scores are not required, but could be optionally provided and considered as part of the applicant’s portfolio, which may also include evidence of prior, relevant preparation, including creative works, software code repositories, etc. Special attention will be paid to applicants from underrepresented backgrounds in computing and data science disciplines.

Completion of the PhD degree in CDS requires coursework covering breadth and depth topics spanning the foundational, applied, and sociotechnical dimensions of computing and data science; completion of research rotations that expose students to ongoing projects; completion of a cohort-based training on ethical and responsible computing; and successful proposal and defense of a doctoral thesis.

For their thesis work, and in preparation for careers in academia, industry, and government, CDS PhD students are expected to pursue theoretical, applied, or empirical studies leading to solution of new problems and synthesis of new knowledge in a topic area determined in consultation with their mentors and collaborators, which may include external researchers and practitioners in industrial and academic research laboratories.

Upon completion of the program, students will be prepared to pursue careers in which they lead independent cutting-edge research and development agendas, whether in academia (by teaching, mentoring, and supervising teams of students engaged in scholarly pursuits) or in industry (by collaborating, directing, and effectively managing diverse teams of practitioners working at the forefront of industrial R&D).

Learning Outcomes

The following learning outcomes explain what you will be able to do at the end of your time as a CDS PhD candidate, as a result of earning your degree.

• Exhibit a strong grasp of the principles governing the design and implementation of the methodological approaches for computational and data-driven inquiry.
• Identify the literature and demonstrate mastery of the compendium of works relevant to a well-defined area of research inquiry in computing and data sciences.
• Show capacity to engage meaningfully in and materially contribute to multidisciplinary research and development endeavors.
• Evidence a strong sense of social and professional responsibility for decisions related to the development and deployment of computational and data-driven technologies.
• Assess and argue the merits, limitations, and possibilities of new research work in a specialized area at the level commensurate with standards of scholarly venues in that area.
• Formulate and pursue a research agenda leading to solution of new problems and to synthesis of new knowledge shared through peer-reviewed publications.

Course Requirements

Sixteen term courses (64 units) are required for post-BA/BS students and 12 term courses (48 units) are required for post-MA/MS students. Students with prior graduate work (including master’s degrees) may be able to transfer up to two courses (8 units) as long as these units were not used to fulfill matriculation requirements, upon the recommendation of the student’s academic advisor, and subject to approval by the Associate Provost for CDS.

Of the 16 courses, up to 3 undergraduate courses (12 units) may be counted as background courses, selected in consultation with the student’s academic advisor and subject to approval by the Associate Provost for CDS. Other than these remedial courses, all other courses must be graduate-level courses or directed studies offered by CDS or by other BU departments in order to satisfy the following degree requirements.

The methodology core requirement ensures that students possess foundational knowledge and competencies in a subset of the following eight methodological areas of CDS:

• Mathematical Foundations of Data Science
• Statistical Modeling and Inference
• Efficient and Scalable Algorithms
• Predictive Analytics and Machine Learning
• Combinatorial Optimization and Algorithms
• Computational Complexity
• Programming and Software Design
• Large-scale Data Management

A list of courses that can be used to satisfy these competencies will be maintained on the website for CDS. Students who start their PhD program in CDS are expected to satisfy at least six of these competencies. Students who complete the course requirement for the PhD program in a cognate discipline are expected to satisfy at least four of these competencies.

The subject core requirement ensures that students establish depth in one area of inquiry that is aligned with either the methodological or applied dimensions of CDS. Subject areas are defined by groups of CDS faculty members working in related disciplinary and/or interdisciplinary areas of research who expect their prospective students to have enough depth in the subset of topics to enable them to tackle doctoral-level research in these topics. The set of subject areas as well as a list of preapproved graduate-level courses offered in CDS or elsewhere at BU that can be used to satisfy each subject area will be maintained on the website for CDS.

During the first two years in the program, all PhD candidates in CDS must complete three cohort-based requirements; namely, a two-term training course (4 units) covering various aspects of the responsible and ethical conduct of computational and data-driven research, a two-term doctoral seminar (4 units) that introduces them to the research portfolios of CDS faculty members as well as to the skills and capacities needed for success as scholars, and at least two research or lab rotations (8 units) that expose them to real-world computational and data-driven applications that must be tackled through effective multidisciplinary teamwork.

A cumulative GPA not less than 3.3 must be maintained for all non-Pass/Fail courses taken to satisfy the methodology core requirement and the subject core requirement of the degree, excluding any background courses and excluding any transferred units. Students who receive grades of B– or lower in any three courses taken at BU will be withdrawn from the program.

Language Requirement

There is no foreign language requirement for the PhD degree in CDS.

Qualifying Examinations

No later than the end of the sixth term (third year), all PhD candidates in CDS must pass a public oral examination administered by a committee of three faculty members, chaired by the student’s research (and presumptive thesis) advisor or coadvisors. The oral area exam is meant to establish the student mastery of a well-defined area of scholarship and preparedness to pursue original research in that area. The oral area examination may require completion of a survey paper or completion of a pilot project ahead of the examination. The scope as well as any additional requirements needed for the examination should be developed in consultation with and approval of the research advisor(s), at least one term prior to the exam.

Dissertation and Final Oral Examination

Candidates shall demonstrate their abilities for independent study in a dissertation representing original research or creative scholarship. A prospectus for the dissertation must be successfully defended no later than the end of the eighth term (fourth year) of study.

Candidates must undergo a final oral examination no later than the end of the 10th term (fifth year) of study in which they defend their dissertation as a valuable contribution to knowledge in their field and demonstrate a mastery of their field of specialization in relation to their dissertation.

Both the prospectus and final dissertation must be administered by a dissertation committee of at least three readers (including the dissertation advisor or coadvisors) and chaired by a CDS faculty member who is not one of the readers.

Related Bulletin Pages

• Abbreviations and Symbols

Beyond the Bulletin

• Faculty of Computing & Data Sciences
• Data Science for Good
• Impact Labs & Co-Labs
• BS in Data Science
• BS/MS in Data Science
• MS in Data Science
• MS in Data Science (Online)
• PhD in Computing & Data Sciences
• Minor in Data Science
• BS in Data Science/MS in Bioinformatics
• MS in Bioinformatics
• PhD in Bioinformatics

Terms of Use

Note that this information may change at any time. Read the full terms of use .

Accreditation

Boston University is accredited by the New England Commission of Higher Education (NECHE).

• © Copyright
• Mobile Version

• Chancellor’s Message

Print Options

2024-25 edition, computational science, ph.d..

Lee Swindlehurst, UCI Director 949-824-2818 computationalscience.uci.edu

Joint Doctoral Program with UC Irvine and San Diego State University

A joint offering with San Diego State University (SDSU), the Ph.D. program in Computational Science trains professionals capable of developing novel computational approaches to solve complex problems in both fundamental sciences and applied sciences and engineering. A program of study combining applied mathematics, computing, and a solid training in basic science culminates in doctoral research focused on an unsolved scientific problem.

The Ph.D. in Computational Science produces broadly educated, research-capable scientists that are well prepared for diverse careers in academia, industry, business, and government research laboratories.

Students are admitted into the joint program via a Joint Admissions Committee. Applicants apply to UCI directly using the UCI graduate application.

Applicants are expected to hold a Bachelor’s degree in one of the science, technology, engineering, and mathematics (STEM) fields.

Applicants are evaluated on the basis of their prior academic record and their potential for creative research and teaching, as demonstrated in submitted materials. These materials include official university transcripts, three letters of recommendation, a Statement of Purpose, and a Personal History statement.

Program Requirements

The normative time to completion is five years. The maximum time to completion is seven years. A total minimum of 66 units of course work, independent study, and research must be completed. These units must be distributed as follows:

• Minimum of 18 units of graduate-level coursework as SDSU.
• Minimum of 24 units of graduate-level coursework at UCI.
• Minimum of 24 units of combined research, practicum, and dissertation research units at either institution.

Summer Research Survey

Students are required to attend the annual summer seminar series featuring participating faculty members describing their current research and possible projects.

Course Requirements

Core courses at sdsu.

MATH 636 - Mathematical Modeling OR MATH 638 - Continuous Dynamical Systems and Chaos MATH 693B - Advanced Computational PDEs COMP 605 - Scientific Computing

Elective Courses at SDSU

Students select 9 units from the following list, or appropriate substitutions, with the approval of the program director and their research mentor

AE 601 - Computational Fluid Mechanics AE 641 - Structural Optimization AE 670 - Optimal Control BIOL 606 - Biological Data BIOL 668 - Advanced Biological Data Analysis BIOL 740 - Phylogenetic Systematics BIOMI 608 - Programming Problems in Bioinformatics CHEM 711 - Chemical Thermodynamics CHEM 712 - Chemical Kinetics CHEM 713 - Quantum Chemistry CIVE 620 - Traffic Flow and Control CIVE 697 - Traffic Signals Systems Operations and Control COMP 526 - Computational Methods for Scientists COMP 607 - Computational Database Fundamentals COMP 670 - Seminar: Problems in Computational Science CS 600 - Bioinformatics CS 610 - Computational Genomics CS 653 - Data Mining and Knowledge CS 666 - Advanced Distributed Systems CS 696 - Programming Problems in Bioinformatics EE 645 - Antennas and Wave Propagation EE 657 - Digital Signal Processing EE 658 - Advanced Digital Signal Processing EE 665 - Multimedia Wireless Networks EE 740 - Advanced Topics in Physical Electronics Antenna Design MATH 693A - Advanced Computational Optimization MATH 693B - Advanced Computational PDEs MB 610A-B - Advanced Topics in Molecular Biology ME 610 - Finite Element Methods PHYS 604 - Electricity and Magnetism PHYS 606 - Statistical Mechanics PHYS 608 - Classical Mechanics PHYS 610 - Quantum Mechanics STAT 657 - Statistical and Machine Learning Methods STAT 658 - Advanced Data Analytics STAT 676 - Bayesian Statistics STAT 678 - Survival Analysis STAT 700 - Data Analysis STAT 701 - Monte Carlo Methods STAT 702 - Data Mining

Core Courses at UCI

Elective Courses at UCI

Students select 8 units from the following list, or appropriate substitutions, with the approval of the program director and their research mentor

Research Units at SDSU

COMP 897 - Doctoral Research COMP 898 - Practicum COMP 899 - Dissertation

Research Units at UCI

Practicum and Doctoral Research

Dissertation research is carried out at either UCI or SDSU, or at an industry or national laboratory under the supervision of the Doctoral Advisor. While conducting dissertation research, students must enroll in the appropriate research units at the campus of the Doctoral Advisor. If research is done outside of UCI or SDSU, students should register in-absentia if appropriate.

Research Report Exam

The student is expected to pass the Research Report Exam within three years of admittance. This examination consists of a term research project supervised by a faculty mentor. The student is required to prepare a written account of research work performed and its results, and offer an oral presentation before the members of the Doctoral Committee. The student must submit a paper based on their research report before giving the oral presentation to the Doctoral Committee. Should a student fail the Research Report Exam, one retake is allowed.

Dissertation Proposal

Students must submit a dissertation proposal to the doctoral committee by the end of their third year in the program. This proposal should take the form of a scientific grant proposal to a major funding agency. It should describe the research project that the student intends to carry out and upon which their doctoral dissertation will be based. The student must also offer an oral presentation of the proposal before the Computational Science faculty. Upon successful completion of this presentation, the student will be recommended for advancement to candidacy for the doctoral degree.

Advancement to Doctoral Candidacy

After successful completion of the dissertation proposal and certification that all other requirements are fulfilled, the student is advanced to candidacy at both campuses. Students not registered at UCI will need to formally advance to candidacy in the summer term. Advancement to candidacy for the Ph.D. must occur at least one term prior to dissertation defense.

Dissertation and Final Oral Examination

On completion of the research, the student prepares the dissertation in accordance with UCI regulations. A final draft of the dissertation is presented to each member of the doctoral committee at least three weeks prior to the final oral examination. The oral defense is held on the campus of the primary faculty advisor. Students must follow UCI filing deadlines. Students are required to be registered for Dissertation Research (3 units) at SDSU and Dissertation Research (4 units) simultaneously at UCI during the semester in which they present their doctoral defense. Alternatively, students can request filing fee status at UCI in the quarter in which they present their doctoral defense.

University of California, Irvine Faculty

Mohammad A. Al Faruque, Ph.D. University of Kaiserslautern, Chair of Emulex Career Development and Associate Professor of Electrical Engineering and Computer Science; Mechanical and Aerospace Engineering (system-level design, embedded systems, cyber-physical-systems, multi-core systems)

Jun F. Allard, Ph.D. University of British Columbia, Assistant Professor of Mathematics; Physics and Astronomy (Mathematical and computational biology, biopolymers, biomembranes, force-sensitive biomolecular bonds)

Ioan Andricioaei, Ph.D. Boston University, Professor of Chemistry (Theoretical Chemistry and Biophysics: Developing novel theoretical techniques and applying computer and modeling methods to describe, in terms of dynamics and thermodynamics, biologically important molecular processes, with the aim to complement, enhance or predict experimental findings.)

Pierre F. Baldi, Ph.D. California Institute of Technology, UCI Chancellor's Professor of Computer Science; Biological Chemistry; Biomedical Engineering; Developmental and Cell Biology (Bioinformatics, computational biology, AI and machine learning with particular emphasis on: Deep Learning, Neural Networks, Reinforcement Learning, and their Theoretical Foundations and Applications)

Kieron Burke, Ph.D. University of California, Santa Barbara, Professor of Chemistry; Physics and Astronomy (Physical chemistry and chemical physics, polymer, materials, nanoscience, theoretical and computational)

Filippo Capolino, Ph.D. University of Florence, Italy, Professor of Electrical and Computer Science (Optics/electromagnetics in nanostructures and sensors, antennas/microwaves, RF and wireless systems)

Ann Marie Carlton, Ph.D. Rutgers University, Associate Professor of Chemistry (Atmospheric chemistry, aerosol liquid water, cloud processing, secondary organic aerosol)

Peter Chang, M.D. Northwestern University, Assistant Professor in Residence of Radiological Sciences; Computer Science; Pathology and Laboratory Medicine

Olivier Cinquin, Ph.D. University College London, Assistant Professor of Developmental and Cell Biology (Mathematical modeling of networks, systems biology)

Donald A. Dabdub, Ph.D. California Institute of Technology, Professor of Mechanical and Aerospace Engineering; Civil and Environmental Engineering (Mathematical modeling of urban and global air pollution, dynamics of atmospheric aerosols, secondary organic aerosols, impact of energy generation on air quality, chemical reactions at gas-liquid interfaces)

Kristen Davis, Ph.D. Stanford University, Assistant Professor of Civil and Environmental Engineering; Earth System Science (Coastal Dynamics)

Franco De Flaviis, Ph.D. University of California, Los Angeles, Professor of Electrical Engineering and Computer Science (microwave systems, wireless communications, electromagnetic circuit simulations)

Russell L. Detwiler, Ph.D. University of Colorado Boulder,  Associate Professor   of Civil and Environmental Engineering  (groundwater hydrology, contaminant fate and transport, subsurface process modeling, groundwater/surface-water interaction)

Efi Foufoula-Georgiou, Ph.D. University of Florida, Distinguished Professor of Civil and Environmental Engineering (hydrology and geomorphology with emphasis on modeling the interactions between the atmosphere, land, and the terrestrial environment at plot to large-watershed scale)

Filipp Furche, Ph.D. University of Karlsruhe, Professor of Chemistry (Physical chemistry and chemical physics, theoretical and computational)

Robert Benny Gerber, Ph.D. University of Oxford, Professor of Chemistry (Vibrational spectroscopy, chemical reaction dynamics, biological molecules, molecular dynamics)

Wayne B. Hayes, Ph.D. University of Toronto, Associate Professor of Computer Science (Biomedical Informatics and Computational Biology, Computer Vision Scientific and Numerical Computing)

Alexander Ihler, Ph.D. Massachusetts Institute of Technology, Associate Professor of Information and Computer Science (Artificial intelligence and machine learning, focusing on statistical methods for learning from data and on approximate inference techniques for graphical models)

Perry Johnson, Ph.D. John Hopkins University, Assistant Professor of Mechanical and Aerospace Engineering (turbulent flows, particle-laden and multiphase flows, turbulent boundary layers, large-eddy simulations, scientific computing)

Frithjof Kruggel, M.D., Ph.D. Ludwig Maximilian University of Munich, Professor of Biomedical Engineering; Electrical Engineering and Computer Science (Biomedical signal and image processing, anatomical and functional neuroimaging in humans, structure-function relationship in the human brain)

Arthur D. Lander, Ph.D. University of California, San Francisco, Donald Bren Professor and Professor of Developmental and Cell Biology; Biomedical Engineering; Logic and Philosophy of Science; Pharmacology (Systems biology of development, pattern formation, growth control)

Marco Levorato, Ph.D. University of Padua,  Associate Professor   of Computer Science; Electrical Engineering and Computer Science  (artificial intelligence and machine learning, networks and distributed systems, statistics and statistical theory, stochastic modeling, signal processing)

Mo Li, Ph.D. University of Michigan, Assistant Professor of Civil and Environmental Engineering (ultra-damage-tolerant and multifunctional composite materials for protective and resilient structures, built environments, and energy infrastructure)

Feng Liu, Ph.D. Princeton University, Professor of Mechanical and Aerospace Engineering (Computational fluid dynamics and combustion, aerodynamics, aeroelasticity, propulsion, turbomachinery aerodynamics and aeromechanics)

John S. Lowengrub, Ph.D. Courant Institute of Mathematical Sciences, UCI Chancellor's Professor of Mathematics; Biomedical Engineering; Chemical Engineering and Materials Science (Applied and computational mathematics, mathematical and computational biology)

Ray Luo, Ph.D. University of Maryland, College Park, Professor of Molecular Biology and Biochemistry; Biomedical Engineering; Chemical Engineering and Materials Science (Protein structure, non-covalent associations involving proteins)

Vladimir A. Mandelshtam, Ph.D. Institute of Spectroscopy, Academy of Sciences of USSR, Professor of Chemistry (Theoretical and Computational Chemistry)

Craig C. Martens, Ph.D. Cornell University, Professor of Chemistry (Theoretical Chemistry, Chemical Physics)

Eric D. Mjolsness, Ph.D. California Institute of Technology, Professor of Computer Science; Mathematics (Applied mathematics, mathematical biology, modeling languages)

David L. Mobley, Ph.D. University of California, Davis, Associate Professor of Pharmaceutical Sciences; Chemistry (Chemical biology, physical chemistry and chemical physics, theoretical and computational)

Mathieu Morlighem, Ph.D. Ecole Centrale Paris, Vice Chair and Associate Professor of Earth System Science

Seyed Ali Mortazavi, Ph.D. California Institute of Technology, Assistant Professor of Developmental and Cell Biology (Functional genomics to study transcriptional regulation in development)

Shaul Mukamel, Ph.D. Tel Aviv University, UCI Distinguished Professor of Chemistry; Physics and Astronomy (Physical chemistry and chemical physics, polymer, materials, nanoscience, theoretical and computational)

Alexandru Nicolau, Ph.D. Yale University, Department Chair and Professor of Computer Science; Electrical Engineering and Computer Science (Architecture, parallel computation, programming languages and compilers)

Qing Nie, Ph.D. Ohio State University, C hancellor's Professor, Developmental & Cell Biology (Computational Biology; Systems Biology; Stem Cells; Regulatory Networks; Stochastic Dynamics; Scientific Computing and Numerical Analysis)

Francois W. Primeau, Ph.D. Massachusetts Institute of Technology, Professor of Earth System Science

Michael S. Pritchard, Ph.D. University of California, San Diego, Associate Professor of Earth System Science

Roger H. Rangel, Ph.D. University of California, Berkeley, Professor of Mechanical and Aerospace Engineering (Fluid dynamics and heat transfer of multiphase systems including spray combustion, atomization and metal spray solidification, applied mathematics and computational methods)

Elizabeth L. Read, Ph.D. University of California, Berkeley, Assistant Professor of Chemical Engineering and Materials Science; Molecular Biology and Biochemistry (Dynamics of complex biochemical systems, regulation of immune responses)

Eric Rignot, Ph.D. University of Southern California, Donald Bren Professor of Earth System Science (Glaciology, climate change, radar remote sensing, ice sheet modeling, interferometry, radio echo sounding, ice-ocean interactions)

Timothy Rupert, Ph.D. Massachusetts Institute of Technology, Assistant Professor of Mechanical and Aerospace Engineering; Chemical Engineering and Materials Science (Mechanical behavior, nanomaterials, structure property relationships, microstructural stability, grain boundaries and interfaces, materials characterization)

Manabu Shiraiwa, Ph.D. Max Planck Institute for Chemistry, Associate Professor of Chemistry (Atmospheric Chemistry, Heterogeneous and Multiphase Chemistry, Aerosol Particles, Reactive Oxygen Species, Kinetic Modeling)

Hal S. Stern, Ph.D. Stanford University, Professor of Statistics; Cognitive Sciences (Bayesian methods, model diagnostics, forensic statistics, and statistical applications in biology/health, social sciences, and sports)

Lizhi Sun, Ph.D. University of California, Los Angeles, Professor of Civil and Environmental Engineering; Chemical Engineering and Materials Science (Micro- and nano-mechanics, composites and nanocomposites, smart materials and structures, multiscale modeling, elastography)

A. Lee Swindlehurst, Ph.D. Stanford University, Professor of Electrical Engineering and Computer Science (Signal processing, estimation and detection theory, applications in wireless communications, geo-positioning, radar, sonar, biomedicine)

Kevin Thornton, Ph.D. University of Chicago, Associate Professor of Ecology and Evolutionary Biology School of Biological Sciences (Genome evolution, gene duplication, population genetics, adaptation)

Douglas J. Tobias, Ph.D. Carnegie Mellon University, Professor of Chemistry (Atmospheric and environmental, chemical biology, physical chemistry and chemical physics, theoretical and computational)

Isabella Velicogna, Ph.D. Università degli Studi di Trieste, UCI Chancellor's Fellow and Professor of Earth System Science

Nalini Venkatasubramanian, Ph.D., University of Illinois at Urbana-Champaign, Professor of Computer Science (Distributed Systems Middleware, Multimedia Systems and Applications, Mobile and Pervasive Computing, Formal Methods, Data Management, and Grid Computing)

Jasper A. Vrugt, Ph.D. University of Amsterdam, Associate Professor of Civil and Environmental Engineering; Earth System Science (Complex systems, modeling, statistics, hydrology, geophysics, ecology, data, optimization, hydropower, data assimilation)

Yun Wang, Ph.D. Pennsylvania State University, Associate Professor of Mechanical and Aerospace Engineering (Fuel cells, computational modeling, thermo-fluidics, two-phase flows, electrochemistry, Computational Fluid Dynamics (CFD), turbulent combustion)

Zhiying Wang, Ph.D. California Institute of Technology, Assistant Professor of Electrical Engineering and Computer Science (information theory, coding theory for data storage, modeling, compression, and computation for genomic data)

Daniel Whiteson, Ph.D. University of California, Berkeley, Professor of Physics and Astronomy; Logic and Philosophy of Science (Particle Physics: Experimental High Energy Physics, structure of matter and the nature of its interactions at the very smallest scales)

Dominik Franz X. Wodarz, Ph.D. University of Oxford, Professor of Ecology and Evolutionary Biology; Mathematics; Program in Public Health (Dynamics of virus infections and the immune system, dynamics of cancer and its treatment, and general evolutionary dynamics and population dynamics)

Xiaohui Xie, Ph.D. Massachusetts Institute of Technology, Professor of Computer Science; Developmental and Cell Biology (computational biology, bioinformatics, genomics, neural computation, machine learning)

Charles S. Zender, Ph.D. University of Colorado Boulder, Professor of Earth System Science; Computer Science

San Diego State University Faculty

Reza Akhavian, Ph.D. University of Central Florida, Assistant Professor of Department of Civil, Construction, and Environmental Engineering (Construction Engineering and Management, Internet of Things (IoT), Data Analytics, Machine Learning, Robotics, Cyber-Physical Systems, Building Information Modeling (BIM)

Ashkan Ashrafi, Ph.D. University of Alabama, Huntsville, Associate Professor of Electrical and Computer Engineering (Digital and Statistical Signal Processing, Real-Time DSP, Biomedical Signal Processing, Fourier Analysis, Direct Digital Frequency Synthesizers, Multivariate Spectral Analysis, Hilbert Spaces, Matrix Theory and Applications)

Barbara Ann Bailey, Ph.D. North Carolina State University, Associate Professor of Statistics (Nonlinear Time Series, Dynamical Systems, and Clouds. Visualization of Nonlinear Models. Environmental Monitoring. Population Dynamics and Embryonic Mortality. Model Validation)

Arlette Baljon, Ph.D. University of Chicago, Associate Professor of Physics (Biophysics, Complex Networks, Polymer Science and computational soft matter physics)

Amneet Bhalla, Ph.D. Northwestern University, Assistant Professor of Mechnical Engineering (Fluid-Structure Interaction, Multiphase Flows, Aquatic Locomotion, Renewable Energy Device Modeling, Numerical Methods, High Performance Computing, Scientific Software Design)

Peter Blomgren, Ph.D. University of California, Los Angeles, Professor of Mathematics (Image Processing, Wave Propagation in Complex Media, Numerical Solutions of PDEs, Scientific Computing, Nonlinear Dynamical Systems)

Joaquin Camacho, Ph.D. University of California, Los Angeles, Assistant Professor of Mechanical Engineering (Multiphase Flows, Sustainable Energy, Nanomaterial Theory and Fabrication, Combustion, Aerosol Dynamics, Carbon Materials)

Margherita Capriotti, Ph.D. University of California, San Diego, Assistant Professor of Aerospace Engineering (Develop novel and efficient tools to characterize aerospace composite structures using wave propagation of different physical nature)

Ricardo Carretero, Ph.D. University College London, Professor of Mathematics (Nonlinear Dynamics, Nonlinear Waves, Bose-Einstein Condensation (BEC))

Jose Castillo, Ph.D. University of New Mexico, Professor of Mathematics (Numerical Solution of Partial Differential Equations, Scientific Computing, and Modeling)

Jianwei Chen, Ph.D. Chinese University of Hong Kong, Associate Professor of Statistics (Statistical Inferences for Nonlinear Dynamic Models, Bayesian Methods, MCMC, and Computational Statistics)

Andy Cooksy, Ph.D. University of California, Berkeley, Professor of Chemistry and Biochemistry (Laser Spectroscopy, Reaction Dynamics, and Ab Initio Calculation of Free Radicals and Other Transient Molecule)   Chris Curtis,  Ph.D., University of Washington, Assistant Professor of Mathematics (Fluid Mechanics, Modeling and Simulation, Computational Fluid Dynamics and Numerical Simulation)

Bryan Donyanavard, Ph.D., University of California, Irvine, Assistant Professor of Computer Science (Runtime Resource Management for Energy-Efficient Execution of Cyber-Physical Systems)   Robert Edwards, Ph.D. University of Sussex, Brighton, England, Professor of Computer Science (Microbiology, Bioinformatics, and High Performance Computing)   Juanjuan Fan, Ph.D. University of Washington, Professor of Statistics (Multivariate Failure Time Data, Tree Based Methods, Genetic Epidemiology)

Uduak George, Ph.D. University of Sussex, Brighton, UK, Assistant Professor of Mathematics and Statistics (Mathematical biology, fluid dynamics, continuum mechanics of tissues, morphogenesis, solute transport)   Jerome Gilles,  Ph.D. Ecole Normale Supeieure, France, Assistant Professor of Mathematics (Applied Harmonic/Functional Analysis, Signal/Image Processing, data driven methods, Functional analysis)

Kyle Hasenstab, Ph.D. University of California, Los Angeles, Assistant Professor of Statistics (Deep neural networks, medical image analysis, interpretability of AI algorithms, functional data analysis)

Hajar Homayouni,  Ph.D. Colorado State University, Assistant Professor of Computer Science (Data Quality Testing, Big Data, and Machine Learning)

Ke Huang, Ph.D. University of Grenoble, France, Assistant Professor of Electrical and Computer Engineering   (VLSI Testing, Fault Modeling and Diagnosis. Machine Learning, Data Mining. Trustworthy ICs. Computer-Aided Design)   Gustav Jacobs, Ph.D., University of Illinois at Chicago, Professor of Aerospace Engineering (Computational Physics, High-Order Methods, Fluid and Plasma Dynamics)   Calvin Johnson, Ph.D. University of Washington, Professor of Physics (Theoretical and Computational Nuclear Structure and Nuclear Astrophysics)   Parag Katira,  Ph.D. University of Florida, Assistant Professor of Mechanical Engineering (Biomolecular Motors, Cell Mechanics, Mechanosensing, Tissue Dynamics, Soft Matter Interactions, Design of Active Materials)

Alicia Kinoshita, Ph.D. University of California, Los Angeles, Associate Professor of Civil Engineering (Hydrologic change in coupled human-natural systems)   Sunil Kumar, Ph.D.  Birla  Institute  of  Technology and Science, India, Professor of Electrical and Computer Engineering and Thomas G. Pine Faculty Fellow (Wireless Networks, Multimedia Traffic, and Video Processing Techniques)   Lyuba Kuztnesova, Ph.D. Cornell University, Assistant Professor of Physics (Nanophotonics, ultrafast lasers, and cavity quantum electrodynamics and high energy short-pulse generation in fiber laser systems, mode-locking in quantum cascade lasers, blue LEDs, microcavities, and metamaterials)   Richard Levine, Ph.D. Cornell University, Professor of Statistics (Markov Chain Monte Carlo Methods, Environmental Statistics, Biostatistics, Bayesian Decision Theory)   Xiaobai Liu,  Ph.D.  Huazhong  University  of  Science and Technology, China, Associate Professor of Computer Science (Computer Vision, Machine Learning, Computational Statistics and their applications to clinic diagnosis, sports, transportation, surveillance, video games and others)   Antonio Luque, Ph.D. University of Barcelona, Assistant Professor of Mathematics (Applied Mathematics, Biophysics, Physical Virology+ theoretical and computational biophysics as well as mathematical modeling, molecular and physicochemical properties of viruses in viral ecology)

Sahar Ghanipoor Machiani, Ph.D. Virginia Tech University, Assistant Professor of Civil, Construction, and Environmental Engineering (Traffic Safety and Signal Operation, Human Behavior Modeling, Connected/Automated Vehicles, Evacuation Modeling Infrastructure-Based Safety Systems)

Marta Miletic, Ph.D. Kansas State University, Assistant Professor of Civil, Construction, and Environmental Engineering (Geotech Engineering)

Duy Nguyen, Ph.D. McGill University, Canada, Assistant Professor of Electrical and Computer Engineering (Signal Processing, Communications, and Information Theories for Wireless Systems and Networks)   Kenneth Nollett, Ph.D. University of Chicago, Assistant Professor of Physics (Theoretical and computational physics, spanning the interface between nuclear physics and astrophysics)   Christopher Paolini, Ph.D. San Diego State University, Assistant Professor of Electrical and Computer Engineering (Cyberinfrastructure, Computational Geochemistry and Combustion Science)

Pavel Popov, Ph.D. Cornell University,   Professor of Aerospace Engineering (Computational combustion with applications to aerospace propulsion. His research interests include combustion instability in aerospace engines, stochastic modelling of turbulent combustion, plasma-combustion interactions simulation of multiphase flow, turbulence modelling and high-performance computing.)

Shangping Ren, Ph.D.  University of  Illinois at Urbana-Champaign, Professor of Computer Science (Cyber-Physical Systems, Real-Time Scheduling, and Cloud Computing)   Forest Rohwer, Ph.D. San Diego State University, Professor of Biology (Genomic Analysis of Phage, Diversity of Coral-associated Bacteria, Opportunistic Infections and Coral Disease)

Eric Sandquist, Ph.D. University of California, Santa Cruz, Professor of Astronomy (Physics of Stars and the Way They Age)   Anca Segal, Ph.D. University of Utah, Professor of Biology (The Mechanism of Site-Specific Recombination; Structure/Function Analysis of Recombination Proteins)

Ignacio Sepulveda, Ph.D. Cornell University, Assistant Professor of Civil Engineering (Coastal Hazards, Coastal Engineering, Tsunami Science, Seismology, Stochastic Calculus for Uncertainty Quantification, Remote sensing, Wave Mechanics, Inversions)

Arun Sethuraman, Ph.D. Iowa State University, Assistant Professor of Bioinformatics (Population Genomics, Evolution, Bioinformatics)  

Satish Sharma, Ph.D. Banaras Hindu University, India, Professor of Electrical and Computer Engineering (Electromagnetics antennas and waves, microwave devices and systems)   Samuel Shen, Ph.D. University of Wisconsin, Madison, Albert W. Johnson Distinguished Professor of Mathematics (Statistical Climatology & Agroclimatology, Fluid Dynamcis & Forced Nonlinear Waves)   Nicholas Shikuma, Ph.D. University of California, Santa Cruz, Assistant Professor of Biology (Molecular Mechanisms of Bacteria/Bacteriophage/Animal Interactions)   Usha Sinha, Ph.D. Indian Institute of Science, Bangalore, India, Professor of Physics (Medical and Imaging Physics, Magnetic Resonance Imaging (MRI), and Informatics)

Jeet Sukumaran, Ph.D. University of Kansas, Assistant Professor of Biology (Process-based modeling of macroevolutionary dynamics, diversification, and biogeography/phylogeography; species delimitation; host-parasite coevolution, phylogenetics)   Mauro Tambasco, Ph.D. University of Western Ontario, Associate Professor of Physics (Medical Physics: Biophysics effects of ionizing radiation in the presence of strong magnetic fields)   Naveen Vaidya, Ph.D. York University, Canada, Assistant Professor of Mathematics (Applied Mathematics, Mathematical Biology, Disease Modeling, Differential Equations)   Satchi Venkataraman, Ph.D. University of Florida, Professor of Aerospace Engineering (Structural Mechanics, Design Optimization, Composite Materials, Biomechanics)   Wei Wang, Ph.D. University of Nebraska, Lincoln, Associate Professor of Computer Science (Cyber-Physical Systems, Wireless Multimedia Networking, Breast Cancer Image Processing)

Qi Wang, Ph.D. Johns Hopkins University, Assistant Professor of Aerospace Engineering (Data Assimilation in Turbulent Environments, Adjoint-Based Optimization, Measurement-Enhanced Simulations, Drag Reduction and Optimal Sensor Placement, Pollution Source Localization in Stratified or Non-Stratified Turbulence)   Fridolin Weber, Ph.D. University of Munich, Germany, Albert W. Johnson Distinguished Professor of Physics (Superdense Matter, Astrophysics, General Relativity)   Tao Xie, Ph.D. New Mexico Institute of Mining and Technology, Professor of Computer Science (High-Performance Computing, Energy-Efficient Storage Systems, Parallel/Distributed Systems, and Security-Aware Scheduling)

Yang Xu, Ph.D. Penn State University, Assistant Professor of Computer Science (Cognitive science, computer science, linguistics and psychology)

Ahmad Bani Younes, Ph.D. Texas A&M University, Assistant Professor of Aerospace Engineering (Space research topics: including the development of fast and high fidelity gravity model for the earth anomalies; fast and efficient trajectories propagation for satellite motions; optimal control theory, and, algorithms development for optimization theory, perturbation theory, orbital motion, and very broadly algorithmic differentiation for automatically generating mixed sets of high-order partial derivatives.)

Send Page to Printer

Print this page.

Download Page (PDF)

The PDF will include all information unique to this page.

2024-2025 Catalogue

A PDF of the entire 2024-2025 catalogue.

• Values of Inclusion
• 2020 Antiracism Task Force
• 2022 DEI Report
• Research News
• Department Life
• Listed by Recipient
• Listed by Category
• Oral History of Cornell CS
• CS 40th Anniversary Booklet
• ABC Book for Computer Science at Cornell by David Gries
• Books by Author
• Books Chronologically
• The 60's
• The 70's
• The 80's
• The 90's
• The 00's
• The 2010's
• Faculty Positions: Ithaca
• Faculty Positions: New York City
• Lecturer Position: Ithaca
• Post-doc Position: Ithaca
• Staff/Technical Positions
• Ugrad Course Staff
• Ithaca Info
• Internal info
• Graduation Information
• Cornell Learning Machines Seminar
• Student Colloquium
• Fall 2024 Colloquium
• Conway-Walker Lecture Series
• Salton 2024 Lecture Series
• Fall 2024 Artificial Intelligence Seminar
• Fall 2024 Robotics Seminar
• Fall 2024 Theory Seminar
• Big Red Hacks
• Cornell University - High School Programming Contests 2024
• Game Design Initiative
• CSMore: The Rising Sophomore Summer Program in Computer Science
• Explore CS Research
• ACSU Research Night
• Cornell Junior Theorists' Workshop 2023
• Researchers
• Ph.D. Students
• M.Eng. Students
• M.S. Students
• Ph.D. Alumni
• M.S. Alumni
• List of Courses
• Course and Room Roster
• CS Advanced Standing Exam
• Architecture
• Artificial Intelligence
• Computational Biology
• Database Systems
• Human Interaction
• Machine Learning
• Natural Language Processing
• Programming Languages

Scientific Computing

• Software Engineering
• Systems and Networking
• Theory of Computing
• Contact Academic Advisor
• Your First CS Course
• Technical Electives
• CS with Other Majors/Areas
• Transfer Credits
• CS Honors Program
• CPT for International CS Undergrads
• Graduation Requirements
• Useful Forms
• Becoming a CS Major
• Requirements
• Game Design Minor
• Co-op Program
• Cornell Bowers CIS Undergraduate Research Experience (BURE)
• Independent Research (CS 4999)
• Student Groups
• UGrad Events
• Undergraduate Learning Center
• UGrad Course Staff Info
• The Review Process
• Early M.Eng Credit Approval
• Financial Aid
• Prerequisites
• The Application Process
• The Project
• Pre-approved Electives
• Degree Requirements
• The Course Enrollment Process
• Advising Tips
• Entrepreneurship
• Cornell Tech Programs
• Professional Development
• Contact MEng Office
• Career Success
• Applicant FAQ
• Computer Science Graduate Office Hours
• Exam Scheduling Guidelines
• Graduate TA Handbook
• MS Degree Checklist
• MS Student Financial Support
• Special Committee Selection
• Diversity and Inclusion
• Contact MS Office
• Ph.D. Applicant FAQ
• Graduate Housing
• Non-Degree Application Guidelines
• Ph. D. Visit Day
• Advising Guide for Research Students
• Business Card Policy
• Cornell Tech
• Curricular Practical Training
• A & B Exam Scheduling Guidelines
• Fellowship Opportunities
• Field of Computer Science Ph.D. Student Handbook
• Field A Exam Summary Form
• Graduate School Forms
• Instructor / TA Application
• Ph.D. Requirements
• Ph.D. Student Financial Support
• Travel Funding Opportunities
• Travel Reimbursement Guide
• The Outside Minor Requirement
• CS Graduate Minor
• Outreach Opportunities
• Parental Accommodation Policy
• Special Masters
• Student Spotlights
• Contact PhD Office

Search form

You are here

Faculty and researchers.

• David Bindel
• Giulia Guidi
• Bharath Hariharan
• Volodymyr Kuleshov
• Wei-Chiu Ma
• Curran Muhlberger
• David Shmoys
• Alex Townsend
• Charles Van Loan

Related Areas

Applied mathematics.

Scientists and engineers rely more than ever on computer modeling and simulation to guide their experimental and design work. The infrastructure that supports this activity depends critically on the development of new numerical algorithms that are reliable, efficient, and scalable. "Large N" is the hallmark of modern, data-intensive scientific computing and it is a common thread that unifies departmental research in numerical linear algebra, optimization, and partial differential equations.

David Bindel works on numerical linear algebra, numerical methods for data science, and simulating microelectromechanical systems and fusion plasmas. His research involves software design, mathematical analysis and physical modeling.

Anil Damle  works on the development of fast algorithms in applied and computational mathematics that exploit structure coming from underlying physical or statistical models. This includes work in the areas of computational quantum chemistry, numerical linear algebra, and spectral clustering.

Giulia Guidi  works in the field of high-performance computing for large-scale computational sciences (in particular, computational biology). Her research involves the development of algorithms and software infrastructures on parallel machines to accelerate data processing without sacrificing programming productivity and to make high-performance computing more accessible.

The scientific computing group is also active in the Applied Mathematics Ph.D. program, which is part of Cornell's Center for Applied Mathematics . Prospective Ph.D. applicants interested in the mathematical aspects of scientific computing may wish to consider that graduate field as well.

School of Computational Science and Engineering

College of computing, ph.d. programs.

For those students looking to build a career in computing research, the School of Computational Science and Engineering offers a range of Ph.D. programs that allow students to work alongside some of the most brilliant researchers in the world. For those looking to join the ranks of academia, our doctorate graduates regularly find tenure-track positions in top programs. The School maintains strong research relationships with companies from Fortune 500 to the latest startups that enable  graduates to continue their research in jobs with some of the world’s hottest private-sector employers.

For more information about applying to our programs, please see our Prospective Student FAQs .

Ph.D. in Computational Science and Engineering Interdisciplinary program devoted to the creation, study, and application of computer-based models of natural and engineered systems. 

Ph.D. in Computer Science As a research-oriented degree, the Ph.D. in Computer Science prepares exceptional students for careers at the cutting edge of academia, industry and government. 

Ph.D. in Machine Learning Machine learning builds and learns from both algorithm and theory to understand the world around us and create the tools we need and want.

Ph.D. in Bioinformatics An elite multidisciplinary program sponsored by the College of Computing, the H. Milton Stewart School of Industrial and Systems Engineering, and the School of Mathematics.

Ph.D. in Bioengineering Be a leader through interdisciplinary graduate education and research in areas that improve health and the environment.

• Skip to Content
• Catalog Home
• Institution Home
• Graduate Catalog /
• School of Engineering and Applied Science /

Scientific Computing, MSE

The MSE in Scientific Computing (SCMP) program at Penn provides multifaceted education in the fundamentals and applications of computational science. This education program provides a rigorous computational foundation for applications to a broad range of scientific disciplines. An education in SCMP combines a comprehensive set of core courses centered on numerical methods, algorithm development for high performance computational platforms, and the analysis of large data, and offers flexibility to specialize in different computational science application areas. Students may elect to pursue a thesis in computationally-oriented research within the School of Engineering and Applied Science.

We welcome applications from candidates who have a strong background in physical or theoretical sciences, engineering, math, or computer science. Some experience with computer programming is also strongly recommended.

For more information: https://pics.upenn.edu/masters-science-engineering-scientific-computing/

10 course units are required for the MSE in Scientific Computing.

1. All Methods and   Simulations courses count as  Applications in Natural Science courses.

2. All Applications in Natural Science  courses count as Free Elective  courses.

3.  Applications in Natural Science courses cannot count toward  Methods and  Simulations .

4. Students cannot use Machine Learning courses to count toward the Methods and  Simulations requirements.

The degree and major requirements displayed are intended as a guide for students entering in the Fall of 2024 and later. Students should consult with their academic program regarding final certifications and requirements for graduation.

Print Options

Print this page.

The PDF will include all information unique to this page.

A PDF of the entire 2024-25 catalog.

A PDF of the 2024-25 Undergraduate catalog.

A PDF of the 2024-25 Graduate catalog.

Ph.D. in Scientific Computing

Examples of approved courses for the ph.d. in scientific computing.

The courses listed below are examples of what past students have taken to fulfill the program’s requirements. This is not an exhaustive list and is subject to change . Approval may also depend on your home department. Please consult with the program coordinator at [email protected] to confirm that any course you plan to take is approved, and for which group. Course plans for the Ph.D. in Scientific Computing are individualized for each student, so please confirm that any given course will work. You can email the program coordinator at [email protected] for more approved courses than are listed here.

If you are preparing a list of proposed courses for the Ph.D. in Scientific Computing audit form , please list all computational coursework you’ve taken or plan to take, whether or not it appears on this list.

If you would like to suggest a computational course for approval, please submit it for review .

Course Plan Review

A course being listed on this page does not constitute approval for your individualized course plan. Please consult with the program coordinator at [email protected] to confirm that any course you plan to take is approved, and for which group.

Methodology

Computational Science/Applications

Must be outside of your home department

Virtual Tour

Experience University of Idaho with a virtual tour. Explore now

• Discover a Career
• Find a Major
• Experience U of I Life

More Resources

• Admitted Students
• International Students

Take Action

• Find Financial Aid
• View Deadlines
• Find Your Rep

Helping to ensure U of I is a safe and engaging place for students to learn and be successful. Read about Title IX.

Get Involved

• Clubs & Volunteer Opportunities
• Recreation and Wellbeing
• Student Government
• Student Sustainability Cooperative
• Academic Assistance
• Safety & Security
• Career Services
• Health & Wellness Services
• Register for Classes
• Dates & Deadlines
• Financial Aid
• Sustainable Solutions
• U of I Library

• Upcoming Events

Review the events calendar.

Stay Connected

• Vandal Family Newsletter
• Here We Have Idaho Magazine
• Living on Campus
• Campus Safety
• About Moscow

The largest Vandal Family reunion of the year. Check dates.

Benefits and Services

• Vandal Voyagers Program
• Vandal License Plate
• Submit Class Notes
• Make a Gift
• View Events
• Alumni Chapters
• University Magazine
• Alumni Newsletter

SlateConnect

U of I's web-based retention and advising tool provides an efficient way to guide and support students on their road to graduation. Login to SlateConnect.

Common Tools

• Administrative Procedures Manual (APM)
• Class Schedule
• OIT Tech Support
• Academic Dates & Deadlines
• U of I Retirees Association
• Faculty Senate
• Staff Council

Department of Computer Science

Computer Science

Physical Address: Janssen Engineering Building 236

Computer Science University of Idaho 875 Perimeter Drive MS 1010 Moscow, ID 83844-1010

Phone: 208-885-6592

Fax: 208-885-9052

Email: [email protected]

Web: Computer Science

Student Services

Email: [email protected]

Ph.D. Computer Science

Career information is not specific to degree level. Some career options may require an advanced degree.

Current Job Openings and Salary Range

in ID, WA, OR, MT and HI

Entry-Level

Senior-Level

• Career Options
• Computer and Information Systems Manager
• Computer Science Teacher, Postsecondary

Regional Employment Trends

Employment trends and projected job growth in ID, WA, OR, MT & HI

*Job data is collected from national, state and private sources. For more information, visit EMSI's data sources page .

• Degree Prep

View the Ph.D. Computer Science prerequisites, deadlines and contact information on the U of I Admissions website .

• Most applicants hold a bachelor’s degree in Computer Science or closely related field from an accredited institution and demonstrate a strong background in the fundamentals.
• If your undergraduate degree is not in Computer Science, you may be required to complete background courses to remove deficiencies before beginning the graduate program.

As a prerequisite to enter the Computer Science Ph.D. program, competence in the following areas must be demonstrated: knowledge of a structured, high-level language (CS120); algorithms and data structures (CS121); and a full year of calculus.

If prerequisite requirements are met, a student who does not have an adequate coursework background in computer science may be admitted with deficiencies in computer organization and architecture (CS150); computer languages (CS210); computer operating systems (CS240); software engineering (CS383); analysis of algorithms (CS395); or theory of computation (CS385).

• Degree Roadmap

Depending on your interests, your academic adviser will help you develop a focused plan of study for the Ph.D. Computer Science degree. Some examples include:

• Cyber security
• Survivable systems (fault-tolerant computing)
• Artificial intelligence & machine learning
• Collaborative virtual environments
• Data science

View current Computer Science courses Catalogs are released each year with up-to-date course listings. Students reference the catalog released during their first year of enrollment. For catalog related questions, email [email protected] or call 208-885-6731.

• Degree Requirements
• Students are required to write a dissertation and give a final dissertation defense at the end of their degree program
• Computer Science graduate requirements
• College of Graduate Studies requirements

For questions relating to Computer Science degrees, please email [email protected] or call 208-885-6592.

• Funding Opportunities

The University of Idaho is awarded more than $100 million in annual grants, contracts and research appropriations.

• CyberCorps®: Scholarship for Service – graduate recipients receive a $34,000 stipend. All tuition and degree-related fees are paid through this National Science Foundation grant open to computer science and computer engineering students.
• Stone Scholars Program – awarded annually to support students enrolled in the Computer Science program at the University of Idaho Coeur d’Alene Center.
• Idaho National Laboratory (INL) Graduate Fellowship Program – Recipients of this competitive fellowship receive full tuition and fees by U of I during their first three years of graduate school. INL covers tuition, fees, and a $60,000 annual salary during the final two years of their doctoral research, to be conducted at INL.
• National Nuclear Security Administration (NNSA) Graduate Fellowship Program – These year-long salaried assignments offer hands-on experience in nuclear security and nonproliferation. Administered by Pacific Northwest National Laboratory (PNNL) and open to all engineering disciplines.
• NASA Idaho Space Grant Consortium (ISGC) – $25,000 fellowship programs that contribute to NASA’s mission of exploration and discovery. Open to full-time graduate and doctoral students.
• National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP ) – For outstanding graduate students in NSF-supported science, technology, engineering, and mathematics disciplines who are pursuing research-based master's and doctoral degrees.
• National Defense Science and Engineering Graduate (NDSEG) Fellowship – Three-year fellowship with full coverage of tuition and all mandatory fees, including a monthly stipend and up to $1,000 a year in medical insurance.
• DAAD Research Internships in Science and Engineering (RISE Professional) Programs – RISE Professional offers summer research internships in Germany to Master’s and Ph.D. students at companies and non-university research institutions with strong relations to industry.
• Faculty provide funding through a variety of external agencies and industry partners. Contact our faculty  to learn more about these funding opportunities.

For more funding options, visit the College of Graduate Studies’ funding website .

• Clubs & Organizations

Our college offers 20+ clubs and organizations tied to international and national engineering organizations, including national competition teams.

Learn about clubs related to your major:

• Cyber Defense Club
• CyberForce Competition Team
• Graduate & Professional Student Association (GPSA)
• Humanitarian Engineering Corps (HEC)
• Polymorphic Games Studio
• National Society of Black Engineers (NSBE)
• Robotics Club
• Society of Hispanic Professional Engineers (SHPE)
• Society of Women Engineers (SWE)
• Job Openings and Salary Range
• Employment Trends

Advanced Computer Concepts

As a graduate student in this field, you will gain an in-depth understanding of the limitations and opportunities in the use of computers to solve problems. Work alongside faculty on leading research and explore high-level concepts in computational biology and more to prepare for your career in the field or in academia.

Availability

• No. 1 Best Value Public University in the West – ranked for the fifth year in a row by U.S. News and World Report . We’re also the only public university in Idaho to be ranked best value by Forbes , Money  and The Princeton Review .
• Highest Salary Earnings for early- and mid-career undergraduate degree recipients than any other public university in Idaho – Payscale
• Personalized Attention from nationally and internationally recognized faculty and staff through 1-on-1 interaction, mentorship, advising and research collaboration. All faculty  hold Ph.D.s in their field.

Feature: Rolling With the Beat

13-09-04 band-beesten

Meet the Faculty

View our Faculty

• Ph.D. in Mathematics
• Ph.D. in Atmosphere Ocean Science
• M.S. in Mathematics

M.S. in Scientific Computing

• M.S. in Mathematics in Finance
• Non-Degree Study
• M.S. at Tandon School of Engineering
• Current Students

The departments of mathematics and computer science at NYU's Courant Institute of Mathematical Sciences offer a master's degree in scientific computing. The program provides broad yet rigorous training in areas of mathematics and computer science related to scientific computing. It aims to prepare people with the right talents and background for a technical career doing practical computing.

The program accommodates both full-time and part-time students, with most courses meeting in the evening. The masters program focuses on computational science, which includes modeling and numerical simulation as used in engineering design, development, and optimization. While data science is an increasingly important aspect of computational science, this program is distinct and different from the recently-created  Masters of Science in Data Science  within the  NYU Center for Data Science . Students specifically interested in data science are encouraged to apply to that program instead.

See frequently-asked questions regarding the impact of the Covid-19 pandemic on MS programs.

Scientific Computing: Overview

Scientific computing is an indispensable part of almost all scientific investigation and technological development at universities, government laboratories, and within the private sector. Typically a scientific computing team consists of several people trained in some branch of mathematics, science, statistics, or engineering. What is often lacking is expertise in modern computing tools such as visualization, modern programming paradigms, and high performance computing. The master's program in scientific computing aims to satisfy these needs, without omitting basic training in numerical analysis and computer science. Many graduates of this program work at technologically advanced institutions, especially in research and development, where their skills and experience complement those without interdisciplinary degrees. The program is also open to students who will go on to pursue doctoral studies in computer science, mathematics, or statistics.

The master's program in scientific computing focuses on the mathematics and computer science related to advanced computer modeling and simulation. The program is similar in structure to terminal master's programs in engineering, combining classroom training with practical experience. The coursework ranges from foundational mathematics and fundamental algorithms to such practical topics as data visualization and software tools. Electives encourage the exploration of specific application areas such as mathematical and statistical finance, applications of machine learning, fluid mechanics, finite element methods, and biomedical modeling. The program culminates in a master's project, which serves to integrate the classroom material.

Admission Requirements

The program requires least three semesters of Calculus (including  multivariate calculus ), as well as linear algebra . Experience with  programming in a high-level language  (e.g., Java, C, C++, Fortran, Python) as well as  data structures  and algorithms , equivalent to a first-year sequence in computer science,  is also required. It is highly desirable that applicants have undergraduate major or significant experience in mathematics, a quantitative science or engineering, or economics.

Applicants are strongly recommended to submit their applications by February 15 for Fall semester enrollment. The final application deadline for Fall semester enrollment is May 1. The program admits students both on a full-time and on a part-time basis. The application process takes place online via the Graduate School of Arts and Sciences; please visit the  Graduate School Admissions site .

For more information, please contact us at

[email protected] Phone:  (212) 998-3238 Fax: (212) 995-4121

Degree Requirements

A candidate for a master's degree in scientific computing must complete a number of requirements. .

Computing Facilities

The Courant Institute makes available for graduate training and coursework a network of workstations maintained by systems administrators. All graduate students have computer accounts for the duration of their studies. NYU also runs a high-performance computing center with both shared-memory and distributed-memory computers.

Many members of the departments of mathematics and computer science have research interests bearing on scientific computing. The list includes:

Marsha J. Berger . B.S. 1974, Binghamton; M.S. 1978, Ph.D. 1982, Stanford. Research interests: computational fluid dynamics, adaptive mesh refinement, parallel computing.

Yu Chen . B.S. 1982, Tsinghua; M.S. 1988, Ph.D. 1991, Yale. Research Interests: numerical scattering theory, ill-posed problems, scientific computing.

Aleksandar Donev . B.S. 2001, Michigan State; Ph.D. 2006, Princeton. Research interests: multi-scale methods, fluctuating hydrodynamics, coarse-grained particle methods, jamming and packing.

Davi Geiger . B.S. 1980, Pontifica (Brazil); Ph.D. 1990, MIT. Research interests: computer vision, information theory, medical imaging, and neuroscience.

Jonathan B. Goodman . B.S. 1977, MIT; Ph.D. 1982, Stanford. Research interests: numerical analysis, fluid dynamics, computational physics, partial differential equations.

Leslie Greengard . B.A. 1979, Wesleyan; M.S. 1987, Yale School of Medicine; Ph.D. 1987, Yale. Research interests: scientific computing, fast algorithms, potential theory.

Yann LeCun . B.S. 1983, ESIEE (Paris); D.E.A. 1984, Ph.D. 1987, Pierre and Marie Curie University (Paris). Research interests: machine learning.

Andrew Majda . B.S. 1970, M.S. 1971, Ph.D. 1973, Stanford. Research interests: modern applied mathematics, atmosphere/ocean science, turbulence, statistical physics.

Bhubaneswar Mishra . B.S. 1980, India Institute of Technology, Kharagpur; M.S. 1982, Ph.D. 1985, Carnegie-Mellon. Research interests: robotics, mathematical and theoretical computer science.

Michael L. Overton . B.S. 1974, British Columbia; M.S. 1977, Ph.D. 1979, Stanford. Research interests: numerical linear algebra, optimization, linear and semidefinite programming.

Kenneth Perlin . B.A. 1979, Harvard; M.S. 1984, Ph.D. 1986, NYU. Research interests: computer graphics, simulation, computer-human interfaces, multimedia.

Charles S. Peskin .  B.A. 1968, Harvard; Ph.D. 1972, Yeshiva. Research interests: physiology, fluid dynamics, numerical methods.

Aaditya V. Rangan . B.A. 1999, Dartmouth; Ph.D. 2003, Berkeley. Research interests: large-scale scientific modeling of physical, biological, and neurobiological phenomena.

Tamar Schlick . B.S. 1982, Wayne State; M.S. 1984, Ph.D. 1987, NYU. Research interests: mathematical biology, numerical analysis, computational chemistry.

Michael J. Shelley . B.S. 1981, Colorado; M.S. 1984, Ph.D. 1985, Arizona. Research interests: scientific computation, fluid dynamics, neuroscience.

Eero Simoncelli . B.A. 1984, Harvard; M.S. 1988, Ph.D. 1993, MIT. Research interests: image processing, computational neuroscience, computer vision.

Esteban Tabak . Bach. 1988, Buenos Aires; Ph.D. 1992, MIT. Research interests: fluid dynamics, conservation laws, optimization and data analysis.

Olof B. Widlund . C.E. 1960, Tekn. L. 1964, Technology Institute, Stockholm; Ph.D. 1966, Uppsala. Research interests: numerical analysis, partial differential equations, parallel computing.

Margaret H. Wright . B.S. 1964, M.S. 1965, Ph.D. 1976, Stanford. Research interests: mathematical optimization, numerical methods, nonlinear programming.

Denis Zorin . B.S. 1991, Moscow Institute of Physics and Technology; M.S. 1993, Ohio State; Ph.D. 1997, Caltech. Research interests: computer graphics, geometric modeling, subdivision surfaces, multiresolution surface representations, perceptually based methods for computer graphics.

Miranda Holmes-Cerfon . B.S. 2005 University of British Columbia, PhD 2010 NYU. Research interests: soft-matter physics, fluid dynamics, oceanography, stochastic methods. 

Antoine Cerfon . B.S. 2003, M.S. 2005 Ecole des Mines de Paris, PhD 2010 MIT. Research interests: Computational plasma physics, multi-scale methods, fast algorithms.

Dimitris GIannakis . MSci 2001 Cambridge, PhD 2009 Chicago. Research interests: geometrical data analysis, statistical modeling, climate dynamics.

Academic Standards

To register for courses, students must maintain good academic standing, fulfilling the following requirements:

• Students must maintain an average of B or better over their first twelve credits. Students who fail to achieve this cannot continue in the master's program.
• Students cannot obtain a master's degree unless they have maintained an overall average of B or better. Students at risk of failing to meet this requirement receive a warning letter from the department.
• Students cannot obtain more than four no-credit grades, withdrawals, or unresolved incomplete grades during their academic tenure, and no more than two such grades in the first six courses for which they have registered.

Up to two core courses taken elsewhere can earn transfer credit, subject to the normal NYU graduate school restrictions on transfer of credit and the approval of the program director. At least 30 credits must be taken at NYU. 

For further  administrative  information (including applications, transfer of credits, entrance exams, registration for courses, etc.) please contact:

Carly Gubitz [email protected]

For further  academic  information (e.g., substituting a course) please contact:

Jonathan B. Goodman Director of the Master's Program in Scientific Computing [email protected]

Revised Fall 2016

Cybersecurity Guide

From scholar to expert: Cybersecurity PhD options

In this guide

• Industry demand
• 2024 rankings
• Preparation
• Considerations
• School listings

The cybersecurity landscape is not just growing—it’s evolving at a breakneck pace. And what better way to stay ahead of the curve than by pursuing a PhD in cybersecurity?

This advanced degree is no longer confined to the realm of computer science. Today, it branches into diverse fields like law, policy, management, and strategy, reflecting the multifaceted nature of modern cyber threats.

If you’re looking to become a thought leader in this dynamic industry, a PhD in cybersecurity offers an unparalleled opportunity to deepen your expertise and broaden your horizons.

This guide is designed to give prospective cybersecurity PhD students a general overview of available cybersecurity PhD programs. It will also outline some of the factors to consider when trying to find the right PhD program fit, such as course requirements and tuition costs. 

Industry demand for PhDs in cybersecurity

Like other cutting-edge technology fields, until recently, cybersecurity PhD programs were often training grounds for niche positions and specialized research, often for government agencies (like the CIA, NSA, and FBI),  or closely adjacent research organizations or institutions. 

Today, however, as the cybersecurity field grows to become more pervasive and consumer-oriented, there are opportunities for cybersecurity PhDs to work at public-facing companies like startups and name-brand financial, software, infrastructure, and digital service firms.

One trend that is emerging in the cybersecurity field is that cybersecurity experts need to be well-versed in a variety of growing threats. If recent headlines about cybersecurity breaches are any indication, there are a number of new attack vectors and opportunities for cybercrime and related issues. Historically, committing cybercrime took resources and a level of sophistication that required specialized training or skill.

But now, because of the pervasiveness of the internet, committing cybercrime is becoming more commonplace. So training in a cybersecurity PhD program allows students to become an experts in one part of a growing and multi-layered field.

In fact, this trend of needing well-trained, but adaptable cybersecurity professionals is reflected by the move by cybersecurity graduate schools to offer specialized master’s degrees , and many companies and professional organizations offer certifications in cybersecurity that focus on particular issues related to cybersecurity technology, cybersecurity law , digital forensics , policy, or related topics.

That said, traditional research-oriented cybersecurity positions continue to be in demand in academia and elsewhere — a trend that will likely continue. 

One interesting facet of the cybersecurity field is trying to predict what future cybersecurity threats might look like and then develop tools and systems to protect against those threats.

As new technologies and services are developed and as more of the global population begins using Internet services for everything from healthcare to banking — new ways of protecting those services will be required. Often, it’s up to academic researchers to think ahead and examine various threats and opportunities to insulate against those threats.

Another key trend coming out of academic circles is that cybersecurity students are becoming increasingly multidisciplinary.

As cybersecurity hacks impact more parts of people’s everyday lives, so too do the academic programs that are designed to prepare the next generation of cybersecurity professionals. This emerging trend creates an enormous amount of opportunity for students who have a variety of interests and who are looking to create a non-traditional career path.

The best cybersecurity PhD programs for 2024

Georgia institute of technology, northeastern university, marymount university, school of technology and innovation, nova southeastern university, college of computing & engineering, purdue university, stevens institute of technology, worcester polytechnic institute, university of illinois at urbana-champaign, mississippi state university, new york institute of technology.

These rankings were compiled from data accessed in November 2023 from the Integrated Post-Secondary Education Data System (IPEDS) and College Navigator (both services National Center for Education Statistics). Tuition data was pulled from individual university websites and is current as of November 2023.

What is required to get a PhD in cybersecurity?

Good news first: Obtaining a PhD in a field related to cybersecurity will likely create tremendous employment opportunities and lead to interesting and dynamic career options.

Bad news: Getting a PhD requires a lot of investment of time and energy, and comes with a big opportunity cost (meaning you have to invest four to five years, or longer, or pursue other opportunities to obtain a doctoral degree. 

Here’s a quick breakdown of what is required to get a PhD in cybersecurity. Of course, specific degree requirements will vary by program. One growing trend in the field is that students can now obtain degrees in a variety of formats, including traditional on-campus programs, online degree programs , and hybrid graduate degree programs that combine both on-campus learning with online learning. 

Related resources

• Online PhD in cybersecurity – A guide to finding the right program
• Cybersecurity degree programs
• Podcast episodes and expert interviews

Preparing for a cybersecurity doctorate program

Cybersecurity is a relatively new formalized technology field, nonetheless, there are several ways that students or prospective PhD candidates can get involved or explore the field before and during a graduate school program. A few examples of ways to start networking and finding opportunities include: 

Join cybersecurity organizations with professional networks

Specialized professional organizations are a good place to find the latest in career advice and guidance. Often they publish newsletters or other kinds of information that provide insights into the emerging trends and issues facing cybersecurity professionals. A couple of examples include:

The Center for Internet Security  (CIS) is a non-profit dedicated to training cybersecurity professionals and fostering a sense of collaboration. The organization also publishes information and analysis of the latest cybersecurity threats and issues facing the professional community.

The SANS Institute runs several different kinds of courses for students (including certification programs) as well as ongoing professional cybersecurity education and training for people working in the field. The organization has several options including webinars, online training, and live in-person seminars. Additionally, SANS also publishes newsletters and maintains forums for cybersecurity professionals to interact and share information.

Leverage your social network

Places like LinkedIn and Twitter are good places to start to find news and information about what is happening in the field, who the main leaders and influencers are, and what kinds of jobs and opportunities are available.

Starting a professional network early is also a great opportunity. Often professionals and members of the industry are willing to provide guidance and help to students who are genuinely interested in the field and looking for career opportunities. 

Cybersecurity competitions 

Cybersecurity competitions are a great way to get hands-on experience working on real cybersecurity problems and issues. As a PhD student or prospective student, cybersecurity competitions that are sponsored by industry groups are a great way to meet other cybersecurity professionals while getting working on projects that will help flesh out a resume or become talking points in later job interviews.

The US Cyber Challenge , for example, is a series of competitions and hackathon-style events hosted by the Department of Homeland Security Science and Technology Directorate and the Center for Internet Security to prepare the next generation of cybersecurity professionals.

Internships

Internships also continue to be a tried and true way to gain professional experience. Internships in technical fields like cybersecurity can also pay well. Like the industry itself, cybersecurity internships are available across a wide range of industries and can range from academic research-oriented to more corporate kinds of work. 

Things to consider when choosing a cybersecurity PhD program

There are many considerations to evaluate when considering any kind of graduate degree, but proper planning is essential to be able to obtain a doctoral degree. It’s also important to note that these are just guidelines and that each graduate program will have specific requirements, so be sure to double-check.

What you will need before applying to a cybersecurity PhD program:

• All undergraduate and graduate transcripts
• A statement of intent, which is like a cover letter outlining interest
• Letters of reference
• Application fee
• Online application
• A resume or CV outlining professional and academic accomplishments

What does a cybersecurity PhD program cost?

Obtaining a PhD is a massive investment, both in terms of time and money. Cybersecurity PhD students are weighing the cost of becoming an expert in the field with the payoff of having interesting and potentially lucrative career opportunities on the other side.

Degree requirements are usually satisfied in 60-75 hours, so the cost of a doctoral degree can be well into the six-figure range. Here’s a more specific breakdown:

Tuition rates

The Cybersecurity Guide research team looked at 26 programs that offer a cybersecurity-related PhD degree. Here’s a breakdown of tuition rates (all figures are based on out-of-state tuition).

$17,580 is the most affordable PhD program option and it is available at the Georgia Institute of Technology.

$86,833 is the average cost of a cybersecurity PhD and is based on tuition rates from all 26 schools.

$197,820 is the most expensive cybersecurity PhD program and is available at Indiana University Bloomington.

The good news is that by the time students get to the PhD level there are a lot of funding options — including some graduate programs that are completely funded by the university or academic departments themselves.

Additionally, funding in the form of research grants and other kinds of scholarships is available for students interested in pursuing cybersecurity studies. 

One example is the CyberCorps: Scholarships for Service program. Administered by the National Science Foundation, PhD students studying cybersecurity are eligible for a $34,000 a year scholarship, along with a professional stipend of $6,000 to attend conferences in exchange for agreeing to work for a government agency in the cybersecurity space after the PhD program. 

Frequently asked questions about cybersecurity PhD programs

Most traditional and online cybersecurity graduate programs require a minimum number of credits that need to be completed to obtain a degree. On average, it takes 71 credits to graduate with a PhD in cybersecurity — far longer (almost double) than traditional master’s degree programs. In addition to coursework, most PhD students also have research and teaching responsibilities that can be simultaneously demanding and great career preparation.

At the core of a cybersecurity doctoral program is a data science doctoral program, you’ll be expected to learn many skills and also how to apply them across domains and disciplines. Core curriculums will vary from program to program, but almost all will have a core foundation of statistics.  

All PhD candidates will have to take a series of exams that act as checkpoints during the lengthy PhD process. The actual exam process and timing can vary depending on the university and the program, but the basic idea is that cybersecurity PhD candidates generally have to sit for a qualifying exam, which comes earlier in the program (usually the winter or spring of the second year of study), a preliminary exam, which a candidate takes to show they are ready to start the dissertation or research portion of the PhD program, and a final exam where PhD students present and defend their research and complete their degree requirements. 

A cybersecurity PhD dissertation is the capstone of a doctoral program. The dissertation is the name of a formal paper that presents the findings of original research that the PhD candidate conducted during the program under the guidance of faculty advisors. Some example cybersecurity research topics that could potentially be turned into dissertation ideas include: * Policies and best practices around passwords * Ways to defend against the rise of bots * Policies around encryption and privacy * Corporate responsibility for employee security * Internet advertising targeting and privacy * The new frontier of social engineering attacks * Operation security (OpSec) strategy and policy * Network infrastructure and defense * Cybersecurity law and policy * The vulnerabilities of biometrics * The role of ethical hacking * Cybersecurity forensics and enforcement

A complete listing of cybersecurity PhD programs

The following is a list of cybersecurity PhD programs. The listing is intended to work as a high-level index that provides enough basic information to make quick side-by-side comparisons easy. 

You should find basic data about what each school requires (such as a GRE score or prior academic work) as well as the number of credits required, estimated costs, and a link to the program.

Arizona State University

Augusta university, boise state university, carnegie mellon university, colorado school of mines, dakota state university, george mason university, indiana university bloomington, iowa state university, louisiana tech university, marymount university, naval postgraduate school, new jersey city university, new york university, nova southeastern university, rochester institute of technology, sam houston state university, st. thomas university, the university of tennessee, university of california-davis, university of central florida, university of colorado-colorado springs, university of fairfax, university of idaho, university of missouri-columbia, university of north carolina at charlotte, university of north texas, university of texas at san antonio, university of tulsa, virginia tech.