mit phd computational science and engineering

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Master of Science Program in Computational Science and Engineering

The master’s degree in Computational Science and Engineering (CSE), previously the Computation for Design and Optimization (CDO) SM program, is an interdisciplinary program designed to prepare tomorrow’s engineers and scientists in advanced computational methods and applications. The program provides a strong foundation in computational approaches to the design and operation of complex engineered and scientific systems.

As an interdisciplinary academic program, the CSE SM is housed in the Center for Computational Science & Engineering but students have the opportunity to work with faculty from across the Institute.  Through hands-on projects and a master’s thesis, students develop and apply advanced computational methods to a diverse range of applications, from aerospace to nanotechnology, from Internet protocols to telecommunications system design. Career opportunities for CSE SM graduates include companies and research centers where systems modeling, numerical simulation, design and optimization play a critical role.

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Center for Computational Science and Engineering (CCSE)

Established in 2008 and incorporated into the Schwarzman College of Computing as one of its core academic units in January 2020, the MIT Center for Computational Science and Engineering (CCSE) is an interdisciplinary research and education center focused on innovative methods and applications of computation. CCSE involves faculty, researchers, and students from MIT’s Schools of Engineering, Science, Architecture and Planning, and Management, as well as other units of the Schwarzman College of Computing. Our educational programs seek to train future generations of computational scientists and engineers to both develop and use sophisticated computational methods for a wide variety of applications.

CCSE offers two graduate programs (SM & PhD) in Computational Science and Engineering (CSE). The CSE master’s degree program is interdisciplinary, providing students with a strong foundation in computational methods for the simulation, design, and analysis of complex engineered and scientific systems; it emphasizes educational breadth through introductory core courses in numerical analysis, simulation, and optimization, and depth though an elective component that focuses on particular applications as well as a thesis. Students in the CSE PhD program undertake advanced specialization in a computation-related field of their choice through focused coursework and a doctoral thesis in this field. The CSE PhD degree is awarded by one of the following eight departments: Aeronautics and Astronautics, Chemical Engineering, Civil and Environmental Engineering, Earth Atmospheric and Planetary Sciences, Materials Science and Engineering, Mathematics, Mechanical Engineering, and Nuclear Science and Engineering; the specialization in computational science and engineering is highlighted by specially crafted thesis fields.

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Sub-communities within this community, computation for design and optimization (cdo) master of science program [renamed in 2020], collections in this community, recent submissions.

Leveraging the Linear Response Theory in Sensitivity Analysis of Chaotic Dynamical Systems and Turbulent Flows 

Modeling Feedback Effects of Transient Nuclear Systems Using Monte Carlo 

Learning Mixed Multinomial Logit Models 

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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.

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Graduate students in the Department of Civil and Environmental Engineering at MIT are central to our educational mission.

The degrees offered are:

• Master of Engineering (MEng) , including a new 9 month MEng degree in Data Science for Engineering Systems
• The Master of Science (SM) & Doctor of Philosophy (PhD)

Interdepartmental Program in Transportation (SM and PhD)

Joint and dual degrees, master of engineering (meng), the master of science (sm) & doctor of philosophy (phd).

The Master of Engineering (MEng) degree program is our professional-oriented graduate program. The MEng program consists of high level, fast paced coursework and significant engagement with real world engineering projects, preparing our graduates for a professional career path, or further graduate studies at MIT or elsewhere. This 9 month program, with opportunities for individualized tracks in CEE – prepares our graduates for addressing significant challenges in the domains of Civil and Environmental Engineering.

For current MIT students, the program is a natural extension of the Institute’s 4 year Bachelor of Science degree for students to gain practical experiences and prepare for emerging fields in today’s job market. The MEng program is self-funded. Admitted students are responsible for paying tuition and cost-of-living expenses or securing external fellowships. MIT’s Office of Graduate Education maintains a helpful database of such fellowships on their website.

Summary of Requirements The Master of Engineering degree is awarded to students who have satisfactorily completed a structured program of at least 90 units, consisting of 66 units graduate level subjects, and a 24 unit thesis approved by the department. In one academic year, the students are required to take:

• Four graduate subjects offered by the Department of Civil and Environmental Engineering, totaling at least 48 units
• Two elective subjects, possibly offered by other MIT departments, totaling at least 18 units
• MEng Thesis, equivalent to 24 units

What do students do with their degree? Graduates of the MEng Program go on to careers as leaders in industry, government, consulting, and some pursue further graduate education.

MEng Track Descriptions and Requirements

Climate, environment, and sustainability (ces) track.

Within the Climate, Environment and Sustainability (CES) track of the MEng degree program, students take coursework to develop their understanding of CES and pursue research on CES topics across the breadth of civil and environmental engineering (e.g. ecological systems, air pollution, food, energy). Read our article to learn more about this track.

Coursework: Within the Climate, Environment, and Sustainability track, students must select 3 of the following subjects:

• 1.771 Global Change Science
• 1.837 Resilience of Living Systems to Environmental Change
• 1.855 Air Pollution and Atmospheric Chemistry
• 1.65 Atmospheric Boundary Layer Flows and Wind Energy
• 1.811[J] Environmental Law, Policy, and Economics: Pollution Prevention and Control
• 1.845 Introduction to the terrestrial carbon cycle and ecosystem ecology
• 1.670 Energy Systems for Climate Change Mitigation
• 1.C51 Machine Learning for Sustainable Systems

Data Science for Engineering Systems (DSES) track

Within the Data Science for Engineering Systems (DSES) track of the MEng degree program, students gain expertise in data science and computational modeling tools for improving the sustainability and resilience of next generation societal-scale infrastructure systems. Students pursue curriculum, research and specialized training that prepare them for careers in sustainable and resilient design of energy systems, materials and structures, supply chains, and urban systems. Learn more, download our information sheet.

In the Data Science for Engineering Systems track, there are two focus areas of concentration for students to choose based on their interests: Computational Modeling and Design for Sustainability or Resilient Infrastructure Systems and Services.

Coursework: Within the DSES track, students must take 48 units in the Department of Civil and Environmental Engineering including: 6 units of project studio. Project studio class combines work in the lab with MIT faculty and seminars conducted by industry partners. Students will also receive guidance on data acquisition, analysis and computational modeling tools.

18 units of CEE-centric data science from the following subjects:

•  1.275 Business and Operations Analytics
• IDS.131 Statistics, Computation, Analytics
• 6.C51 Modeling with Machine Learning: from Algorithms to Applications + 1.C51 Machine Learning for Sustainable Systems (must be taken and completed simultaneously)
• 1.121 Advancing Mechanics and Materials via Machine Learning
• 1.125 Architecting and Engineering Software Systems

24 units of CEE offered subjects in Computational Modeling and Design for Sustainability:

•  1.545 Atomistic Modeling and Simulation of Materials and Structures
• 1.579 Materials in Agriculture, Food Security, and Food Safety
• 1.61 Transport Processes in the Environment
•  1.147 Startup Sustainable Tech
• 1.723 Computational Methods for Environmental Flows
• 1.631 Fluids and Diseases

24 units of CEE offered subjects in Resilient Infrastructure Systems and Services:

• 1.208 Resilient Networks
• 1.260 Logistics Systems
• 1.303 Infrastructure Design for Climate Change
• 1.581 Structural Dynamics
• 1.583 Topology Optimization of Structures
• 1.200 Transportation: Foundations and Methods
• 1.202 Demand Modeling
• 1.266 Supply Chain and Demand Analytics
• 1.263 Urban Last-Mile Logistics

Structural Mechanics and Design (SMD) Track

Within the Structural Mechanics and Design track of the MEng degree program, students pursue curriculum and research in areas including structural engineering mechanics, computational design and optimization, and collaborative workflows at the interface of engineering and architecture. Take a peek inside the SMD track by watching a three minute video .

Coursework: Within the Structural Mechanics and Design track, two of the four CEE subjects must include 1.562 Structural Design Project I (Fall) and 1.563 Structural Design Project II (Spring)

The Master of Science (SM) and Doctor of Philosophy (PhD) are the research focused graduate degrees in the department of Civil and Environmental Engineering (CEE). Each SM and PhD graduate student in our department is matched with one of our faculty members to work together on the research component of the graduate degree.

Connecting graduate students through research interests with faculty and offering the opportunity for advanced course work is a combination that produces highly successful graduates that emerge as leaders in their fields.

With broad areas of study, we offer students the opportunity to pursue educational goals – through research and course work that is exciting and motivating to you.

The Areas of Study Include:

• Environmental Chemistry
• Environmental Fluid Mechanics
• Ecology and Evolution
• Hydrology and Hydroclimatology
• Systems Engineering including Networks and Transportation
• Materials, Structures and Geomechanics

Degree Requirements

Master of Science The Master of Science degree is awarded to students who have satisfactorily completed a program of study of at least 66 units of graduate level subjects, approved by the department in which s/he is enrolled, and a 24 unit thesis approved by the department.

Summary of Requirements

• 66 units of graduate level subjects, of which 34 units are offered by the Department of Civil and Environmental Engineering

Doctor of Philosophy The Doctor of Philosophy degree is awarded to students who have satisfactorily completed a doctoral program in CEE consisting of 96 units of graduate level coursework, including a 3-Subject Core and one breadth subject. The 3-Subject Core reflects core knowledge in the student’s chosen field. The remainder of the doctoral program consists of graduate subjects that complement the Core.

• 96 units of graduate level subjects, including a three Subject Core and one breadth subject
• Successful completion of the general exam at the end of academic year 2
• Successful completion of the PhD proposal

What do students do with their research degree? Graduates from our SM and PhD degree tracks go on to top jobs as engineers, faculty members at eminent universities, engineering consultants, leaders for NGOs, and entrepreneurs.

Master of Science in Transportation The interdepartmental Master of Science in Transportation (MST) degree program emphasizes the complexity of transportation and its dependence on the interaction of technology, operations, planning, management and policy-making. For this reason, the Master of Science in Transportation program is interdepartmental. Faculty members and research staff from several centers, departments and divisions within MIT are affiliated with the program and serve as research advisors and mentors to MST students.

Summary of Requirements:

• 1.200 Transportation: foundations and methods (12 units)
• One of the following: (12 units)
• 1.202 Demand Modeling, or
• 1.208 Resilient Networks or
• 1.260 Logistics Systems or
• 11.478 Behavior and Policy : Connections in Transportation
• 11.S953  Frontier of Transportation Research (3 units)
• IDS.131 Statistics, Computation and Applications or
• 6.862 Applied Machine Learning
• Equivalent subject or higher [petition to Transportation Education Committee]
• Policy and Technology: select subject in consultation with advisor (9-12 units)
• Individually designed program from MST program areas (15-18 units)
• MST Thesis, equivalent to 24 units

Interdepartmental Doctoral Program in Transportation The interdepartmental Doctoral Program in Transportation provides a structured and direct follow-on doctoral program for students enrolled in the MST program or other transportation-related master degree programs at MIT or elsewhere. The interdepartmental structure of the program allows students greater flexibility in developing individual programs of study that cross both disciplinary and departmental lines. The program is administered by the transportation faculty through the Transportation Education Committee (TEC), who are responsible for admissions, establishment and oversight of program requirements, and conduct of the general examination and thesis defense.

• 120 units of graduate level subjects, including a doctoral core program, consisting of two subject areas as selected by the student
• Successful completion of the general exam by the end of academic year 2
• PhD Thesis, equivalent to 24 units

Both the interdepartmental Master of Science in Transportation and the interdepartmental Doctoral Program in Transportation are administered through the department of Civil and Environmental Engineering.

The joint and dual degrees listed below stemmed from a formal arrangement between an MIT department and another program affiliated with MIT. CEE participates in four such degree programs.

WHOI: Woods Hole Oceanographic Institution

Through MIT’s agreement with the WHOI the department of Civil and Environmental Engineering we have several outstanding students who participate in this joint program, often times splitting their time between MIT’s and WHOI’s campuses.

Please visit the  MIT-WHOI joint program website  to learn more about the fields of study.

CSE: PhD Program in Computational Science and Engineering

The PhD Program in Computational Science and Engineering ( CSE ) is a collaboration of CEE and the  Center for Computational Engineering . The CSE Doctoral Program allows CEE students to specialize in a computation-related field of their choice through focused coursework and a Doctoral Thesis, and to participate in a multi-departmental program by satisfying a set of computation-specific requirements.

LGO: Leaders for Global Operations

Leaders for Global Operations (LGO) is a collaboration of MIT’s School of Engineering and Sloan School of Management. Students earn both an MBA and SM in two years. Please visit the  LGO in Civil and Environmental Engineering website  to learn about the different CEE tracks.

Learn more about the LGO application process. 

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Yaocheng Sparrow Tian

MechE-CSE PhD

Kento Abeywardane

ChemE-CSE PhD

Jose del Aguila Ferrandis

d'Arbeloff Career Development Assistant Professor of Mechanical Engineering

Zeyad Al Awwad

AeroAstro-CSE PhD

Meshal Alharbi

Abdullah Almaatouq

Douglas Drane Career Development Professor in Information Technology

Saurabh Amin

Professor of Civil and Environmental Engineering

Anuradha Annaswamy

Senior Research Scientist, Mechanical Engineering; Director of Active-Adaptive Control Lab

Brian Anthony

Principal Research Scientist, Mechanical Engineering

Constantine Athanitis

DMSE-CSE PhD

Abdulaziz Aziz Alhassan

CEE-CSE PhD

Navid Azizan

Edgerton Assistant Professor of Mechanical Engineering, and Data, Systems, and Society

Emilio Baglietto

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George Barbastathis

Professor of Mechanical Engineering

Paul Barton

Lammot du Pont Professor of Chemical Engineering

Professor of Biological Engineering

Klaus-Jürgen Bathe

Martin Bazant

E. G. Roos (1944) Professor of Chemical Engineering; Professor of Mathematics

Abigail Bodner

Assistant Professor, Departments of Earth, Atmospheric and Planetary Sciences, and Electrical Engineering and Computer Science

Marcos Botto Tornielli

Richard Braatz

Edwin R. Gilliland Professor of Chemical Engineering

Markus J. Buehler

Jerry McAfee (1940) Professor in Engineering; Professor of Civil and Environmental Engineering

Jackson Burns

Oral Buyukozturk

George Macomber Professor in Construction Management; Professor of Civil and Environmental Engineering

Pengfei Cai

W. Craig Carter

POSCO Professor of Materials Science and Engineering

Bianca Champenois

Connor Coley

Henri Slezynger (1957) Career Development Assistant Professor

Josefina Correa

CSE-SM, PhD candidate in Neuroscience and Statistics

Luca Daniel

Professor of Electrical Engineering and Computer Science

David Darmofal

Jerome C. Hunsaker Professor of Aeronautics and Astronautics

Olivier de Weck

Professor of Aeronautics and Astronautics and Engineering Systems

Laurent Demanet

Professor of Applied Mathematics; Professor of Earth, Atmospheric and Planetary Sciences

Isabel Diersen

Vaibhav dixit.

Manan Doshi

Terry J. Kohler Professor of Aeronautics and Astronautics

Xiaochen Du

Alan Edelman

Professor of Applied Mathematics, Computer Science & AI Laboratories, Julia Lab Group Leader

Raffaele Ferrari

Cecil and Ida Green Professor of Oceanography

Katharine Fisher

Benoit Forget

Korea Electric Power Professor of Nuclear Engineering

Rodrigo Freitas

AMAX Assistant Professor of Materials Science and Engineering

Robert Freund

Theresa Seley Professor in Management Science, Sloan School of Management

James Gabbard

Ahmed F. Ghoniem

Ronald C. Crane (1972) Professor; Director, Center for Energy and Propulsion Research; Director, Reacting Gas Dynamics Laboratory

Rafael Gomez-Bombarelli

Associate Professor of Materials Science and Engineering; Jeffrey Cheah Career Development Chair

William H. Green

Hoyt Hottel Professor in Chemical Engineering, Postdoctoral Officer

Kevin Greenman

Sarah Greer

Math-CSE PhD

Jeffrey C. Grossman

Morton and Claire Goulder and Family Professor in Environmental Systems; Professor of Materials Science and Engineering

Nicolas G. Hadjiconstantinou

Professor of Mechanical Engineering; Co-Director, Center for Computational Science and Engineering

Robert Haimes

Principal Research Engineer, Aeronautics and Astronautics

Giulianna Hashemi-Asasi

Colette L. Heald

The Germeshausen Professor; Professor of Civil and Environmental Engineering; Professor of Earth, Atmospheric and Planetary Sciences

Ian Hutchinson

Professor of Nuclear Science & Engineering

Vineet Jagadeesan Nair

Steven G. Johnson

Professor of Applied Mathematics and Physics

Ruben Juanes

Professor of Civil and Environmental Engineering; Professor of Earth, Atmospheric, and Planetary Sciences

Xin Kai Lee

EAPS-CSE PhD

Kenneth Kamrin

Dimitris Konomis

Heather J. Kulik

Associate Professor, Chemical Engineering

Adriana Ladera

Pierre Lermusiaux

Nam Pyo Suh Professor of Mechanical Engineering; Associate Department Head for Operations

Battelle Energy Alliance Professor in Nuclear Engineering; Professor of Materials Science and Engineering

Class of 1947 Career Development Professor, Associate Professor of Nuclear Science and Engineering

Nuno Loureiro

Professor of Nuclear Science and Engineering; Professor of Physics

Adrián Lozano-Durán

Draper Assistant Professor of Aeronautics and Astronautics

Julien Luzzatto

John Marshall

Professor of Oceanography

Youssef M. Marzouk

Professor of Aeronautics and Astronautics; Co-director, Center for Computational Science and Engineering

Evan Massaro

Aimee Maurais

Dennis B. McLaughlin

H.M. King Bhumibol Professor, Post-Tenure

Caitlin Mueller

Associate Professor of Civil and Environmental Engineering; Associate Professor of Architecture

Kate Nelson

CCSE Academic Administrator

N Cuong Nguyen

Principal Research Scientist, Aeronautics and Astronautics, Center for Computational Engineering

Subeen Pang

Pablo A. Parrilo

Joseph F. & Nancy P. Keithley Professor in Electrical Engineering and Computer Science

Anthony T. Patera

Alex “Sandy” Pentland

Toshiba Professor of Media, Arts, and Sciences

Jaime Peraire

H.N. Slater Professor of Aeronautics and Astronautics

Simone Peter

Raúl A. Radovitzky

Jerome C. Hunsaker Professor in Aeronautics and Astronautics; Associate Director, MIT Institute for Soldier Nanotechnologies

Aaditya Rau

Principal Research Scientist, Earth, Atmospheric and Planetary Sciences (EAPS)

Rashmi Ravishankar

Danyal Rehman

Gavin Ridley

NSE-CSE PhD

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Institute for Medical Engineering and Science

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Study reveals why AI models that analyze medical images can be biased

These models, which can predict a patient’s race, gender, and age, seem to use those traits as shortcuts when making medical diagnoses.

Computational Science and Engineering SM

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

617-253-3725 [email protected]

Website: Computational Science and Engineering SM

Note: To focus resources on the new  CSE PhD program ,   external admissions for the CSE SM program are currently paused. Applicants interested in Computer Science must apply to the Electrical Engineering and Computer Science program.

Terms of Enrollment

Fall Term (September)

Master of Science in Computational Science and Engineering

Standardized Tests

Graduate Record Examination (GRE)

• General test not required for the 2022-2023 admissions cycle

International English Language Testing System (IELTS)

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

TOEFL exam scores are not accepted. Waiver of IELTS may be available.

Financial Support

The CCSE SM program cannot guarantee financial support for each admitted student. However, we make every effort to match admitted students with CCSE-affiliated faculty who can offer financial support through a sponsored research project. Most internal sources of support at MIT are in the form of research assistantships (RAs) and teaching assistantships (TAs). The CCSE administrator is in contact with faculty in search of matches between faculty research interests, funding, and the profiles of admitted SM students, with the objective of helping students find opportunities for financial support.

Students who arrive on campus without having secured financial support will be expected to pay their own tuition, fees and living expenses. Please see the CCSE website for more information.

Application Requirements

• Online application
• Statement of objectives (including Subjects Taken section)
• Three letters of recommendation
• Transcripts
• English proficiency exam scores
• CV or resume
• GRE scores (not required for the 2022-2023 application cycle)

Special Instructions

The Computational Science and Engineering (CSE) SM program is an interdisciplinary program designed to prepare tomorrow’s engineers and scientists in advanced computational methods and applications. The program provides a strong foundation in computational approaches to the design and operation of complex engineered and scientific systems.

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

All supporting materials, including transcripts, should be uploaded to the electronic application. Official transcripts are only required upon acceptance and will be requested at a later date.

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Scientists use computational modeling to guide a difficult chemical synthesis

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Researchers from MIT and the University of Michigan have discovered a new way to drive chemical reactions that could generate a wide variety of compounds with desirable pharmaceutical properties.

These compounds, known as azetidines, are characterized by four-membered rings that include nitrogen. Azetidines have traditionally been much more difficult to synthesize than five-membered nitrogen-containing rings, which are found in many FDA-approved drugs.

The reaction that the researchers used to create azetidines is driven by a photocatalyst that excites the molecules from their ground energy state. Using computational models that they developed, the researchers were able to predict compounds that can react with each other to form azetidines using this kind of catalysis.

“Going forward, rather than using a trial-and-error process, people can prescreen compounds and know beforehand which substrates will work and which ones won't,” says Heather Kulik, an associate professor of chemistry and chemical engineering at MIT.

Kulik and Corinna Schindler, a professor of chemistry at the University of Michigan, are the senior authors of the study, which appears today in Science . Emily Wearing, recently a graduate student at the University of Michigan, is the lead author of the paper. Other authors include University of Michigan postdoc Yu-Cheng Yeh, MIT graduate student Gianmarco Terrones, University of Michigan graduate student Seren Parikh, and MIT postdoc Ilia Kevlishvili.

Light-driven synthesis

Many naturally occurring molecules, including vitamins, nucleic acids, enzymes and hormones, contain five-membered nitrogen-containing rings, also known as nitrogen heterocycles. These rings are also found in more than half of all FDA-approved small-molecule drugs, including many antibiotics and cancer drugs.

Four-membered nitrogen heterocycles, which are rarely found in nature, also hold potential as drug compounds. However, only a handful of existing drugs, including penicillin, contain four-membered heterocycles, in part because these four-membered rings are much more difficult to synthesize than five-membered heterocycles.

In recent years, Schindler’s lab has been working on synthesizing azetidines using light to drive a reaction that combines two precursors, an alkene and an oxime. These reactions require a photocatalyst, which absorbs light and passes the energy to the reactants, making it possible for them to react with each other.

“The catalyst can transfer that energy to another molecule, which moves the molecules into excited states and makes them more reactive. This is a tool that people are starting to use to make it possible to make certain reactions occur that wouldn't normally occur,” Kulik says.

Schindler’s lab found that while this reaction sometimes worked well, other times it did not, depending on which reactants were used. They enlisted Kulik, an expert in developing computational approaches to modeling chemical reactions, to help them figure out how to predict when these reactions will occur.

The two labs hypothesized that whether a particular alkene and oxime will react together in a photocatalyzed reaction depends on a property known as the frontier orbital energy match. Electrons that surround the nucleus of an atom exist in orbitals, and quantum mechanics can be used to predict the shape and energies of these orbitals. For chemical reactions, the most important electrons are those in the outermost, highest energy (“frontier”) orbitals, which are available to react with other molecules.

Kulik and her students used density functional theory, which uses the Schrödinger equation to predict where electrons could be and how much energy they have, to calculate the orbital energy of these outermost electrons.

These energy levels are also affected by other groups of atoms attached to the molecule, which can change the properties of the electrons in the outermost orbitals.

Once those energy levels are calculated, the researchers can identify reactants that have similar energy levels when the photocatalyst boosts them into an excited state. When the excited states of an alkene and an oxime are closely matched, less energy is required to boost the reaction to its transition state — the point at which the reaction has enough energy to go forward to form products.

Accurate predictions

After calculating the frontier orbital energies for 16 different alkenes and nine oximes, the researchers used their computational model to predict whether 18 different alkene-oxime pairs would react together to form an azetidine. With the calculations in hand, these predictions can be made in a matter of seconds.

The researchers also modeled a factor that influences the overall yield of the reaction: a measure of how available the carbon atoms in the oxime are to participate in chemical reactions.

The model’s predictions suggested that some of these 18 reactions won’t occur or won’t give a high enough yield. However, the study also showed that a significant number of reactions are correctly predicted to work.

“Based on our model, there's a much wider range of substrates for this azetidine synthesis than people thought before. People didn't really think that all of this was accessible,” Kulik says.

Of the 27 combinations that they studied computationally, the researchers tested 18 reactions experimentally, and they found that most of their predictions were accurate. Among the compounds they synthesized were derivatives of two drug compounds that are currently FDA-approved: amoxapine, an antidepressant, and indomethacin, a pain reliever used to treat arthritis.

This computational approach could help pharmaceutical companies predict molecules that will react together to form potentially useful compounds, before spending a lot of money to develop a synthesis that might not work, Kulik says. She and Schindler are continuing to work together on other kinds of novel syntheses, including the formation of compounds with three-membered rings.

“Using photocatalysts to excite substrates is a very active and hot area of development, because people have exhausted what you can do on the ground state or with radical chemistry,” Kulik says. “I think this approach is going to have a lot more applications to make molecules that are normally thought of as really challenging to make.”

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Caption:A new way to drive chemical reactions could generate a wide variety of drugs containing azetidines, like Penicillin does.

Image: Jose-Luis Olivares, MIT; iStock

Scientists use computational modeling to guide a difficult chemical synthesis

Using this new approach, researchers could develop drug compounds with unique pharmaceutical properties.

Researchers from MIT and the University of Michigan have discovered a new way to drive chemical reactions that could generate a wide variety of compounds with desirable pharmaceutical properties.

These compounds, known as azetidines, are characterized by four-membered rings that include nitrogen. Azetidines have traditionally been much more difficult to synthesize than five-membered nitrogen-containing rings, which are found in many FDA-approved drugs.

The reaction that the researchers used to create azetidines is driven by a photocatalyst that excites the molecules from their ground energy state. Using computational models that they developed, the researchers were able to predict compounds that can react with each other to form azetidines using this kind of catalysis.

“Going forward, rather than using a trial-and-error process, people can prescreen compounds and know beforehand which substrates will work and which ones won’t,” says Heather Kulik , an associate professor of chemistry and chemical engineering at MIT.

Kulik and Corinna Schindler, a professor of chemistry at the University of Michigan, are the senior authors of the study, which appears today in Science . Emily Wearing, recently a graduate student at the University of Michigan, is the lead author of the paper. Other authors include University of Michigan postdoc Yu-Cheng Yeh, MIT graduate student Gianmarco Terrones, University of Michigan graduate student Seren Parikh, and MIT postdoc Ilia Kevlishvili.

Light-driven synthesis

Many naturally occurring molecules, including vitamins, nucleic acids, enzymes and hormones, contain five-membered nitrogen-containing rings, also known as nitrogen heterocycles. These rings are also found in more than half of all FDA-approved small-molecule drugs, including many antibiotics and cancer drugs.

Four-membered nitrogen heterocycles, which are rarely found in nature, also hold potential as drug compounds. However, only a handful of existing drugs, including penicillin, contain four-membered heterocycles, in part because these four-membered rings are much more difficult to synthesize than five-membered heterocycles.

In recent years, Schindler’s lab has been working on synthesizing azetidines using light to drive a reaction that combines two precursors, an alkene and an oxime. These reactions require a photocatalyst, which absorbs light and passes the energy to the reactants, making it possible for them to react with each other.

“The catalyst can transfer that energy to another molecule, which moves the molecules into excited states and makes them more reactive. This is a tool that people are starting to use to make it possible to make certain reactions occur that wouldn’t normally occur,” Kulik says.

Schindler’s lab found that while this reaction sometimes worked well, other times it did not, depending on which reactants were used. They enlisted Kulik, an expert in developing computational approaches to modeling chemical reactions, to help them figure out how to predict when these reactions will occur.

The two labs hypothesized that whether a particular alkene and oxime will react together in a photocatalyzed reaction depends on a property known as the frontier orbital energy match. Electrons that surround the nucleus of an atom exist in orbitals, and quantum mechanics can be used to predict the shape and energies of these orbitals. For chemical reactions, the most important electrons are those in the outermost, highest energy (“frontier”) orbitals, which are available to react with other molecules.

Kulik and her students used density functional theory, which uses the Schrödinger equation to predict where electrons could be and how much energy they have, to calculate the orbital energy of these outermost electrons.

These energy levels are also affected by other groups of atoms attached to the molecule, which can change the properties of the electrons in the outermost orbitals.

Once those energy levels are calculated, the researchers can identify reactants that have similar energy levels when the photocatalyst boosts them into an excited state. When the excited states of an alkene and an oxime are closely matched, less energy is required to boost the reaction to its transition state — the point at which the reaction has enough energy to go forward to form products.

Accurate predictions

After calculating the frontier orbital energies for 16 different alkenes and nine oximes, the researchers used their computational model to predict whether 18 different alkene-oxime pairs would react together to form an azetidine. With the calculations in hand, these predictions can be made in a matter of seconds.

The researchers also modeled a factor that influences the overall yield of the reaction: a measure of how available the carbon atoms in the oxime are to participate in chemical reactions.

The model’s predictions suggested that some of these 18 reactions won’t occur or won’t give a high enough yield. However, the study also showed that a significant number of reactions are correctly predicted to work.

“Based on our model, there’s a much wider range of substrates for this azetidine synthesis than people thought before. People didn’t really think that all of this was accessible,” Kulik says.

Of the 27 combinations that they studied computationally, the researchers tested 18 reactions experimentally, and they found that most of their predictions were accurate. Among the compounds they synthesized were derivatives of two drug compounds that are currently FDA-approved: amoxapine, an antidepressant, and indomethacin, a pain reliever used to treat arthritis.

This computational approach could help pharmaceutical companies predict molecules that will react together to form potentially useful compounds, before spending a lot of money to develop a synthesis that might not work, Kulik says. She and Schindler are continuing to work together on other kinds of novel syntheses, including the formation of compounds with three-membered rings.

“Using photocatalysts to excite substrates is a very active and hot area of development, because people have exhausted what you can do on the ground state or with radical chemistry,” Kulik says. “I think this approach is going to have a lot more applications to make molecules that are normally thought of as really challenging to make.”

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Scientists use computational modeling to guide a difficult chemical synthesis

Using this new approach, researchers could develop drug compounds with unique pharmaceutical properties.

Researchers from MIT and the University of Michigan have discovered a new way to drive chemical reactions that could generate a wide variety of compounds with desirable pharmaceutical properties.

These compounds, known as azetidines, are characterized by four-membered rings that include nitrogen. Azetidines have traditionally been much more difficult to synthesize than five-membered nitrogen-containing rings, which are found in many FDA-approved drugs.

The reaction that the researchers used to create azetidines is driven by a photocatalyst that excites the molecules from their ground energy state. Using computational models that they developed, the researchers were able to predict compounds that can react with each other to form azetidines using this kind of catalysis.

"Going forward, rather than using a trial-and-error process, people can prescreen compounds and know beforehand which substrates will work and which ones won't," says Heather Kulik, an associate professor of chemistry and chemical engineering at MIT.

Kulik and Corinna Schindler, a professor of chemistry at the University of Michigan, are the senior authors of the study, which appears today in Science . Emily Wearing, recently a graduate student at the University of Michigan, is the lead author of the paper. Other authors include University of Michigan postdoc Yu-Cheng Yeh, MIT graduate student Gianmarco Terrones, University of Michigan graduate student Seren Parikh, and MIT postdoc Ilia Kevlishvili.

Light-driven synthesis

Many naturally occurring molecules, including vitamins, nucleic acids, enzymes and hormones, contain five-membered nitrogen-containing rings, also known as nitrogen heterocycles. These rings are also found in more than half of all FDA-approved small-molecule drugs, including many antibiotics and cancer drugs.

Four-membered nitrogen heterocycles, which are rarely found in nature, also hold potential as drug compounds. However, only a handful of existing drugs, including penicillin, contain four-membered heterocycles, in part because these four-membered rings are much more difficult to synthesize than five-membered heterocycles.

In recent years, Schindler's lab has been working on synthesizing azetidines using light to drive a reaction that combines two precursors, an alkene and an oxime. These reactions require a photocatalyst, which absorbs light and passes the energy to the reactants, making it possible for them to react with each other.

"The catalyst can transfer that energy to another molecule, which moves the molecules into excited states and makes them more reactive. This is a tool that people are starting to use to make it possible to make certain reactions occur that wouldn't normally occur," Kulik says.

Schindler's lab found that while this reaction sometimes worked well, other times it did not, depending on which reactants were used. They enlisted Kulik, an expert in developing computational approaches to modeling chemical reactions, to help them figure out how to predict when these reactions will occur.

The two labs hypothesized that whether a particular alkene and oxime will react together in a photocatalyzed reaction depends on a property known as the frontier orbital energy match. Electrons that surround the nucleus of an atom exist in orbitals, and quantum mechanics can be used to predict the shape and energies of these orbitals. For chemical reactions, the most important electrons are those in the outermost, highest energy ("frontier") orbitals, which are available to react with other molecules.

Kulik and her students used density functional theory, which uses the Schrödinger equation to predict where electrons could be and how much energy they have, to calculate the orbital energy of these outermost electrons.

These energy levels are also affected by other groups of atoms attached to the molecule, which can change the properties of the electrons in the outermost orbitals.

Once those energy levels are calculated, the researchers can identify reactants that have similar energy levels when the photocatalyst boosts them into an excited state. When the excited states of an alkene and an oxime are closely matched, less energy is required to boost the reaction to its transition state -- the point at which the reaction has enough energy to go forward to form products.

Accurate predictions

After calculating the frontier orbital energies for 16 different alkenes and nine oximes, the researchers used their computational model to predict whether 18 different alkene-oxime pairs would react together to form an azetidine. With the calculations in hand, these predictions can be made in a matter of seconds.

The researchers also modeled a factor that influences the overall yield of the reaction: a measure of how available the carbon atoms in the oxime are to participate in chemical reactions.

The model's predictions suggested that some of these 18 reactions won't occur or won't give a high enough yield. However, the study also showed that a significant number of reactions are correctly predicted to work.

"Based on our model, there's a much wider range of substrates for this azetidine synthesis than people thought before. People didn't really think that all of this was accessible," Kulik says.

Of the 27 combinations that they studied computationally, the researchers tested 18 reactions experimentally, and they found that most of their predictions were accurate. Among the compounds they synthesized were derivatives of two drug compounds that are currently FDA-approved: amoxapine, an antidepressant, and indomethacin, a pain reliever used to treat arthritis.

This computational approach could help pharmaceutical companies predict molecules that will react together to form potentially useful compounds, before spending a lot of money to develop a synthesis that might not work, Kulik says. She and Schindler are continuing to work together on other kinds of novel syntheses, including the formation of compounds with three-membered rings.

"Using photocatalysts to excite substrates is a very active and hot area of development, because people have exhausted what you can do on the ground state or with radical chemistry," Kulik says. "I think this approach is going to have a lot more applications to make molecules that are normally thought of as really challenging to make."

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Story Source:

Materials provided by Massachusetts Institute of Technology . Original written by Anne Trafton. Note: Content may be edited for style and length.

Journal Reference :

• Emily R. Wearing, Yu-Cheng Yeh, Gianmarco G. Terrones, Seren G. Parikh, Ilia Kevlishvili, Heather J. Kulik, Corinna S. Schindler. Visible light–mediated aza Paternò–Büchi reaction of acyclic oximes and alkenes to azetidines . Science , 2024; 384 (6703): 1468 DOI: 10.1126/science.adj6771

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Scientists use computational modeling to guide a difficult chemical synthesis

by Massachusetts Institute of Technology

Researchers from MIT and the University of Michigan have discovered a new way to drive chemical reactions that could generate a wide variety of compounds with desirable pharmaceutical properties.

These compounds, known as azetidines, are characterized by four-membered rings that include nitrogen. Azetidines have traditionally been much more difficult to synthesize than five-membered nitrogen-containing rings, which are found in many FDA-approved drugs.

The reaction that the researchers used to create azetidines is driven by a photocatalyst that excites the molecules from their ground energy state. Using computational models that they developed, the researchers were able to predict compounds that can react with each other to form azetidines using this kind of catalysis.

"Going forward, rather than using a trial-and-error process, people can prescreen compounds and know beforehand which substrates will work and which ones won't," says Heather Kulik, an associate professor of chemistry and chemical engineering at MIT.

Kulik and Corinna Schindler, a professor of chemistry at the University of Michigan, are the senior authors of the study, which appears today in Science . Emily Wearing, recently a graduate student at the University of Michigan, is the lead author of the paper. Other authors include University of Michigan postdoc Yu-Cheng Yeh, MIT graduate student Gianmarco Terrones, University of Michigan graduate student Seren Parikh, and MIT postdoc Ilia Kevlishvili.

Light-driven synthesis

Many naturally occurring molecules, including vitamins, nucleic acids, enzymes and hormones, contain five-membered nitrogen-containing rings, also known as nitrogen heterocycles. These rings are also found in more than half of all FDA-approved small-molecule drugs, including many antibiotics and cancer drugs.

Four-membered nitrogen heterocycles, which are rarely found in nature, also hold potential as drug compounds. However, only a handful of existing drugs, including penicillin, contain four-membered heterocycles, in part because these four-membered rings are much more difficult to synthesize than five-membered heterocycles.

In recent years, Schindler's lab has been working on synthesizing azetidines using light to drive a reaction that combines two precursors, an alkene and an oxime. These reactions require a photocatalyst, which absorbs light and passes the energy to the reactants, making it possible for them to react with each other.

"The catalyst can transfer that energy to another molecule, which moves the molecules into excited states and makes them more reactive. This is a tool that people are starting to use to make it possible to make certain reactions occur that wouldn't normally occur," Kulik says.

Schindler's lab found that while this reaction sometimes worked well, other times it did not, depending on which reactants were used. They enlisted Kulik, an expert in developing computational approaches to modeling chemical reactions, to help them figure out how to predict when these reactions will occur.

The two labs hypothesized that whether a particular alkene and oxime will react together in a photocatalyzed reaction depends on a property known as the frontier orbital energy match. Electrons that surround the nucleus of an atom exist in orbitals, and quantum mechanics can be used to predict the shape and energies of these orbitals. For chemical reactions, the most important electrons are those in the outermost, highest energy ("frontier") orbitals, which are available to react with other molecules.

Kulik and her students used density functional theory, which uses the Schrödinger equation to predict where electrons could be and how much energy they have, to calculate the orbital energy of these outermost electrons.

These energy levels are also affected by other groups of atoms attached to the molecule, which can change the properties of the electrons in the outermost orbitals.

Once those energy levels are calculated, the researchers can identify reactants that have similar energy levels when the photocatalyst boosts them into an excited state. When the excited states of an alkene and an oxime are closely matched, less energy is required to boost the reaction to its transition state—the point at which the reaction has enough energy to go forward to form products.

Accurate predictions

After calculating the frontier orbital energies for 16 different alkenes and nine oximes, the researchers used their computational model to predict whether 18 different alkene-oxime pairs would react together to form an azetidine. With the calculations in hand, these predictions can be made in a matter of seconds.

The researchers also modeled a factor that influences the overall yield of the reaction: a measure of how available the carbon atoms in the oxime are to participate in chemical reactions .

The model's predictions suggested that some of these 18 reactions won't occur or won't give a high enough yield. However, the study also showed that a significant number of reactions are correctly predicted to work.

"Based on our model, there's a much wider range of substrates for this azetidine synthesis than people thought before. People didn't really think that all of this was accessible," Kulik says.

Of the 27 combinations that they studied computationally, the researchers tested 18 reactions experimentally, and they found that most of their predictions were accurate. Among the compounds they synthesized were derivatives of two drug compounds that are currently FDA-approved: amoxapine, an antidepressant, and indomethacin, a pain reliever used to treat arthritis.

This computational approach could help pharmaceutical companies predict molecules that will react together to form potentially useful compounds, before spending a lot of money to develop a synthesis that might not work, Kulik says. She and Schindler are continuing to work together on other kinds of novel syntheses, including the formation of compounds with three-membered rings.

"Using photocatalysts to excite substrates is a very active and hot area of development, because people have exhausted what you can do on the ground state or with radical chemistry," Kulik says. "I think this approach is going to have a lot more applications to make molecules that are normally thought of as really challenging to make."

Journal information: Science

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• 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 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
• Aeronautics and Astronautics Fields (PhD)
• Artificial Intelligence and Decision Making (Course 6-​4)
• Biological Engineering (PhD)
• 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)
• Computation and Cognition (MEng)
• Computational Science and Engineering (PhD)
• Computer Science, Economics, and Data Science (MEng)
• 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)
• 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 and Theater Arts (Course 21M)
• Naval Science (NS)
• Science, Technology, and Society (STS)
• Special Programs
• Supply Chain Management (SCM)
• Urban Studies and Planning (Course 11)
• Women's and Gender Studies (WGS)

Master of Science in Computational Science and Engineering

Restricted Electives

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