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Computational Science and Engineering PhD

77 Massachusetts Avenue Building 35-434B Cambridge MA, 02139

617-253-3725 [email protected]

Website: Computational Science and Engineering PhD

Application Opens: September 15

Deadline: December 1 at 11:59 PM Eastern Time

Fee: $75.00

Note: Applicants interested in Computer Science must apply to through the Electrical Engineering and Computer Science PhD program .

Terms of Enrollment

Fall Term (September)

Standalone Program:

• Doctor of Philosophy (PhD) in Computational Science and Engineering

Joint Program:

• Doctor of Philosophy (PhD) in Civil Engineering and Computation
• Doctor of Philosophy (PhD) in Environmental Engineering and Computation
• Doctor of Philosophy (PhD) in Mechanical Engineering and Computation
• Doctor of Philosophy (PhD) in Computational Materials Science and Engineering
• Doctor of Philosophy (PhD) in Chemical Engineering and Computation
• Doctor of Philosophy (PhD) in Computational Earth, Atmospheric and Planetary Sciences
• Doctor of Philosophy (PhD) in Aerospace Engineering and Computational Science
• Doctor of Philosophy (PhD) in Mathematics and Computational Science
• Doctor of Philosophy (PhD) in Nuclear Engineering and Computation
• Doctor of Philosophy (PhD) in Computational Nuclear Science and Engineering

Affiliated Departments

• Aeronautics and Astronautics
• Chemical Engineering
• Civil and Environmental Engineering
• Earth, Atmospheric, and Planetary Studies
• Materials Science and Engineering
• Mathematics
• Mechanical Engineering
• Nuclear Science and Engineering

Standardized Tests

Graduate Record Examination (GRE)

• General test not required for Fall 2024 admission cycle
• Institute code: 3514
• Department code: 0000

International English Language Testing System (IELTS)

• Minimum score required: 7
• Electronic scores send to: MIT Graduate Admissions

TOEFL exam may be accepted in special cases. Waivers are not offered.

Financial Support

The CCSE PhD is an interdisciplinary program that collaborates with eight affiliated departments. As financial support may vary by department, CCSE graduate students are encouraged to contact their home department for more information.

Application Requirements

• Online application (including Subjects Taken section)
• Statement of objectives (limited to one page)
• Three letters of recommendation
• Transcripts
• English proficiency exam scores
• CV or resume
• GRE scores (not required for Fall 2023 admission cycle)

Special Instructions

The Computational Science and Engineering (CSE) PhD program allows students to specialize at the doctoral level in a computation-related field of their choice through focused coursework and a doctoral thesis. Applications from candidates who have a strong foundation in core disciplinary areas of mathematics, engineering, physics, or related fields are strongly encouraged.

Applicants interested in Computer Science: Please explore the offerings of the  Department of Electrical Engineering and Computer Science.

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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

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

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• Departmental History
• Departmental Organization
• Visiting Committee
• Faculty AI+D
• Faculty Emeritus

Hal Abelson

Class of 1922 Professor, [CS and AI+D]

• [email protected]
• (617) 253-5856
• Office: 32-G516

Artificial Intelligence + Decision making

Artificial Intelligence + Machine Learning

Anant Agarwal

CEO, edX; Professor of EECS; [CS and EE]

• [email protected]
• (617) 253-1448
• Office: NE55-900

Multicore Processors & Cloud Computing

Pulkit Agrawal

Assistant Professor. [AI+D and CS]

• [email protected]
• (617) 253-5851
• Office: 45-641H

Mohammad Alizadeh

Associate Professor, [CS and AI+D]; Industry Officer; Director, 6-A MEng Thesis Program

• [email protected]
• (617) 253-6042
• Office: 32-G920

Saman Amarasinghe

Professor of EECS, [CS]

• [email protected]
• (617) 253-8879
• Office: 38-427

Faculty Head, CS; Charles W. And Jennifer C. Johnson Professor in Electrical Engineering and Computer Science, [CS and EE]

• [email protected]
• (617) 253-6090
• Office: 32-G866

Hari Balakrishnan

Fujitsu Professor in Electrical Engineering and Computer Science, [CS and AI+D]

• [email protected]
• (617) 253-8713
• Office: 32-G940

Wireless Networks & Mobile Computing

Associate Professor, [CS]

• [email protected]
• (617) 253-0004
• Office: 32-G996

Robert Berwick

Professor of CS and Engineering and Computational Linguistics, [AI+D and CS]

• [email protected]
• (617) 253-8918
• Office: 32-D728

Michael Carbin

Associate Professor, [CS and AI+D]

• [email protected]
• (617) 253-5881
• Office: 32-G782

Anantha Chandrakasan

Dean, MIT School of Engineering; Chief Innovation and Strategy Officer, MIT; Vannevar Bush Professor, [EE and CS]

• [email protected]
• (617) 258-0818
• Office: 1-206

Brynmor Chapman

Lecturer, [CS]

• [email protected]

Adam Chlipala

Professor, [CS]

• [email protected]
• (617) 324-8439
• Office: 32-G842

Isaac Chuang

Professor of EECS, [AI+D and EE, CS]

• [email protected]
• (617) 253-1692
• Office: 26-251

Henry Corrigan-Gibbs

Douglas Ross (1954) Career Development Professor of Software Technology; Assistant Professor, [CS]

• [email protected]
• 617-253-0301
• Office: 32-G970a

Constantinos Daskalakis

Armen Avanessians (1982) Professor, [AI+D and CS]

• [email protected]
• (617) 253-9643
• Office: 32-G694

Christina Delimitrou

KDD Career Development Professor in Communications and Technology; Associate Professor, [CS]

• [email protected]
• Office: 32-G738

Computer Science and Artificial Intelligence Laboratory (CSAIL)

Cybersecurity

Erik Demaine

• [email protected]
• (617) 253-6871
• Office: 32-G680

Srini Devadas

Edwin Sibley Webster Professor, [CS]

• [email protected]
• (617) 253-0454
• Office: 32-G844

Frederic Durand

Amar Bose Professor of Computing, [AI+D and CS]

• [email protected]
• (617) 253-7223
• Office: 32-D426

Professor of the Practice in EECS, [CS and EE]

• [email protected]
• (617) 258-9190
• Office: 32-868

Mohsen Ghaffari

Steven and Renee Finn Career Development Professor; Associate Professor, [CS]

• [email protected]
• (617) 253-4591
• Office: 32-G618

Manya Ghobadi

• [email protected]
• 617-253-5851

David Gifford

Professor of CS and Engineering (Post-Tenure), [AI+D and CS]

• [email protected]
• (617) 253-6039
• Office: 32-G542

Shafrira Goldwasser

RSA Professor (Post-Tenure) , [CS]

• [email protected]
• (617) 253-5914
• Office: 32-G682

W. Eric L Grimson

Chancellor for Academic Advancement; Interim Vice President for Open Learning; Bernard M. Gordon Professor in Medical Engineering; Professor of Computer Science and Engineering, [CS and AI+D]

• [email protected]
• 617-253-4645
• Office: 3-221

John Guttag

Dugald C. Jackson Professor in Electrical Engineering, [CS]

• [email protected]
• (617) 253-6022
• Office: 32-G966

Associate Professor, [EE and CS]

• [email protected]
• (617) 253-0086
• Office: 38-344

Sam Hopkins

Jamieson Career Development Professor in Electrical Engineering and Computer Science; Assistant Professor, [CS and AI+D]

• [email protected]
• Office: 32-G666

Daniel Huttenlocher

Dean, MIT Stephen A. Schwarzman College of Computing; Henry Ellis Warren (1894) Professor, [CS and AI+D]

• [email protected]
• (617) 253-5388
• Office: 4-212

Piotr Indyk

Thomas D. and Virginia W. Cabot Professor, [CS and AI+D]

• [email protected]
• (617) 452-3402
• Office: 32-G642

Daniel Jackson

Professor of CS and Engineering, [CS]

• [email protected]
• (617) 258-8471
• Office: 32-G704

M. Frans Kaashoek

Charles A. Piper (1935) Professor, [CS]

• [email protected]
• (617) 253-7149
• Office: 32-G992

David Karger

• [email protected]
• (617) 258-6167
• Office: 32-G592

Dina Katabi

Thuan (1990) and Nicole Pham Professor, [CS and AI+D]

• [email protected]
• (617) 324-6027
• Office: 32-G936

• [email protected]
• Office: 24-316

Manolis Kellis

Professor of CS, [AI+D and CS]

• [email protected]
• (617) 253-2419
• Office: 32-G564

• [email protected]
• (510) 926-5856
• Office: 32-G914

Butler Lampson

Adjunct Professor of CS and Engineering

• [email protected]
• (425) 703-5925
• Office: 32-G924

Steven Leeb

Emanuel E. Landsman (1958) Professor, [EE and CS]

• [email protected]
• (617) 253-9360
• Office: 10-069

Charles Leiserson

Edwin Sibley Webster Professor; [CS]

• [email protected]
• (617) 253-5833
• Office: 32-G768

Barbara Liskov

Insitute Professor (post tenure)

• [email protected]
• (617) 253-5886
• Office: 32-G942

Assistant professor, [CS]

• [email protected]
• Office: 32-D632

Nancy Lynch

NEC Professor of Software Science and Engineering (Post-Tenure), [CS]

• [email protected]
• (617) 253-7225
• Office: 32-G668

Samuel Madden

Distinguished College of Computing Professor, [CS and AI+D]

• [email protected]
• (617) 258-6643
• Office: 32-G938

Aleksander Mądry

Cadence Design Systems Professor, [AI+D and CS]

• [email protected]
• (617) 324-6739
• Office: 32-G806

Wojciech Matusik

Joan and Irwin M. (1957) Jacobs Professor, [CS and AI+D]

• [email protected]
• (617) 324-8432

Muriel Médard

NEC Professor of Software Science and Engineering, [EE and CS, AI + D]

• [email protected]
• (617) 253-3167
• Office: 36-512

Silvio Micali

Ford Foundation Professor of Engineering (Post-Tenure) , [CS]

• [email protected]
• (617) 253-5949
• Office: 32-G644

Robert Miller

Education Officer for Computer Science, Distinguished Professor in EECS, [CS]

• [email protected]
• (617) 324-6028
• Office: 32-G718

Robert Morris

• [email protected]
• (617) 253-5983
• Office: 32-G972

Stefanie Mueller

TIBCO Founders Professor; TIBCO Founders Researcher; Associate Professor [CS and EE]

• [email protected]
• Office: 32-211

Anand V Natarajan

ITT Career Development Professor in Computer Technology; Assistant Professor, [CS]

• [email protected]
• 408-910-4116

Alan Oppenheim

Ford Professor of Engineering (Post-Tenure), [EE and CS, AI+D]

• [email protected]
• (617) 253-4177
• Office: 36-615D

Asu Ozdaglar

EECS Department Head; MIT Schwarzman College of Computing Deputy Dean of Academics; MathWorks Professor, [AI+D and EE, CS]

• [email protected]
• (617) 253-4607
• Office: 38-403

Jonathan Ragan-Kelley

• [email protected]
• 801-913-0101
• Office: 32-D462

Manish Raghavan

Drew Houston (2005) Professorship; Assistant Professor / Shared Appointment in Sloan School of Management, [CS]

• [email protected]
• (617) 715-5368‬
• Office: E62-459

Srini Raghuraman

• [email protected]
• Office: 38-688

Martin Rinard

Professor of CS and Engineering, [CS and AI+D]

• [email protected]
• (617) 258-6922
• Office: 32-G744

Ronald Rivest

Institute Professor (Post-Tenure); Professor Post-Tenure of Computer Science and Engineering, [CS]

• [email protected]
• (617) 253-5880
• Office: 32-G692

Ronitt Rubinfeld

Edwin Sibley Webster Professor, [CS and AI+D]

• [email protected]
• (617) 253-0884
• Office: 32-G69832-G698

Daniela Rus

Director, CSAIL; MIT Schwarzman College of Computing Deputy Dean of Research; Andrew (1956) and Erna Viterbi Professor, [AI+D and CS]

• [email protected]
• (617) 258-7567
• Office: 32-374

Daniel Sanchez

Professor of EECS, [CS and EE]

• [email protected]
• (617) 715-4886
• Office: 32-G838

Arvind Satyanarayan

• [email protected]
• (617) 258-0697
• Office: 32-G706

• [email protected]
• (617) 324-8440
• Office: 32-G622

Julian Shun

• [email protected]
• (617) 258-0669
• Office: 32G-736

Armando Solar-Lezama

Distinguished Professor of Computing, MIT Schwarzman College of Computing; Professor of EECS, [CS and AI+D]

• [email protected]
• (617) 324-8356
• Office: 32-G840

Justin Solomon

Associate Professor of EECS, [AI+D and CS]

• [email protected]
• (617) 324-6738
• Office: 32-D460

Michael Stonebraker

Adjunct Professor of CS and Engineering, [CS]

• [email protected]
• (617) 253-3538
• Office: 32-G916

Gerald Sussman

Panasonic Professor, [CS and EE, AI+D]

• [email protected]
• (617) 253-5874
• Office: 32-385

Vivienne Sze

Associate Professor of EECS, [EE and CS, AI+D]

• [email protected]
• 617.324.7352
• Office: 38-260

Peter Szolovits

Professor of Computer Science and Engineering; [AI+D and CS]

• [email protected]
• (617) 253-3476
• Office: 32-254

Vinod Vaikuntanathan

Professor, [CS and AI+D]

• [email protected]
• (617) 324-8444
• Office: 32-G696

Andrew Wang

• [email protected]
• Office: 38-648

Ryan Williams

Professor of EECS, [CS and AI+D]

• [email protected]
• Office: 32-G638

Virginia Vassilevska Williams

• [email protected]
• Office: 32-G640

Mengjia Yan

Assistant Professor, [CS]

• [email protected]
• 617-258-0719
• Office: 32G-840

Nickolai Zeldovich

Joan and Irwin M. (1957) Jacobs Professor, [CS]

• [email protected]
• (617) 253-6005
• Office: 32-G994

Delta Electronics Professor of EECS (Post-Tenure), [AI+D and CS]

• [email protected]
• (617) 253-8513
• Office: 32-G470

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3 Questions: A new PhD program from the Center for Computational Science and Engineering

Co-directors Youssef Marzouk and Nicolas Hadjiconstantinou describe how the standalone degree aims to train students in cross-cutting aspects of computational science and engineering.

This fall, the Center for Computational Science and Engineering (CCSE), an academic unit in the MIT Schwarzman College of Computing, is introducing a new standalone PhD degree program that will enable students to pursue research in cross-cutting methodological aspects of computational science and engineering. The launch follows approval of the center’s degree program proposal at the May 2023 Institute faculty meeting.

Doctoral-level graduate study in computational science and engineering (CSE) at MIT has, for the past decade, been offered through an interdisciplinary program in which CSE students are admitted to one of eight participating academic departments in the School of Engineering or School of Science. While this model adds a strong disciplinary component to students’ education, the rapid growth of the CSE field and the establishment of the MIT Schwarzman College of Computing have prompted an exciting expansion of MIT’s graduate-level offerings in computation.

The new degree, offered by the college, will run alongside MIT’s existing interdisciplinary offerings in CSE, complementing these doctoral training programs and preparing students to contribute to the leading edge of the field. Here, CCSE co-directors Youssef Marzouk and Nicolas Hadjiconstantinou discuss the standalone program and how they expect it to elevate the visibility and impact of CSE research and education at MIT.

Q: What is computational science and engineering?

Marzouk: Computational science and engineering focuses on the development and analysis of state-of-the-art methods for computation and their innovative application to problems of science and engineering interest. It has intellectual foundations in applied mathematics, statistics, and computer science, and touches the full range of science and engineering disciplines. Yet, it synthesizes these foundations into a discipline of its own — one that links the digital and physical worlds. It’s an exciting and evolving multidisciplinary field.

Hadjiconstantinou: Examples of CSE research happening at MIT include modeling and simulation techniques, the underlying computational mathematics, and data-driven modeling of physical systems. Computational statistics and scientific machine learning have become prominent threads within CSE, joining high-performance computing, mathematically-oriented programming languages, and their broader links to algorithms and software. Application domains include energy, environment and climate, materials, health, transportation, autonomy, and aerospace, among others. Some of our researchers focus on general and widely applicable methodology, while others choose to focus on methods and algorithms motivated by a specific domain of application.

Q: What was the motivation behind creating a standalone PhD program?

Marzouk: The new degree focuses on a particular class of students whose background and interests are primarily in CSE methodology, in a manner that cuts across the disciplinary research structure represented by our current “with-departments” degree program. There is a strong research demand for such methodologically-focused students among CCSE faculty and MIT faculty in general. Our objective is to create a targeted, coherent degree program in this field that, alongside our other thriving CSE offerings, will create the leading environment for top CSE students worldwide.

Hadjiconstantinou: One of CCSE’s most important functions is to recruit exceptional students who are trained in and want to work in computational science and engineering. Experience with our CSE master’s program suggests that students with a strong background and interests in the discipline prefer to apply to a pure CSE program for their graduate studies. The standalone degree aims to bring these students to MIT and make them available to faculty across the Institute.

Q: How will this impact computing education and research at MIT?  

Hadjiconstantinou: We believe that offering a standalone PhD program in CSE alongside the existing “with-departments” programs will significantly strengthen MIT’s graduate programs in computing. In particular, it will strengthen the methodological core of CSE research and education at MIT, while continuing to support the disciplinary-flavored CSE work taking place in our participating departments, which include Aeronautics and Astronautics; Chemical Engineering; Civil and Environmental Engineering; Materials Science and Engineering; Mechanical Engineering; Nuclear Science and Engineering; Earth, Atmospheric and Planetary Sciences; and Mathematics. Together, these programs will create a stronger CSE student cohort and facilitate deeper exchanges between the college and other units at MIT.

Marzouk: In a broader sense, the new program is designed to help realize one of the key opportunities presented by the college, which is to create a richer variety of graduate degrees in computation and to involve as many faculty and units in these educational endeavors as possible. The standalone CSE PhD will join other distinguished doctoral programs of the college — such as the Department of Electrical Engineering and Computer Science PhD; the Operations Research Center PhD; and the Interdisciplinary Doctoral Program in Statistics and the Social and Engineering Systems PhD within the Institute for Data, Systems, and Society — and grow in a way that is informed by them. The confluence of these academic programs, and natural synergies among them, will make MIT quite unique.

Related Stories

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

• Dept-CSE PhD Program of Study Form   (version date 05Feb2024)
• Checklist for Dept-CSE PhD Students (version date 05Sep2023)

Dept-CSE PhD Participating Departments

The interdisciplinary doctoral program in Computational Science and Engineering ( CSE PhD + Engineering or Science ) at MIT allows enrolled students to specialize at the doctoral level in a computation-related field of their choice through focused coursework and a doctoral thesis. This program is offered through a number of participating departments, namely

• Civil and Environmental Engineering (Course 1) ,
• Mechanical Engineering (Course 2) ,
• Materials Science and Engineering (Course 3) ,
• Chemical Engineering (Course 10) ,
• Earth, Atmospheric and Planetary Sciences (Course 12) ,
• Aeronautics and Astronautics (Course 16) ,
• Mathematics (Course 18) ,
• Nuclear Science & Engineering (Course 22) .

Program Outline

Once admitted, doctoral degree candidates are expected to complete the host department’s degree requirements (including qualifying exam) with CSE deviations relating to coursework, thesis committee composition and thesis submission that are specific to the Dept-CSE program and are discussed in more detail below.

Academic Performance

Dept-CSE PhD students are required to complete at least five graduate-level subjects, totaling no less than 60 credit units, in computational science and engineering selected from the approved list of Computational Concentration Subjects . Dept-CSE PhD students may not use more than 12 units of credit from a “meets with undergraduate” subject to fulfill the CSE curriculum requirement. Subjects taken with the graduate P/D/F grading option, or subjects specifically designated as P/D/F in the MIT Bulletin, cannot be used to satisfy the Dept-CSE PhD curricular requirement of five graduate-level subjects, totaling no less than 60 credit units, in computational science and engineering*.

In addition to departmental academic performance expectations, Dept-CSE students are expected to maintain a grade point average (GPA) of at least 4.5 (out of 5) in CSE subjects and an overall GPA of at least 4.2 (out of 5) during the course of their studies.

*ChemE-CSE students are required to complete at least four subjects in computational science and engineering, in addition to 10.34, for a total of no less than 57 credit units.

Department of Civil and Environmental Engineering

A complete description of the doctoral program in Civil and Environmental Engineering can be found at https://cee.mit.edu/resources/ . Deviations associated with the CEE-CSE degree (“1.CSD”) are as follows.

Coursework Requirements

The CEE-CSE doctoral program of study consists of at least five graduate-level subjects in computational science and engineering selected from the approved list of Computational Concentration Subjects . Subjects taken as part of an MIT SM degree can be counted toward this requirement. Doctoral candidates are normally expected to take their major subjects at the Institute. The specific subjects will depend on the student’s thesis topic and background, and will be approved by their thesis committee.

Thesis Committee Composition

The thesis committee composition requirements are identical to those of Course 1, with the additional requirement that that either the advisor be a CCSE member or the committee contain at least two CCSE members.

Thesis Submission

In addition to approval from the Chair of Course 1 Graduate Program Committee, the complete thesis needs to be submitted to and approved by CCSE. Students should provide a copy of the thesis title page to the CCSE academic administrator for review and approval prior to submitting the final thesis.

Thesis Fields

Course 1 will award degrees under the thesis fields “Civil Engineering and Computation” and “Environmental Engineering and Computation.”

Department of Mechanical Engineering

A complete description of the doctoral program in Mechanical Engineering can be found at http://meche.mit.edu/academic/graduate . Deviations associated with the CSE degree are as follows. MechE-CSE PhD candidates (“2.CSD”) are expected to pass the ME qualifying exam in Computational Engineering (present thesis in computational engineering and take computational engineering subject exam).

The MechE-CSE doctoral program of study consists of at least five graduate-level subjects in computational science and engineering selected from the approved list of Computational Concentration Subjects . Subjects taken as part of an MIT SM degree can be counted toward this requirement. Doctoral candidates are normally expected to take their major subjects at the Institute. The specific subjects will depend on the student’s thesis topic and background, and will be approved by their thesis committee.

The thesis committee composition requirements are identical to those of Course 2, with the additional requirement that  either the advisor be a CCSE member or the committee contain at least two CCSE members.

In addition to approval from the ME Graduate Officer, the complete thesis needs to be submitted to and approved by CCSE. Students should provide a copy of the thesis title page to the CCSE academic administrator for review and approval prior to submitting the final thesis.

Thesis Field

Course 2 will award degrees under the thesis field “Mechanical Engineering and Computation.”

Department of Materials Science and Engineering

A complete description of the graduate program in the Department of Materials Science and Engineering (DMSE) can be found via https://dmse.mit.edu/graduate/programs . Deviations associated with the DMSE-CSE degree (“3.CSD”) are as follows.

The DMSE-CSE doctoral program of study consists of at least five graduate subjects in computational science and engineering selected from the approved list of Computational Concentration Subjects . The CSE five-course requirement can be satisfied through courses that simultaneously satisfy the DMSE core, post-core electives, and/or minor requirements. CSE subjects that a student may have applied towards a MIT SM degree may also be applied towards a DMSE-CSE doctoral major field of study requirement. Doctoral candidates are normally expected to take their major subjects at the Institute. The specific subjects will depend on the student’s thesis topic and background, and will be approved by Thesis Committee.

The Thesis committee composition requirements are identical to those of DMSE, with the additional requirement that that either  the advisor be a CCSE member  or  the committee contain at least two CCSE members.

In addition to approval from the Chair of the Departmental Graduate Program Committee, the complete thesis needs to be submitted to and approved by CCSE. Students should provide a copy of the thesis title page to the CCSE academic administrator for review and approval prior to submitting the final thesis.

DMSE will award degrees under the Thesis field “Computational Materials Science and Engineering”.

Department of Chemical Engineering

A complete description of the doctoral program in Chemical Engineering can be found at  http://web.mit.edu/cheme/academics/grad/advising.html#phdscd . Deviations associated with the ChemE-CSE degree are as follows.

ChemE-CSE students (“10.CSD”) are expected to complete the ChemE core curriculum with a CSE minor consisting of at least four graduate level subjects in computational science and engineering selected from the approved list of Computational Concentration Subjects .  The minor subjects shall not include 10.34, which is already part of the Chemical Engineering core curriculum. Subjects taken as part of an MIT SM program can be counted toward this requirement. Doctoral candidates are normally expected to take their major subjects at the Institute. The specific subjects will depend on the student’s thesis topic and background, and will be approved by the student’s thesis committee.

The thesis committee composition requirements are identical to those of Course 10, with the additional requirement that  either  the committee chair be a CCSE member  or  the committee contain at least two CCSE members.

Course 10 will award degrees under the thesis field “Chemical Engineering and Computation.”

Department of Earth, Atmospheric and Planetary Sciences

Once admitted, doctoral degree candidates are expected to complete the Course 12 degree requirements as outlined at https://eapsweb.mit.edu/academic-resources/grad-resources , except those relating to coursework in the Major Field of Study, Thesis Committee Composition and Thesis Submission that are specific to the EAPS-CSE program and are discussed in more detail below.

Degree candidates are expected to pass the qualifying exam in Course 12.

The EAPS-CSE (“12.CSD”) doctoral program of study consists of at least five graduate-level subjects in computational science and engineering selected from the approved list of Computational Concentration Subjects . The specific subjects will depend on the student’s thesis topic and background, and will be approved by the Thesis Committee. Subjects taken as part of an MIT SM program can be counted toward this requirement. Doctoral candidates are normally expected to take their major subjects at the Institute.

The Thesis committee composition requirements are identical to those of Course 12, with the additional requirement that either the advisor be a CCSE member or the committee contain at least two CCSE members.

In addition to approval from the Examination Committee, the complete thesis needs to be submitted to and approved by CCSE. Students should provide a copy of the thesis title page to the CCSE academic administrator for review and approval prior to submitting the final thesis.

Course 12 will award degrees under the Thesis field ” Computational Earth, Atmospheric and Planetary Sciences “.

Department of Aeronautics and Astronautics

A complete description of the doctoral program in Aeronautics and Astronautics can be found at http://aeroastro.mit.edu/graduate-program/doctoral-degree . Deviations associated with the AeroAstro-CSE degree are as follows. AeroAstro-CSE PhD candidates (“16.CSD”) are expected to pass the Aerospace Computational Engineering track qualifying exam in Course 16.

The AeroAstro-CSE doctoral program of study consists of at least five graduate-level subjects in computational science and engineering selected from the approved list of Computational Concentration Subjects . Subjects taken as part of an MIT SM program can be counted toward this requirement. Doctoral candidates are normally expected to take their major subjects at the Institute. The specific subjects will depend on the student’s thesis topic and background, and will be approved by thesis committee.

The thesis committee composition requirements are identical to those of Course 16, with the additional requirement that either the advisor be a CCSE member or the committee contain at least two CCSE members.

Course 16 will award degrees under the thesis field “Computational Science and Engineering” to students matriculating in/before September 2023 and “Aerospace Engineering and Computational Science” for students matriculating after September 2023.

Department of Mathematics

A description of the plan of study for the Applied Mathematics option of the PhD degree in Course 18, can be found at http://math.mit.edu/academics/grad/timeline/plan.php . Deviations associated with the Math-CSE degree (“18.CSD”) are as follows.

The Math-CSE doctoral program of study consists of at least five graduate-level subjects in computational science and engineering selected from the approved list of Computational Concentration Subjects . Subjects taken as part of an MIT SM degree can be counted toward this requirement. Doctoral candidates are normally expected to take their major subjects at the Institute. The specific subjects will depend on the student’s thesis topic and background, and will be approved by the Chair of the Applied Mathematics Committee in the Mathematics department and CCSE.

The thesis committee composition requirements are identical to those of Course 18, with the additional requirement that either the advisor be a CCSE member or the committee contain at least two CCSE members.

Course 18 will award degrees under the Thesis field “Mathematics and Computational Science”.

Department of Nuclear Science & Engineering

NSE-CSE PhD candidates (“22.CSD”) must satisfy all NSE requirements for doctoral students, including passing the 22.15 module final exam with a satisfactory grade and completing an NSE Field of Specialization requirement. A complete description of the NSE doctoral program  and its requirements can be found at: http://web.mit.edu/nse/education/grad/phd.html .

Deviations associated with the NSE-CSE degree are as follows. The oral exam committee must include at least two CCSE-affiliated faculty members (one or both of whom may be NSE faculty members). The content of the oral exam must address some aspects related to computation.

In addition to satisfying a NSE Field of Specialization requirement, students pursuing the computation option must take at least five graduate-level subjects in computational science and engineering selected from the approved list of Computational Concentration Subjects . Subjects taken as part of an MIT SM program can be counted toward this requirement. Each of these subjects can be applied towards either the Advanced Subject requirement or the Minor requirement (but not both).  None of these subjects can count towards the Field of Specialization requirement. Doctoral candidates are normally expected to take their major subjects at the Institute. The specific subjects will depend on the student’s thesis topic and background, and will be approved by thesis committee.

The thesis committee composition requirements are identical to those of Course 22, with the additional requirement that either the advisor be a CCSE member or the committee contain at least two CCSE members (who may be NSE faculty members).

In addition to approval from the Chair, Department Committee on Graduate Students, the complete thesis needs to be submitted to and approved by CCSE. Students should provide a copy of the thesis title page to the CCSE academic administrator for review and approval prior to submitting the final thesis.

Course 22 will award degrees under the thesis fields “Nuclear Engineering and Computation” and “Computational Nuclear Science and Engineering”.  Student may choose either; the requirements are identical.

Doctoral candidates in general may petition to change the name appearing on their degree certificates. However, petitions from students in the CSE-participating departments listed above to include the keywords ‘computation’ or ‘computational’ in the degree name will only be approved if the student has satisfied requirements listed above. The PhD thesis field “Computational Science and Engineering” will be reserved for students graduating from the standalone CSE PhD program.

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Arvind, longtime MIT professor and prolific computer scientist, dies at 77

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Arvind Mithal, the Charles W. and Jennifer C. Johnson Professor in Computer Science and Engineering at MIT, head of the faculty of computer science in the Department of Electrical Engineering and Computer Science (EECS), and a pillar of the MIT community, died on June 17. Arvind, who went by the mononym, was 77 years old.

A prolific researcher who led the Computation Structures Group in the Computer Science and Artificial Intelligence Laboratory (CSAIL), Arvind served on the MIT faculty for nearly five decades.

“He was beloved by countless people across the MIT community and around the world who were inspired by his intellectual brilliance and zest for life,” President Sally Kornbluth wrote in a letter to the MIT community today.

As a scientist, Arvind was well known for important contributions to dataflow computing, which seeks to optimize the flow of data to take advantage of parallelism, achieving faster and more efficient computation.

In the last 25 years, his research interests broadened to include developing techniques and tools for formal modeling, high-level synthesis, and formal verification of complex digital devices like microprocessors and hardware accelerators, as well as memory models and cache coherence protocols for parallel computing architectures and programming languages.

Those who knew Arvind describe him as a rare individual whose interests and expertise ranged from high-level, theoretical formal systems all the way down through languages and compilers to the gates and structures of silicon hardware.

The applications of Arvind’s work are far-reaching, from  reducing the amount of energy and space required by data centers to  streamlining the design of more efficient multicore computer chips .

“Arvind was both a tremendous scholar in the fields of computer architecture and programming languages and a dedicated teacher, who brought systems-level thinking to our students. He was also an exceptional academic leader, often leading changes in curriculum and contributing to the Engineering Council in meaningful and impactful ways. I will greatly miss his sage advice and wisdom,” says Anantha Chandrakasan, chief innovation and strategy officer, dean of engineering, and the Vannevar Bush Professor of Electrical Engineering and Computer Science.

“Arvind’s positive energy, together with his hearty laugh, brightened so many people’s lives. He was an enduring source of wise counsel for colleagues and for generations of students. With his deep commitment to academic excellence, he not only transformed research in computer architecture and parallel computing but also brought that commitment to his role as head of the computer science faculty in the EECS department. He left a lasting impact on all of us who had the privilege of working with him,” says Dan Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Ellis Warren Professor of Electrical Engineering and Computer Science.

Arvind developed an interest in parallel computing while he was a student at the Indian Institute of Technology in Kanpur, from which he received his bachelor’s degree in 1969. He earned a master’s degree and PhD in computer science in 1972 and 1973, respectively, from the University of Minnesota, where he studied operating systems and mathematical models of program behavior. He taught at the University of California at Irvine from 1974 to 1978 before joining the faculty at MIT.

At MIT, Arvind’s group studied parallel computing and declarative programming languages, and he led the development of two parallel computing languages, Id   and pH. He continued his work on these programming languages through the 1990s, publishing the book “Implicit Parallel Programming in pH”   with co-author R.S. Nikhil in 2001, the culmination of more than 20 years of research.

In addition to his research, Arvind was an important academic leader in EECS. He served as head of computer science faculty in the department and played a critical role in helping with the reorganization of EECS after the establishment of the MIT Schwarzman College of Computing.

“Arvind was a force of nature, larger than life in every sense. His relentless positivity, unwavering optimism, boundless generosity, and exceptional strength as a researcher was truly inspiring and left a profound mark on all who had the privilege of knowing him. I feel enormous gratitude for the light he brought into our lives and his fundamental impact on our community,” says Daniela Rus, the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science and the director of CSAIL.

His work on dataflow and parallel computing led to the Monsoon project in the late 1980s and early 1990s. Arvind’s group, in collaboration with Motorola, built 16 dataflow computing machines and developed their associated software. One Monsoon dataflow machine is now in the  Computer History Museum in Mountain View, California.

Arvind’s focus shifted in the 1990s when, as he explained in a 2012 interview for the Institute of Electrical and Electronics Engineers (IEEE), funding for research into parallel computing began to dry up.

“Microprocessors were getting so much faster that people thought they didn’t need it,” he recalled.

Instead, he began applying techniques his team had learned and developed for parallel programming to the principled design of digital hardware.

In addition to mentoring students and junior colleagues at MIT, Arvind also advised universities and governments in many countries on research in parallel programming and semiconductor design.

Based on his work on digital hardware design, Arvind founded Sandburst in 2000, a fabless manufacturing company for semiconductor chips. He served as the company’s president for two years before returning to the MIT faculty, while continuing as an advisor. Sandburst was later acquired by Broadcom.

Arvind and his students also developed Bluespec, a programming language designed to automate the design of chips. Building off this work, he co-founded the startup Bluespec, Inc., in 2003, to develop practical tools that help engineers streamline device design.

Over the past decade, he was dedicated to advancing undergraduate education at MIT by bringing modern design tools to courses 6.004 (Computation Structures) and 6.191 (Introduction to Deep Learning), and incorporating Minispec, a programming language that is closely related to Bluespec.

Arvind was honored for these and other contributions to data flow and multithread computing, and the development of tools for the high-level synthesis of hardware, with membership in the National Academy of Engineering in 2008 and the American Academy of Arts and Sciences in 2012. He was also named a distinguished alumnus of IIT Kanpur, his undergraduate alma mater.

“Arvind was more than a pillar of the EECS community and a titan of computer science; he was a beloved colleague and a treasured friend. Those of us with the remarkable good fortune to work and collaborate with Arvind are devastated by his sudden loss. His kindness and joviality were unwavering; his mentorship was thoughtful and well-considered; his guidance was priceless. We will miss Arvind deeply,” says Asu Ozdaglar, deputy dean of the MIT Schwarzman College of Computing and head of EECS.

Among numerous other awards, including membership in the Indian National Academy of Sciences and fellowship in the Association for Computing Machinery and IEEE, he received the Harry H. Goode Memorial Award from IEEE in 2012, which honors significant contributions to theory or practice in the information processing field.

A humble scientist, Arvind was the first to point out that these achievements were only possible because of his outstanding and brilliant collaborators. Chief among those collaborators were the undergraduate and graduate students he felt fortunate to work with at MIT. He maintained excellent relationships with them both professionally and personally, and valued these relationships more than the work they did together, according to family members.

In summing up the key to his scientific success, Arvind put it this way in the 2012 IEEE interview: “Really, one has to do what one believes in. I think the level at which most of us work, it is not sustainable if you don’t enjoy it on a day-to-day basis. You can’t work on it just because of the results. You have to work on it because you say, ‘I have to know the answer to this,’” he said.

He is survived by his wife, Gita Singh Mithal, their two sons Divakar ’01 and Prabhakar ’04, their wives Leena and Nisha, and two grandchildren, Maya and Vikram. 

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Researchers leverage shadows to model 3D scenes, including objects blocked from view

Courtesy of the researchers, edited by MIT News

via MIT News

June 18, 2024

• #computer vision
• #artificial intelligence
• #machine learning
• #computer science
• #technology
• #autonomous vehicles
• Ramesh Raskar Associate Professor of Media Arts and Sciences
• Tzofi Klinghoffer Research Assistant
• Siddharth Somasundaram Research Assistant

PlatoNeRF: 3D Reconstruction in Plato's Cave via Single-View Two-Bounce Lidar

• Media Lab Research Theme: Life with AI

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By Adam Zewe 

Imagine driving through a tunnel in an autonomous vehicle, but unbeknownst to you, a crash has stopped traffic up ahead. Normally, you’d need to rely on the car in front of you to know you should start braking. But what if your vehicle could see around the car ahead and apply the brakes even sooner?

Researchers from MIT and Meta have developed a computer vision technique that could someday enable an autonomous vehicle to do just that.

They have introduced a method that creates physically accurate, 3D models of an entire scene, including areas blocked from view, using images from a single camera position. Their technique uses shadows to determine what lies in obstructed portions of the scene.

They call their approach PlatoNeRF, based on Plato’s allegory of the cave, a passage from the Greek philosopher’s “Republic” in which prisoners chained in a cave discern the reality of the outside world based on shadows cast on the cave wall.

By combining lidar (light detection and ranging) technology with machine learning, PlatoNeRF can generate more accurate reconstructions of 3D geometry than some existing AI techniques. Additionally, PlatoNeRF is better at smoothly reconstructing scenes where shadows are hard to see, such as those with high ambient light or dark backgrounds.

In addition to improving the safety of autonomous vehicles, PlatoNeRF could make AR/VR headsets more efficient by enabling a user to model the geometry of a room without the need to walk around taking measurements. It could also help warehouse robots find items in cluttered environments faster.

“Our key idea was taking these two things that have been done in different disciplines before and pulling them together — multibounce lidar and machine learning. It turns out that when you bring these two together, that is when you find a lot of new opportunities to explore and get the best of both worlds,” says Tzofi Klinghoffer, an MIT graduate student in media arts and sciences, affiliate of the MIT Media Lab, and lead author of a  paper on PlatoNeRF .

Klinghoffer wrote the paper with his advisor, Ramesh Raskar, associate professor of media arts and sciences and leader of the Camera Culture Group at MIT; senior author Rakesh Ranjan, a director of AI research at Meta Reality Labs; as well as Siddharth Somasundaram at MIT, and Xiaoyu Xiang, Yuchen Fan, and Christian Richardt at Meta. The research will be presented at the Conference on Computer Vision and Pattern Recognition.

Shedding light on the problem

Reconstructing a full 3D scene from one camera viewpoint is a complex problem.

Some machine-learning approaches employ generative AI models that try to guess what lies in the occluded regions, but these models can hallucinate objects that aren’t really there. Other approaches attempt to infer the shapes of hidden objects using shadows in a color image, but these methods can struggle when shadows are hard to see.

For PlatoNeRF, the MIT researchers built off these approaches using a new sensing modality called single-photon lidar. Lidars map a 3D scene by emitting pulses of light and measuring the time it takes that light to bounce back to the sensor. Because single-photon lidars can detect individual photons, they provide higher-resolution data.

The researchers use a single-photon lidar to illuminate a target point in the scene. Some light bounces off that point and returns directly to the sensor. However, most of the light scatters and bounces off other objects before returning to the sensor. PlatoNeRF relies on these second bounces of light.

By calculating how long it takes light to bounce twice and then return to the lidar sensor, PlatoNeRF captures additional information about the scene, including depth. The second bounce of light also contains information about shadows.

The system traces the secondary rays of light — those that bounce off the target point to other points in the scene — to determine which points lie in shadow (due to an absence of light). Based on the location of these shadows, PlatoNeRF can infer the geometry of hidden objects.

The lidar sequentially illuminates 16 points, capturing multiple images that are used to reconstruct the entire 3D scene.

“Every time we illuminate a point in the scene, we are creating new shadows. Because we have all these different illumination sources, we have a lot of light rays shooting around, so we are carving out the region that is occluded and lies beyond the visible eye,” Klinghoffer says.

A winning combination

Key to PlatoNeRF is the combination of multibounce lidar with a special type of machine-learning model known as a neural radiance field (NeRF). A NeRF encodes the geometry of a scene into the weights of a neural network, which gives the model a strong ability to interpolate, or estimate, novel views of a scene.

This ability to interpolate also leads to highly accurate scene reconstructions when combined with multibounce lidar, Klinghoffer says.

“The biggest challenge was figuring out how to combine these two things. We really had to think about the physics of how light is transporting with multibounce lidar and how to model that with machine learning,” he says.

They compared PlatoNeRF to two common alternative methods, one that only uses lidar and the other that only uses a NeRF with a color image.

They found that their method was able to outperform both techniques, especially when the lidar sensor had lower resolution. This would make their approach more practical to deploy in the real world, where lower resolution sensors are common in commercial devices.

“About 15 years ago, our group invented the first camera to ‘see’ around corners, that works by exploiting multiple bounces of light, or ‘echoes of light.’ Those techniques used special lasers and sensors, and used three bounces of light. Since then, lidar technology has become more mainstream, that led to our research on cameras that can see through fog. This new work uses only two bounces of light, which means the signal to noise ratio is very high, and 3D reconstruction quality is impressive,” Raskar says.

In the future, the researchers want to try tracking more than two bounces of light to see how that could improve scene reconstructions. In addition, they are interested in applying more deep learning techniques and combining PlatoNeRF with color image measurements to capture texture information.

“While camera images of shadows have long been studied as a means to 3D reconstruction, this work revisits the problem in the context of lidar, demonstrating significant improvements in the accuracy of reconstructed hidden geometry. The work shows how clever algorithms can enable extraordinary capabilities when combined with ordinary sensors — including the lidar systems that many of us now carry in our pocket,” says David Lindell, an assistant professor in the Department of Computer Science at the University of Toronto, who was not involved with this work.

Klinghoffer, Tzofi, et al. "PlatoNeRF: 3D Reconstruction in Plato's Cave via Single-View Two-Bounce Lidar." IEEE Conference on Computer Vision and Pattern Recognition (2024).

Media Lab @ CVPR 2024

Two Media Lab research projects are being presented as part of this conference this year.

Camera Culture students selected for National Science Foundation Graduate Research Fellowship Program (NSF-GRFP)

They've been selected as Fellows in the fields of Machine Learning and Computer Vision.

DiSER: Designing Imaging Systems with Reinforcement Learning

Klinghoffer, Tzofi, et al. "DISeR: Designing Imaging Systems with Reinforcement Learning." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023.

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PhD Student Shiwei Ding, Computer Science, to Present Dissertation Proposal

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This as-told-to essay is based on a conversation with Maria Estrada, 51, who is a plant-science lecturer at Fresno State and the mother of two teenagers. It's been edited for length and clarity.

Both of my kids love to compete in science fairs . Combined, they've won more than $67,000 in awards for their projects.

Being immigrant parents, my husband and I are a little bit strict. We make sure that our kids follow the rules .

You need to be respectful and compassionate — that's part of our Filipino culture. We tried to emphasize to them that academics are important, but you need to also be a well-rounded person.

But screen time is one thing that I am probably not good at. My son John loves video games, especially Mario and Pokemon.

He started playing games in fifth or sixth grade on his handheld Nintendo, I think it was. Then he had a PlayStation. He could play for hours.

I did try to control my son's video game use a bit to make sure he would not be addicted to it. I didn't want him to hate me, and I believe in moderation, so I tried to be reasonable. On most school nights, we would ask him to do his assignments first.

Any technology has a positive and a negative aspect. At first, I was just looking at the negative. You don't want your kids to be on the computer a lot.

I saw a lot of articles about kids starting on computers, smartphones, and iPads early . I felt like it could do him harm.

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Then I saw how gaming piqued his interest in computers . It helped him, especially in his science fair projects.

After a while, I saw a lot of advantages to his video game habits .

Games introduced both of my kids to coding

It started with the consoles, but soon John was also playing games on the computer. That's when he began researching how the game was made, which piqued his curiosity about coding.

So I put him and my daughter, Pauline, into an after-school program where they learned to code .

They both used their coding skills later when they developed AI models for their science fair projects.

Video game controllers helped him excel at robotics

John is into electronics — not just the PlayStation 5 console. He programmed a Lego robot in fifth grade.

But video games might have taken my son a step further. John could already use controllers really well, so he got interested in building remote-controlled cars and drones.

In middle school, he built his own drone and flew it around. I don't think he would have been able to do this if he had not been really good at playing with a joystick from his video games.

Soon John was building drones and rovers for his science fair projects.

My kids' electronics projects won awards

Eventually, the kids needed a workshop for their science projects, so we converted a big informal living room into one big table with chairs.

They built their drone there, they built their rover, they built their camera. It was so messy. I would just close the door so I wouldn't see the mess, all the wires and cables. They had so much electronics in there.

In 2021, when he was 16, John won the $50,000 Gordon E. Moore Award for a project he presented at the Regeneron International Science and Engineering Fair (ISEF) , as well as $5,000 for first place in the plant sciences category. He had developed an AI model to detect drought stress in bell pepper plants, using a robotic infrared camera he built.

John and Pauline did their next science fair project together, expanding on the concept with tomato plants and a rover.

Their project went to ISEF in 2022 and won first place in the plant sciences category.

Now John is studying computer science at the University of California, Berkeley. Ultimately, his gaming helped him get there.

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Edward E. Whitacre Jr. College of Engineering

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Master of Engineering

Program overview.

The Master of Engineering degree program is an interdisciplinary, non-thesis program designed primarily for practicing engineers that is offered on the campus of Texas Tech University and by distance learning. For practicing engineers, credit for graduate coursework completed in residence at another accredited graduate school may be accepted for as much as 9 hours of the 36 semester hour requirements for the Master of Engineering degree. All work credited toward the degree must be completed within nine calendar years. In addition to the regulations governing admission to the Texas Tech University Graduate School, a baccalaureate degree in engineering, or its equivalent, is required for entrance to the Master of Engineering program.

Can I take Computer Science (CS) courses as part of the Master of Engineering program?

You may be unable to take Computer Science courses as part of the Master of Engineering program. If you are interested in taking Computer Science courses, however, you are encouraged to apply to the Software Engineering certificate program . The four courses required to earn the certificate can count towards your MEng degree program as well. 

I am an international student. May I apply for a Graduate Assistantship position in the Engineering department?

The Master of Engineering program does not have any assistantship positions.

I am an international student. Does the Master of Engineering program have an internship or co-op? 

No. There are no Curricular Practical Training (CPT), internships or co-op opportunities associated with the Master of Engineering program. 

I am an international student. Is STEM OPT available for the Master of Engineering program?

Yes, Science, Technology, Engineering and Math (STEM) Optional Practical Training (OPT) is available.  

I want to conduct research. May I apply for a Research Assistantship position in the Engineering department?

The general Master of Engineering program does not require a research project and no assistantship positions are available.

I applied for a different Engineering graduate program, but was accepted to the Master of Engineering Program. Can I reapply for my first-choice program in the future?

There are a couple of different avenues you could take. If you truly want to reapply to your first-choice program, you could take three "first-choice" Engineering courses (presuming you're enrolled full-time on campus) during your first semester at Texas Tech and reapply for the following semester. If you are not accepted, however, you'll only be permitted to take an additional two "first-choice" Engineering courses under the Master of Engineering program.

I am an online Distance Learning student. I applied for a different Engineering graduate program, but was accepted to the Master of Engineering Program. Can I reapply for my first-choice program in the future?

There are a couple of different avenues you could take. If you truly want to reapply to your first-choice program, you could take one or two "first-choice" Engineering courses during your first semester and reapply to your desired program. If you are not accepted, you may continue to take "first choice" Engineering courses and reapply until you have reached the Master of Engineering program limit of five courses in any one discipline.

From what engineering disciplines may I select courses?

You may select graduate-level (5000-6999) courses from other Engineering disciplines** with the exception of Graduate Seminars, Master's Reports, Master's Thesis, or Individual Studies.

** Chemical, Civil, Construction, Electrical and Computer Engineering, Engineering, Environmental, Industrial, Mechanical and Petroleum.

How do I search for available classes as a graduate Master of Engineering student?

Why is my course listed in fall through courses in degree works and not reflected in my degree requirements does this mean that my course does not count as a subject for the masters of engineering program.

Because of the interdisciplinary nature of the Master of Engineering program, every student's program is different. Therefore, Degree Works requires manual entries which are completed in March and October just prior to advising appointments for the next term.

Program Details

Degree requirements.

Students in the Master of Engineering program are subject to all master's degree regulations as outlined in the Graduate Catalog. Due to its interdisciplinary nature, the Master of Engineering program does not require specific major and minor subjects. However, the program does allow up to six hours of course work to be taken outside of engineering, upon the approval of the graduate advisor. Students in the Master of Engineering program do not have any language or tool-subject requirements. Every candidate for a master's degree is required to pass a final comprehensive examination. Students will write a report and complete 33 hours of coursework and three hours of a reports course.

Transfer of Courses

As stated above, students are permitted to transfer courses into the Master of Engineering program upon approval of their graduate advisor. The student should seek approval of the transfer courses prior to registering for such courses. Courses taken without prior approval and prior to enrolling in the Master of Engineering program will be accepted at the discretion of the graduate advisor. All transfer courses are subject to the same time limitations as courses taken in the program (a maximum of nine years from the first course in the plan of study until graduation).

The Curriculum

The curriculum for the Master of Engineering program consists of 36 semester credit hours of coordinated graduate level course work. No more than 15 credit hours (5 courses) can be taken in any one engineering discipline, e.g., Industrial Engineering or Mechanical Engineering.

Special Cases

Any student who does not have an undergraduate degree in engineering is considered a special case. To provide the student with a solid program and to ensure that the student has the necessary background knowledge to successfully complete the program, the following courses are required:

• Principles of Chemistry I & II (TTU CHEM 1307 & 1308)
• Principles of Physics I & II (Calculus-based) (TTU PHYS 1408 & 2401)
• Calculus with Applications I, II, & III (TTU MATH 1451, 1452, & 2450)
• Differential Equations (TTU MATH 3354)

Note: TTU Course Numbers are provided for your reference. These courses may also be taken at TTU or at a local university or community college. Transcripts must be included with your graduate application.

Master of Engineering Specializations

Standard option.

Students may elect to take engineering courses from any of the College of Engineering's disciplines.

Healthcare Engineering Option

The Healthcare Engineering Option is available for engineers who are interested in applying the principles of engineering, health sciences, and business administration to effectively manage the physical, technological, and supports services of healthcare facilities.

Learn more at the Healthcare Engineering Option page.

Dual Degree Program

Doctor of jurisprudence (j.d.) and master of engineering.

The School of Law, in association with the Graduate School, offers a program that enables a student to earn both the Doctor of Jurisprudence (J.D.) and Master of Engineering degrees in three or four years of academic work. Interested students must already be accepted and enrolled in the School of Law program and declare their intent to pursue the dual degrees no later than their third semester in law school.

Learn more at the Doctor of Jurisprudence and Master of Engineering page.

Contact Information

If you have questions about the degree program, please contact:

Pal, Ranadip, Ph.D. Assistant Dean of Strategic Initiatives and Professor [email protected] 806.834.8631

Contact TTU

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