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EECS is where the future is invented

Covering the full range of computer, information and energy systems, eecs brings the world’s most brilliant faculty and students together to innovate and explore. from foundational hardware and software systems, to cutting-edge machine learning models and computational methods to address critical societal problems, our work changes the world., latest news.

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Upcoming events, doctoral thesis: efficient consensus and synchronization for distributed systems, eecs special seminar: hao liu, “towards a machine capable of learning everything”, doctoral thesis: neuro-symbolic learning for bilevel robot planning, paul krogmeier – learning symbolic concepts and domain-specific languages, career inspiration series: rich miner, eecs special seminar: geoff ramseyer: scaling the unscalable: solving worst-case contention with better economic mechanisms.

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

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

Center for Computational Science and Engineering

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

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

Department of Electrical Engineering and Computer Science

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

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

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

Institute for Data, Systems, and Society

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

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

Operations Research Center

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

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

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

• Current MIT Graduate Students

Doctoral Programs in Computational Science and Engineering

Application & admission information.

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

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

Applicants to the standalone CSE PhD program are expected to have an undergraduate degree in CSE, applied mathematics, or another field that prepares them for an advanced degree in CSE. Applicants to the Dept-CSE PhD program should have an undergraduate degree in a related core disciplinary area as well as a strong foundation in applied mathematics, physics, or related fields. When completing the MIT CSE graduate application , students are expected to declare which of the two programs they are interested in. Admissions decisions will take into account these declared interests, along with each applicant’s academic background, preparation, and fit to the program they have selected.  All applicants are asked to specify MIT CCSE-affiliated faculty that best match their research interests; applicants to the Dept-CSE PhD program also select the home department(s) that best match. At the discretion of the admissions committee, Dept-CSE PhD applications might also be shared with a home department beyond those designated in the application. CSE PhD admissions decisions are at the sole discretion of CCSE; Dept-CSE PhD admission decisions are conducted jointly between CCSE and the home departments.

Please note: These are both doctoral programs in Computational Science and Engineering; applicants interested in Computer Science must apply to the Department of Electrical Engineering and Computer Science .

Important Dates

September 15: Application Opens December 1: Deadline to apply for admission* December – March: Application review period January – March: Decisions released on rolling basis

*All supplemental materials (e.g., transcripts, test scores, letters of recommendation) must also be received by December 1. Application review begins on that date, and incomplete applications may not be reviewed. Please be sure that your recommenders are aware of this hard deadline, as we do not make exceptions. We also do not allow students to upload/submit material beyond what is required, such as degree certificates, extra recommendations, publications, etc.

A complete electronic CSE application includes the following:

• Three letters of recommendation ;
• Students admitted to the program will be required to supply official transcripts. Discrepancies between unofficial and official transcripts may result in the revocation of the admission offer.
• Statement of objectives (limited to approximately one page) and responses to department-specific prompts for Dept-CSE PhD applicants;
• Official GRE General Test score report , sent to MIT by ETS via institute code 3514 GRE REQUIREMENT WAIVED FOR FALL 2024 ;
• Official IELTS score report sent to MIT by IELTS†  (international applicants from non-English speaking countries only; see below for more information)
• Resume or CV , uploaded in PDF format;
• MIT graduate application fee of $75‡.

‡Application Fee

The MIT graduate application fee of $75.00 is a mandatory requirement set by the Institute payable by credit card. Please visit the MIT Graduate Admission Application Fee Waiver page for information about fee waiver eligibility and instructions.

Please note: CCSE cannot issue fee waivers; email requests for fee waivers sent to [email protected] will not be considered.

Admissions Contact Information

Email: [email protected]

► Current MIT CSE SM Students: Please see the page for Current MIT Graduate Students .

GRE Requirement

GRE REQUIREMENT WAIVED FOR FALL 2024 All applicants are required to take the Graduate Record Examination (GRE) General Aptitude Test. The MIT code for submitting GRE score reports is 3514 (you do not need to list a department code). GRE scores must current; ETS considers scores valid for five years after the testing year in which you tested.

†English Language Proficiency Requirement

The CSE PhD program requires international applicants from non-English speaking countries to take the academic  version of the International English Language Testing System (IELTS).  The IELTS exam measures one’s ability to communicate in English in four major skill areas: listening, reading, writing, and speaking.  A minimum IELTS score of 7 is required for admission.  For more information about the IELTS, and to find out where and how to take the exam, please visit the IELTS web site .

While we will also accept the TOEFL iBT (Test of English as a Foreign Language), we strongly prefer the IELTS. The minimum TOEFL iBT score is 100.

This requirement is waived for those who can demonstrate that one or more of the following are true:

• English is/was the language of instruction in your four-year undergraduate program,
• English is the language of your employer/workplace for at least the last four years,
• English was your language of instruction in both primary and secondary schools.

Degree Requirements for Admission

To be admitted as a regular graduate student, an applicant must have earned a bachelor’s degree or its equivalent from a college, university, or technical school of acceptable standing. Students in their final year of undergraduate study may be admitted on the condition that their bachelor’s degree is awarded before they enroll at MIT.

Applicants without an SM degree may apply to the CSE PhD program, however, the Departments of Aeronautics and Astronautics and Mechanical Engineering nominally require the completion of an SM degree before a student is considered a doctoral candidate. As a result, applicants to those departments holding only a bachelor’s degree are asked in the application to indicate whether they prefer to complete the CSE SM program or an SM through the home department.

Nondiscrimination Policy

The Massachusetts Institute of Technology is committed to the principle of equal opportunity in education and employment.  To read MIT’s most up-to-date nondiscrimination policy, please visit the Reference Publication Office’s nondiscrimination statement page .

Additional Information

For more details, as well as answers to most commonly asked questions regarding the admissions process to individual participating Dept-CSE PhD departments including details on financial support, applicants are referred to the website of the participating department of interest.

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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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PhD Program

Program overview.

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Rigorous, discipline-based research is the hallmark of the MIT Sloan PhD Program. The program is committed to educating scholars who will lead in their fields of research—those with outstanding intellectual skills who will carry forward productive research on the complex organizational, financial, and technological issues that characterize an increasingly competitive and challenging business world.

Start here.

Learn more about the program, how to apply, and find answers to common questions.

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Start Your Application

Visit this section to find important admissions deadlines, along with a link to our application.

Click here for answers to many of the most frequently asked questions.

PhD studies at MIT Sloan are intense and individual in nature, demanding a great deal of time, initiative, and discipline from every candidate. But the rewards of such rigor are tremendous:  MIT Sloan PhD graduates go on to teach and conduct research at the world's most prestigious universities.

PhD Program curriculum at MIT Sloan is organized under the following three academic areas: Behavior & Policy Sciences; Economics, Finance & Accounting; and Management Science. Our nine research groups correspond with one of the academic areas, as noted below.

MIT Sloan PhD Research Groups

Behavioral & policy sciences.

Economic Sociology

Institute for Work & Employment Research

Organization Studies

Technological Innovation, Entrepreneurship & Strategic Management

Economics, Finance & Accounting

Accounting  

Management Science

Information Technology

System Dynamics  

Those interested in a PhD in Operations Research should visit the Operations Research Center .  

PhD Program Structure

Additional information including coursework and thesis requirements.

MIT Sloan Predoctoral Opportunities

MIT Sloan is eager to provide a diverse group of talented students with early-career exposure to research techniques as well as support in considering research career paths.

Rising Scholars Conference

The fourth annual Rising Scholars Conference on October 25 and 26 gathers diverse PhD students from across the country to present their research.

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The goal of the MIT Sloan PhD Program's admissions process is to select a small number of people who are most likely to successfully complete our rigorous and demanding program and then thrive in academic research careers. The admission selection process is highly competitive; we aim for a class size of nineteen students, admitted from a pool of hundreds of applicants.

What We Seek

• Outstanding intellectual ability
• Excellent academic records
• Previous work in disciplines related to the intended area of concentration
• Strong commitment to a career in research

MIT Sloan PhD Program Admissions Requirements Common Questions

Dates and Deadlines

Admissions for 2024 is closed. The next opportunity to apply will be for 2025 admission. The 2025 application will open in September 2024. 

More information on program requirements and application components

Students in good academic standing in our program receive a funding package that includes tuition, medical insurance, and a fellowship stipend and/or TA/RA salary. We also provide a new laptop computer and a conference travel/research budget.

Funding Information

Throughout the year, we organize events that give you a chance to learn more about the program and determine if a PhD in Management is right for you.

PhD Program Events

May phd program overview.

During this webinar, you will hear from the PhD Program team and have the chance to ask questions about the application and admissions process.

June PhD Program Overview

July phd program overview, august phd program overview.

Complete PhD Admissions Event Calendar

Unlike formulaic approaches to training scholars, the PhD Program at MIT Sloan allows students to choose their own adventure and develop a unique scholarly identity. This can be daunting, but students are given a wide range of support along the way - most notably having access to world class faculty and coursework both at MIT and in the broader academic community around Boston.

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Profiles of our current students

MIT Sloan produces top-notch PhDs in management. Immersed in MIT Sloan's distinctive culture, upcoming graduates are poised to innovate in management research and education.

Academic Job Market

Doctoral candidates on the current academic market

Academic Placements

Graduates of the MIT Sloan PhD Program are researching and teaching at top schools around the world.

view recent placements 

MIT Sloan Experience

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The PhD Program is integral to the research of MIT Sloan's world-class faculty. With a reputation as risk-takers who are unafraid to embrace the unconventional, they are engaged in exciting disciplinary and interdisciplinary research that often includes PhD students as key team members.

Research centers across MIT Sloan and MIT provide a rich setting for collaboration and exploration. In addition to exposure to the faculty, PhD students also learn from one another in a creative, supportive research community.

Throughout MIT Sloan's history, our professors have devised theories and fields of study that have had a profound impact on management theory and practice.

From Douglas McGregor's Theory X/Theory Y distinction to Nobel-recognized breakthroughs in finance by Franco Modigliani and in option pricing by Robert Merton and Myron Scholes, MIT Sloan's faculty have been unmatched innovators.

This legacy of innovative thinking and dedication to research impacts every faculty member and filters down to the students who work beside them.

Faculty Links

• Accounting Faculty
• Economic Sociology Faculty
• Finance Faculty
• Information Technology Faculty
• Institute for Work and Employment Research (IWER) Faculty
• Marketing Faculty
• Organization Studies Faculty
• System Dynamics Faculty
• Technological Innovation, Entrepreneurship, and Strategic Management (TIES) Faculty

Student Research

“MIT Sloan PhD training is a transformative experience. The heart of the process is the student’s transition from being a consumer of knowledge to being a producer of knowledge. This involves learning to ask precise, tractable questions and addressing them with creativity and rigor. Hard work is required, but the reward is the incomparable exhilaration one feels from having solved a puzzle that had bedeviled the sharpest minds in the world!” -Ezra Zuckerman Sivan Alvin J. Siteman (1948) Professor of Entrepreneurship

Sample Dissertation Abstracts - These sample Dissertation Abstracts provide examples of the work that our students have chosen to study while in the MIT Sloan PhD Program.

We believe that our doctoral program is the heart of MIT Sloan's research community and that it develops some of the best management researchers in the world. At our annual Doctoral Research Forum, we celebrate the great research that our doctoral students do, and the research community that supports that development process.

The videos of their presentations below showcase the work of our students and will give you insight into the topics they choose to research in the program.

How Should We Measure the Digital Economy?

2020 PhD Doctoral Research Forum Winner - Avinash Collis

Watch more MIT Sloan PhD Program  Doctoral Forum Videos

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Discover & explore

Why choose MIT? Our own graduate students have asked this very same question, and have shared their insights through insightful blog posts . Diverse students explore topics from choosing an advisor to balancing mental health and wellness.

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We’re excited to welcome you to MIT! As you prepare to come to Cambridge, lean on these important updates and helpful resources .

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Graduate School Statement of Purpose

Criteria for success.

Your statement of purpose, sometimes referred to by other names including “statement of objectives” or (rarely) “personal statement” should…

• convince a faculty committee that you are qualified for their program and that you are a good fit for their program’s focus and goals.
• show a select group of skills and experiences that convey your scientific and technical accomplishments and interests.
• include concrete and quantitative experiences, accomplishments, and qualifications.
• adhere to the length and formatting guidelines of the program to which you are applying (usually no more than 2 pages).

Structure Diagram

The graduate school statement of purpose should strengthen your chance of acceptance into a graduate program by demonstrating that you are a good match for the particular department or program. Matching goes both ways: they should be interested in you, and you should be interested in them. Your statement should make this match clear by telling your personal story as a researcher.

Analyze Your Audience

Your statement of purpose is typically read by a graduate committee, a handful of faculty from your program. They’re trying to determine if you will be a successful graduate student in their department, a positive force in the department’s intellectual life, and a successful researcher after you graduate. They are therefore interested in your qualifications as a researcher, your career goals, and how your academic focus matches their labs and department.

The graduate committee could read hundreds of applications a year. To make it easy for them to figure out that you are a good fit, make direct, concrete statements about your accomplishments and qualifications. To make it easy for them to remember you, create a narrative that “brands” you.

Create a personal narrative

PhD programs invest in the professional and technical growth of their students. Get the committee excited about investing in you by opening your essay with a brief portrait of what drives you as a scientist or engineer. What research directions are you passionate about, and why? What do you picture yourself doing in 10 years?

If you’re not quite sure what you want to do in graduate school, it’s still best to focus on a specific area or topic in your application. What would you work on if you had to start graduate school tomorrow? You can always change your research focus later.

Any personal stories should fit in the overall narrative of your research story. Avoid cliche openings like “ever since my childhood, I’ve been fascinated by…”. The statement of purpose, sometimes called a personal statement, is “personal” in the sense that it tells your unique research story, not your life story.  

Close your essay with a 2-3 sentence discussion of your career interests. No one will hold you to these plans; it simply helps your committee visualize your potential trajectory. Emphasize how the program you are applying to will help prepare you for these long-term goals.

Describe your experiences

Experiences are the “what” of your essay. What experiences led you to develop your skill set and passions? Where have you demonstrated accomplishment, leadership, and collaboration? Include research, teaching, and relevant work experience or extracurriculars. State concrete achievements and outcomes like awards, publications, or projects completed.

Achievements do not need to be directly tied to research projects or publications. However, they should relate to the type of work you expect in graduate school. For example, you can discuss significant, research-oriented class projects. Describe any collaborations with senior students or faculty that demonstrate your passion about relevant research topics. Computer science students can discuss projects from software internships that involved architecture design, algorithm design, security considerations, machine learning, etc. These experiences can inform your academic interests and demonstrate ability as well as more formal research experience. Also consider including experiences that demonstrate other skills that are crucial for a successful grad student such as good communication and self-motivation. 

Quantify your experiences to show concrete impact. How many people were on your team? How many protocols did you develop? How many people were in competition for an award? As a TA, how often did you meet with your students?

Describe actions, not just changes in your internal mental or emotional state. A statement of purpose is a way to make a narrative out of your CV. It is not a diary entry

Explain the meaning of your experiences

Meaning is the “why” or “so what” of the document. Why was this experience important to your growth as a researcher? What does it say about your abilities and potential? It feels obvious to you, but you need to be explicit with your audience. Your descriptions of meaning should also act as transition statements between experiences: try to “wrap” meaning around your experiences.

Demonstrate match to your target program

Demonstrate an understanding of the program to which you’re applying and about how you will be successful in that program. To do this:

• Read the program’s website. See what language they use to describe themselves, and echo that language in your essay. For example, the website for MIT’s Computer Science and Artificial Intelligence Laboratory displays three main research areas and the values of the lab .
• In some areas (often those with a more rigid lab structure), reaching faculty is very important. You should know what faculty members are taking new students and whether or not you would be a fit for their lab before applying. In other areas, contacting faculty is not expected and may be difficult. Reach out if in doubt and consult current graduate students, undergraduate professors, or graduate admissions officers if you are unsure what is typical in your area.
• State which professors in the program you would plan to work with. Show how their research areas align with your background and your goals. You can even describe potential research directions or projects. It can also be helpful to mention other departments or groups at the university that you might be interested in collaborating with or learning from.

Content adapted by the MIT Electrical Engineering and Computer Science Communication Lab from an article originally created by the MIT Biological Engineering Communication Lab .

Related Resource

For more information, see these statements from MIT EECS faculty about what they look for in a grad school application.

Resources and Annotated Examples

Annotated example 1.

Personal Statement submitted by graduate student enrolled at MIT in CSAIL 192 KB

Annotated Example 2

Personal Statement submitted by graduate student enrolled at MIT in EECS 292 KB

Annotated Example 3

Personal Statement submitted by graduate student enrolled at MIT in EECS 873 KB

People : Elfar Adalsteinsson

77 Massachusetts Ave. Cambridge, MA 02139

• PhD in Electrical Engineering, Stanford University, 1995
• MS in Electrical Engineering, Stanford University, 1991
• BS in Electrical Engineering, University of Iceland Reykjavik, 1989

Elfar Adalsteinsson, Professor of Electrical Engineering and Computer Science and of MIT’s Division of Health Sciences and Technology, joined the MIT faculty and the Research Laboratory of Electronics in 2004.

Selected Awards/Societies

• College of Fellows Inductee, American Institute for Medical and Biological Engineering, 2016
• International Society of Magnetic Resonance in Medicine (ISMRM)
• Fulbright Fellowship

Selected Publications

• Placental MRI: Developing Accurate Quantitative Measures of Oxygenation. Abaci Turk E, Stout JN, Ha C, Luo J, Gagoski B, Yetisir F, Golland P, Wald LL, Adalsteinsson E, Robinson JN, Roberts DJ, Barth WH Jr, Grant PE. Top Magn Reson Imaging. 2019 Oct;28(5):285-297
• In Vivo Quantification of Placental Insufficiency by BOLD MRI: A Human Study. Luo J, Abaci Turk E, Bibbo C, Gagoski B, Roberts DJ, Vangel M, Tempany-Afdhal CM, Barnewolt C, Estroff J, Palanisamy A, Barth WH, Zera C, Malpica N, Golland P, Adalsteinsson E, Robinson JN, Grant PE. Sci Rep. 2017 Jun 16;7(1):3713.
• The ultimate signal-to-noise ratio in realistic body models. Guérin B, Villena JF, Polimeridis AG, Adalsteinsson E, Daniel L, White JK, Wald LL. Magn Reson Med. 2017 Nov;78(5):1969-1980.

A full list of Professor Adalsteinsson’s publications can be found on his  website .

Courses Taught

• HST 580 /6.556 Data Acquisition and Image Reconstruction in MRI Data Acquisition and Image Reconstruction in MRI
• 6.003 Signal Processing
• 6.03 Introduction to EECS from a Medical Technology Perspective
• 6.011 Signals, Systems & Inference

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• Leadership Components
• Treks & Travel
• Action Learning
• AI & Machine Learning at LGO
• Data Analysis & Analytics
• Manufacturing / Lean Optimization
• Product Design & Development
• Research & Development
• Robotics & Automation
• Supply Chain
• Sustainability
• Systems Optimization
• Aeronautics and Astronautics
• Chemical Engineering
• Civil and Environmental Engineering

Electrical Engineering and Computer Science

• Mechanical Engineering
• Nuclear Science and Engineering
• Operations Research
• Class Profile
• Meet Our Students
• Diversity & Inclusion
• Career Placement Report
• Career Statistics
• Application Directions
• Financial Aid & Costs
• Admissions Events
• LGO Early Admission
• Give to MIT LGO
• Alumni Networking
• Alumni Resources
• Partner Companies
• Current Student Resources

The MIT Electrical Engineering and Computer Science (EECS) department offers an in-depth education in principles built on mathematics, computation, and the physical sciences. Researchers make breakthroughs in a variety of topics, including product design, data science, robotics, and hardware design.

LGO students work with EECS faculty to develop their own curriculum that pairs with their MBA coursework and plays to the student’s strengths and academic interests.

Degree Requirements

LGO students completing the MS in Electrical Engineering and Computer Science complete:

• The required courses in the LGO summer core
• Four engineering courses (usually 3-4 within the department) that show a coherent focus in the discipline. LGO EECS students work with faculty to determine courses that match their goals.
• LGO Internship  incorporating Electrical Engineering and Computer Science and management content, resulting in a dual-degree thesis overseen by the student’s faculty advisor.

Research Areas

LGOs have the flexibility to design their own research focuses within Electrical Engineering and Computer Science (Course 6), with guidance from their department advisor. EECS is organized into three broad research areas where students can pursue specific research focuses:

• Biological and Medical Devices and Systems  
• Electronic, Magnetic, Optical and Quantum Materials and Devices

Integrated Circuits and Systems

• Nanoscale Materials, Devices and Systems
• Algorithms and Theory
• Computational Biology
• Computer Architecture
• Graphics and Vision
• Human-Computer Interaction
• Programing Languages and Software Engineering
• Security and Cryptography
• Systems and Networking

Artificial Intelligence and Machine Learning

• ML and Social Science 
• Natural Language and Speech Processing
• Optimization and Game Theory
• Systems, Theory, Control and Autonomy

Some research areas (for example, Robotics) are interdisciplinary by nature, and span several of these headers. ​Bioinformatics research is also included in Computer Science. A full list of Course 6 classes can be found on the MIT Course Catalog .

Popular research areas among LGOs:

The focus in circuits deals with the design and creation of sophisticated circuits and systems for applications ranging from computation to sensing.

Key Areas of Research: Signal Processing, Communications, and Control; Energy and Power Systems; Circuits and Systems; Digital Design and Computer Architecture; Computer-Aided Design and Numerical Methods

Sample of Classes: 6.1311 Independent Inquiry Power Lab; 6.320 Feedback System Design; 6.321 Solid-State Circuits; 6.345 Automatic Speech Recognition; 6.374 Analysis and Design of Digital Integrated Circuits; 6.631 Optics and Photonics; 6.695; Engineering Economic & Regulation: Electric Power; 6.720 Integrated Microelectronics Devices; 6.943 How to Make Almost Anything

Biological and Medical Devices and Systems

This area focuses on the development of technology and systems that will transform the future of biology and healthcare. Specific areas include biomedical sensors and electronics, nano- and micro-technologies, imaging, and computational modeling of disease.

Key Areas of Research: Cellular and Molecular Engineering; Medical Imaging; Medical Devices and Microsystems; Clinical Inference and Learning in Medicine; Physiological Modeling

Sample of Classes: 6.524 Molecular, Cellular, and Tissue Biomechanics; 6.525 Medical Device Design; 6.521 Cellular Neurophysiology and Computing; 6.555 Biomedical Signal and Image Processing; 6.557 Biomolecular Feedback Systems; 6.872 Biomedical Computing

This research area covers a wide range of topics in this quickly evolving field, advancing how machines learn, predict, and control, while also making them secure, robust and trustworthy. Research covers both the theory and applications of ML. This broad area studies ML theory (algorithms, optimization, etc.); statistical learning (inference, graphical models, causal analysis, etc.); deep learning; reinforcement learning; symbolic reasoning ML systems; as well as diverse hardware implementations of ML.

Key Areas of Research: Knowledge representation and reasoning; Knowledge-based systems; Medical information systems; Machine learning; Natural language processing; Perceptual interfaces and human/computer interaction; Robotics; Speech Understanding; Vision

Sample Classes: 6.844 Artificial Intelligence; 6.854 Advanced Algorithms; 6.860 Statistical Learning Theory; 6.862 Applied Machine Learning; 6.867 Machine Learning; 6.877 Principles of Autonomy & Decision Making; 6.883 Adv Topics in Artificial Intel; 6.884 Adv Topics in Artificial Intel

Systems Theory, Control, and Autonomy

This area focuses on theoretical research including quantification of fundamental capabilities and limitations of feedback systems, inference and control over networks, and development of practical methods and algorithms for decision making under uncertainty.

Key Areas of Research: Distributed computing; Fault-tolerant computing; High performance computing and applications; Operating systems; Parallel systems; Parallel computation; Programming languages and compilers; Software specification Design, and analysis; VLSI architecture and computer systems

Sample of Classes: 6.828 Operating System Engineering; 6.829 Computer Networks; 6.831 User Interface Design; 6.833 Human Intelligence Enterprise; 6.835 Intell Multi User Interfaces; 6.857 Network and Computer Security; 6.869 Advances in Computer Vision; 6.894 Adv Top Graph Human Comp Inter

Internships

Many of LGO’s partner companies are eager to work with LGOs getting their degree through EECS. Depending on what your academic focus is, you can work on a number of fascinating topics.

A few recent EECS internships were at:

• Integration of Advanced Analytics and Engineering Automation Solutions into Target’s End-to-End Supply Chain Modernization and Optimization Transformation
• Transmission System Overvoltage Mitigation Through the Use of Distributed Generation (DG) Smart Inverters
• Improving Project Timelines Using AI / ML To Detect Forecasting Errors

For LGO applicants, the Electrical Engineering and Computer Science department looks for:

• A strong academic background in science or engineering with significant knowledge of electrical engineering or computer science. Previous LGO EECS students have enrolled with degrees in computer science, physics, applied mathematics, biomedical engineering, and electrical engineering. In rare cases, students have a background in another engineering discipline with research and work experience in an EECS field.
• Defined research interests in line with EECS’s department research areas.

Applicants should have a stated interest in Electrical Engineering and Computer Science. Previous work experience in an industrial setting is ideal. Former students came from systems engineering and design engineering roles within firms in the aerospace, IT, electronics, and software industries.

A dual degree in MBA and EECS prepares students for a range of roles in many industries. Recent graduates from LGO EECS work in electronics, software, e-commerce, and web services. Some students go into biotech, research institutes, or consulting. Many work in strategic management, operations management, or project management roles directly after the program.

LGO Careers >

Quantum Photonics & AI Group

Prof. Dirk Englund • Dr. Ryan Hamerly • Dr. Matthew Trusheim • Dr Avinash Kumar • Dr Charles Hsu • Dr Franco Wong

• Publications
• Theses and Presentations
• Collaborators

MEng UROP and SuperUROP VISITING/ASSOCIATED

Dirk Englund

PhD Associate Professor of Electrical Engineering and Computer Science

englund -at- mit -dot- edu

Google Scholar

Dirk Englund received his BS in Physics from Caltech in 2002. Following a Fulbright year at TU Eindhoven, he earned an MS in electrical engineering and a PhD in Applied Physics in 2008, both from Stanford University. He was a postdoctoral fellow at Harvard University until 2010, when he started his group as Assistant Professor of Electrical Engineering and of Applied Physics at Columbia University. In 2013, he joined the faculty of MIT's Department of Electrical Engineering and Computer Science. Dirk's research focuses on quantum technologies based on semiconductor and optical systems. Outside the office, Dirk enjoys doing sports, music, and spending time with family and friends.

PhD (Appl. Physics), Stanford (2008) MS (Electrical Engineering), Stanford BS (Physics), Caltech

Matthew E. Trusheim

PhD Research Scientist

mtrush -at- mit -dot- edu

PhD (EECS) MIT, MS (Appl. Phys.) Columbia, BS (Appl. Phys.) Yale

Ryan Hamerly

rhamerly -at- mit -dot- edu

R.H. was born in San Antonio, Texas in 1988. He graduated from Boulder High School in 2006 and received a B.S. degree from Caltech in 2010, working with Prof. Yanbei Chen on black hole mergers. In 2016 he received a Ph.D. degree in applied physics from Stanford, for work with Prof. Hideo Mabuchi on quantum control, nanophotonics, and nonlinear optics. In 2017 he was at the National Institute of Informatics (Tokyo), working with Prof. Yoshihisa Yamamoto on quantum annealing and optical computing concepts. He is currently an IC postdoctoral fellow at MIT with Prof. Dirk Englund.

PhD (Applied Physics) Stanford (2016). BS (Physics) Caltech (2010)

Program Manager, Research

erikyost -at- mit -dot- edu

Erik Yost is a Grand Canyon University graduate with honors in Business Management. He pioneered numerous microgravity research at NASA AMES and around the world while presenting findings. He then founded GENERATE and the GCU STELLAR program for undergraduates to conduct research on the International Space Station NL. Working with LaBelle Labs, he successfully led the seminal deployment of energy and health research to the ISS. With a PMP certification, he helps the MIT Quantum Photonics Group with research program management. Outside work, Erik enjoys surfing, hiking, and family time.

BS (Grand Canyon University)

Avinash Kumar

avinashk -at- mit -dot- edu

Franco Wong

ncw -at- mit -dot- edu

PhD (Stanford), MS (Stanford), BA, BS (Rochester)

Janice Balzer

Administrator

balzer -at- mit -dot- edu

Adyant Kamdar

Software Engineer

k_adyant -at- mit -dot- edu

BA (Physics, minor EECS), UC Berkeley

Anders Khaykin

Research Engineer

akhaykin -at- mit -dot- edu

BSc (Materials Science & Engineering & Economics) MIT 2021

Hyeongrak Choi

PhD Postdoctoral

choihr -at- mit -dot- edu

M.S. (Electrical Engineering), MIT (2017); B.S. (Electrical engineering), Seoul National University (2014)

Adrian Menssen

amenssen -at- mit -dot- edu

Adrian Menssen's research focuses on programming atomic systems for quantum computing and simulation using large-scale photonic circuits.

BS (Physics) Goethe Univ., Ger 2011, M.Sc (Physics) Goethe Univ., Ger 2014, DPhil (Physics) Univ. of Oxford, UK 2019

Sivan Trajtenberg-Mills

sivantra -at- mit -dot- edu

Sivan Trajtenberg Mills recieved her BsC in Physics and Computer science from Tel Aviv Univeristy (TAU). She holds a PhD in Physics from TAU in the field of nonlinear optics, supervised by prof. Ady Arie. She was an officer in the Israeli defence forces in the field of cyber-security, and continued instructing high-school students in cyber related academic programs throughout her PhD. She has been active in promoting women in STEM. Outside the lab, she was a second-league vollyball player and referee, as well as part of a semi-proffesional classical Choir.

B.Sc. (Physics and computer science), Tel Aviv University, Israel Ph.D (Physics) Tel Aviv University, Israel

Bevin Huang

huangbev -at- mit -dot- edu

Ph.D. (Physics) University of Washington (2020)

Jawaher Almutlaq

jawaher -at- mit -dot- edu

Jawaher received her B.S. in Materials Science and Engineering from The Pennsylvania State University in 2015. Her thesis was on the additive manufacturing of metals for the next generation of spaceships as the recipient of The NASA Pennsylvania Space Grant Consortium scholarship. In 2015, she joined King Abdullah University of Science and Technology (KAUST) where she worked under the supervision of Prof. Osman Bakr. Her research focused on investigating the synthesis, structural and optical properties of perovskites. For her work, she was awarded a PhD degree in 2020. During her PhD, she was a visiting researcher at the University of Cambridge, where she worked on yttrium barium copper oxide (YBCO) superconductors. She Received the Ibn Khaldun Postdoctoral Fellowship in 2021, and the Ibn Rushd Postdoctoral Fellowship award in 2022. In Quantum Photonics Group, she works on quantum materials, focusing on the 2D materials for quantum technoloy. Outside of work, she is an avid tea drinker and she enjoys stargazing and practicing archery.

Ph.D (Materials Science & Engineering), KAUST, 2020

Sri Krishna Vadlamani

srikv -at- mit -dot- edu

Sri received a B.Tech. (with honors) in Electrical Engineering, with a minor in Physics, from Indian Institute of Technology Bombay (IIT Bombay), India, in 2016. In 2021, he obtained a Ph.D. in Electrical Engineering and Computer Sciences, under the supervision of Prof. Eli Yablonovitch, from the University of California, Berkeley. His Ph.D. work explored spectroscopic lineshape theory for tunnel transistors, and physics-based combinatorial optimization solvers.

B.Tech. (Electrical Engineering) IIT Bombay, India, 2016. Ph.D. (EECS) UC Berkeley, 2021.

Valeria Saggio

vsaggio -at- mit -dot- edu

Valeria Saggio received her Ph.D. at the University of Vienna (Austria) in 2021, where she worked on entanglement detection in photonic cluster states as well as on applications of quantum mechanics to reinforcement learning. She carried out her Master thesis at the University of Florence (Italy) and did an internship at the Queen's University Belfast (UK) during her studies at the University of Catania (Italy), where she obtained her B.A. and M.S. in Physics. She is currently a Postdoctoral Associate at the Quantum Photonics Laboratory, MIT.

Ph.D. (Physics), University of Vienna, Austria (2021)

Ethan Arnault

earnault -at- mit -dot- edu

Ethan received his BA in Physics from Cornell University in 2016. Afterwards, he attended a PhD in experimental condensed matter physics under Prof. Gleb Finkelstein at Duke University. While there, he studied nanoelectronic devices looking at the interplay of superconductivity with graphene heterostructures. In particular, studying multiterminal Josephson junctions and superconducting effects in the quantum Hall regime. For this work, he was awarded a PhD and concurrent MS in ECE in May 2022. His current work is on utilizing 2D materials for quantum sensors.

PhD Physics, MS ECE, Duke University (2022), BA Physics Cornell University (2016)

yonghu -at- mit -dot- edu

Yong Hu received his B.S. in Material Physics from Hefei University of Technology in 2014 and his M.S. in Condensed Matter Physics from Nanjing University in 2017. He earned a Ph.D. degree in Mechanical Engineering from University at Buffalo in 2022. He is a recipient of the 2022 MMM-INTERMAG Best Student Presentation Finalist; 2021 Materials Research Society (MRS) Graduate Student Awards; 2021 MRS Science as Art Competition Award, and 2020 Dean’s Graduate Achievement Award. His current research focuses on low-dimensional quantum material devices, including quantum emitters in 2D materials and graphene plasmonics for nonlinear frequency conversion and photodetection.

B.S. (Material Physics) Hefei University of Technology 2014, M.S. (Condensed Matter Physics) Nanjing University 2017, Ph.D. (Mechanical Engineering) 2022.

lichao -at- mit -dot- edu

Chao Li earned his BS with the highest honor from Jilin University in 2016. He continued his education at Georgia Institute of Technology, where he received his Ph.D. in 2022. His doctoral research focused on developing chip-scale atomic beam technologies and devices for timekeeping and sensing applications. This work earned him the 2023 APS Outstanding Doctoral Thesis Research in Beam Physics Award. Currently, he is a postdoc at MIT RLE, where he is exploring the use of large-scale photonic integrated circuits for fast and coherent qubit control.

B.S. (Physics), Jilin University 2016; Ph.D. (Physics), Georgia Institute of Technology 2022

Camille Papon

cpapon -at- mit -dot- edu

Camille obtained her Ph.D. degree from the Niels Bohr Institute in 2023 (NBI, Denmark) . She has an extensive experience in the nanofabrication of photonic devices and their use for quantum information applications. Under the guidance of Assoc. Prof. Leonardo Midolo and Prof. Peter Lodahl she implemented various quantum hardware, including nanomechanical single-photon routers and photonic circuits with multiple emitters, for the scalable operation of deterministic single-photon sources. In the Quantum Photonics Group at MIT, she works on interfacing atom-like defects in silicon with nanophotonic structures toward their use in quantum technologies.

B.S. (Physics), University of Copenhagen M.S. (Quantum Physics), University of Copenhagen Ph.D. (Quantum Photonics), University of Copenhagen

Gu received his BA, M.Sci from the University of Cambridge. He later joined Mete Atature's group working on optically addressable spin defects in diamond and hBN for quantum sensing and communication. At MIT he works on identifying color centers in silicon.

BA and M.Sci, University of Cambridge, 2017 PhD, University of Cambridge, 2022

bohanwu -at- mit -dot- edu

Bo-Han Wu received his BS degree in National Chiao Tung University (Electrophysics), MS degree in National Tsing Hua University (Physics), and PhD degree in the University of Arizona, majoring in Physics and minoring in Optical science. During his PhD study, he designed, fabricated and tested the photonic chip to generate the continuous-variable (CV) entangled photons; further, he proposed a theoretical protocol of CV quantum repeater to distribute long-distance entanglement and a theoretical quantum radar scheme to interrogate the direction of an distant unknown object. In MIT, he will theoretically develope the quantum sensing protocol empowered by machine learning tasks and will participate the experiments of large-scale photonic chip for atomic system.

B.S. (Electrophysics) National Chiao Tung University; M.S. (Physics) National Tsing Hua University; Ph. D. (Physics/Optical Science) The University of Arizona

chaoluan -at- mit -dot- edu

B.S. (Physics), University of Jinan (2014) M.S. (Optical Engineering), Shandong University (2017) Ph.D. (Silicon Photonics), Technical University of Denmark (2022)

Ian Berkman

iberkman -at- mit -dot- edu

B.S. (Physics), Leiden University, 2015. M.S. (Applied Physics), Delft University of Technology, 2018. Ph.D. (Quantum computing/communication), University of New South Wales, 2023.

Saumil Bandyopadhyay

PhD Visiting

saumilb -at- mit -dot- edu

Saumil received his S.B., M.Eng., and Ph.D. in Electrical Engineering from MIT. His research centers on developing energy-efficient chip-scale photonics for communication and computing, and his doctoral thesis focused on the design and experimental demonstration of end-to-end, monolithically integrated silicon photonic processors for deep neural networks. Prior to his doctoral work, Saumil worked with multiple optical communications teams across the industry, including internships with the Facebook Connectivity Lab and Network Hardware Engineering Teams (the latter in collaboration with the Telecom Infra Project), and most recently as a Senior Photonics Engineer on the Photonic Design Team at Elenion Technologies. His work has previously been recognized with a Smithsonian American Ingenuity Award and a National Science Foundation (NSF) Graduate Research Fellowship. Saumil recently joined the Physics and Informatics Laboratories at NTT Research and concurrently holds a joint appointment as a visiting scientist with the Quantum Photonics & AI Group at MIT.

PhD, Electrical Engineering, MIT (2023) | MEng, Electrical Engineering and Computer Science, MIT (2018) | SB, Electrical Science and Engineering, MIT (2017)

Mahdi Mazaheri

Kfir Sulimany

kfir -at- mit -dot- edu

Ph.D. ,Hebrew University of Jerusalem, Israel

Lingling Fan

linglingfan -at- csail -dot- mit -dot- edu

Dr. Lingling Fan received a Ph.D. degree in Electrical Engineering at Stanford University in 2023. Prior to Stanford, she did her undergraduate research at Yale University, Applied Physics. She was a research intern at Google LLC in 2022 summer. She has published over 30 papers in peer-reviewed journals and conference venues, including Nature Portfolio, American Association for the Advancement of Science, American Physical Society, Optical Society of America, and American Chemical Society. Her research has translated to two U.S. patents and influenced emerging industry products in Google. She is a recipient of an Engineering Fellowship from Stanford University (2018), a CLEO presenter award (2020), a DARE fellowship finalist (2021), an EECS Rising Star award (2022), and a travel grant from ACM SIGCOMM (2023).

Ph.D. (Electrical Engineering), Stanford University

dingq30 -at- mit -dot- edu

Ph.D. (EE), ETH Zurich

Kenaish Alqubaisi

kenaish -at- mit -dot- edu

Kenaish Al Qubaisi received a Ph.D. degree in electrical engineering from Boston University in 2023 for his work with Prof. Miloš Popović on the design and demonstration of novel passive and active photonic devices and circuits in monolithic electronics-photonics CMOS platforms. In 2015, he obtained a Master's in Microsystems Engineering from the Masdar Institute of Science and Technology. Under Prof. Anatol Khilo’s guidance, Kenaish explored modeling of nonlinear effects in silicon microring resonators and the design of high-order integrated optical filters resilient against wavelength-dependent variations. He was invited to join Prof. Erich Ippen’s lab at MIT in 2014 as a visiting researcher to experimentally characterize integrated ultra‑fast pulse shapers. Kenaish received a B.Sc. degree in electrical engineering from Purdue University in 2013, where he undertook a year-long senior design project as the lead telecommunication system engineer for a miniature satellite (cubesat) named PurdueSAT.

Ph.D. (EECS), Boston University

Ian Christen

Graduate student

ichr -at- mit -dot- edu

Ian received his BS in Physics and Mathematics from the University of Washington. There, he worked with Professor Kai-Mei Fu on nitrogen-vacancy center quantum computation. Ian will continue similar work in the Quantum Photonics Lab. Ian runs kinda fast.

B.S. (Math, Physics) University of Washington (2017)

Isaac B. Harris

ibwharri -at- mit -dot- edu

B.A.Sc. (Nanotechnology Engineering) University of Waterloo, Canada (2018)

Liane Bernstein

lbern -at- mit -dot- edu

Liane received her B. Eng. in Engineering Physics from Polytechnique Montreal in 2016, specializing in Photonics. In 2018, she was awarded an M.S. in Electrical Engineering and Computer Science at MIT for “Ultrahigh-Resolution, Deep-Penetration Spectral-Domain Optical Coherence Tomography” in Prof. Andy Yun’s group. For her PhD work in the Quantum Photonics and AI Group at MIT, Liane transitioned to optical computing, where she developed optical hardware to improve the speed and energy efficiency of deep learning. Outside the lab, Liane loves to rock climb and play the flute. Liane is a recipient of the Order of the White Rose scholarship (2016), FRQNT Doctoral Fellowship (2016-2018), and NSERC Postgraduate Scholarship (2018-2021). BEng (Engineering Physics), Polytechnique Montreal (2016) MS (Electrical Engineering and Computer Science), MIT (2018)

BEng (Engineering Physics), Polytechnique Montreal (2016) MS (Electrical Engineering and Computer Science), MIT (2018)

Ronald Davis

radavis4 -at- mit -dot- edu

Hugo Larocque

hlarocqu -at- mit -dot- edu

Hugo completed his B.Sc. and M.Sc. in physics at the University of Ottawa where he worked with Ebrahim Karimi and Robert Boyd on generating and characterizing topologically structured waves. The methods that he developed there involve applications in several fields including quantum cryptography, electron microscopy, materials science, nonlinear optics, and fundamental physics. Hugo joined the Quantum Photonics Lab as a Graduate student in 2018. His research focuses on developing large-scale photonic integrated circuits with devices designed to host nonlinear interactions, multimode interference processes, andheterogeneously integrated quantum emitters. He is a recipient of the 2019 NSERC PGSD and 2020 NSF QISE-NET awards.

B.Sc. (Physics) University of Ottawa, Canada (2016). M.Sc. (Physics) University of Ottawa, Canada (2018).

linsenli -at- mit -dot- edu

Linsen Li received his BS in Microelectronics from Tsinghua University in 2019. He was a gold medal winner in the 16th Asian Physics Olympiad in 2015. As an undergraduate, he was awarded the Tsinghua Presidential Award, the highest honor in Tsinghua University. He has pursued the advanced curriculum and undertaken several research projects during his undergraduate period at Tsinghua University, MIT, and Stanford University. Linsen Li is a recipient of the Analog Devices Fellowship in MIT EECS. His research interests include Advanced modeling and computing, Nanotechnology and Quantum Technology. Outside of work, he enjoys swimming, skiing, and golf.

BS (Microelectronics) Tsinghua University, China 2019

Yuqin Duan (Sophia)

sophiayd -at- mit -dot- edu

Sophia received a B.S. in Electrical and Computer Engineering from Purdue University in 2019, where she worked on 2D ferroelectric material and p-bits simulation with Prof. Peide Ye and Supryio Datta. Later on, she spent a year exploring micro-robotics at Prof. Robert Wood lab, where she worked on designing robust and power-autonomous micro-bee. Inspired by the beauty of nature, Sophia joined the Quantum Photonics Lab at MIT to work on vertical cavity with artificial atom array. She is a recipient of Edwin Webster Fellowship. Outside of the lab, Sophia is a traditional/contemporary dance choreographer at MIT ADT, a classic musician, and renaissance art and Assyriology enthusiast.

B.S. (EE) Purdue University, 2019. S.M. (EECS) MIT, 2021

mgd -at- mit -dot- edu

Marc received his B.A. in Physics and Computer Science from UC Berkeley, where he worked in Prof. Irfan Siddiqi's lab on quantum gate synthesis and circuit optimization techniques for NISQ quantum circuits. Now Marc works on quantum circuit optimization techniques for a variety of different scenarios, such as fault-tolerant quantum computing and distributed quantum circuits.

B.A. (CS and Physics) UC Berkeley 2020, S.M (EECS) MIT 2023

Hamza Raniwala

raniwala -at- mit -dot- edu

Hamza received his BS in Applied Physics from Caltech in 2020. There, he worked in the lab of Dr. Hyuck Choo on a nanophotonic sensor implant for monitoring intraocular pressure, and later in the lab of Professor Oskar Painter on coherent, piezoelectric-based microwave-to-optical transduction. For his senior thesis, Hamza continued working with Professor Painter on vertical nanogap capacitors for low TLS density superconducting transmon qubits. Hamza joined the Quantum Photonics Laboratory in 2020 and is currently researching spin-phonon interfaces for quantum computing and networking applications. Hamza is a recipient of a NSF Graduate Research Fellowship and will be an NDSEG fellow in the fall.

B.S. (Applied Physics) California Institute of Technology 2020

Hanfeng Wang

hanfengw -at- mit -dot- edu

B.S. (Applied Physics), USTC, 2020

Thomas Propson

tpropson -at- mit -dot- edu

Thomas received a BA in Physics from the University of Chicago in 2021. There, under the guidance of Fred Chong, he developed techniques for improving the performance of quantum algorithms on NISQ devices. He completed his senior thesis in the lab of David Schuster, where he developed a numerical optimization protocol for making quantum gates robust to control imperfections and applied it to fluxonium-type superconducting circuits. For his undergraduate work, Thomas was awarded the Barry Goldwater Scholarship and a full-tuition scholarship for his senior year from the UChicago Physics Department. Following graduation, he joined the Quantum Photonics Group at MIT as a Ph.D. student with the support of the NSFGRFP and the MIT Jacobs Presidential Fellowship. At MIT, Thomas is excited to develop photonic technologies for controlling quantum devices.

B.A. (Physics) University of Chicago, 2021

Reggie Wilcox

rwilcox -at- mit -dot- edu

Cole Brabec

cbrabec -at- mit -dot- edu

B.S. (Electrical Engineering), Caltech, 2021

Alessandro Buzzi

MSc Graduate student

abuzzi -at- mit -dot- edu

Alessandro Buzzi received his M.Sc. in Electrical Engineering from Politecnico di Torino in 2022, as part of a joint degree between Politecnico di Torino, Institut Polytechnique de Grenoble (INPG Phelma), and Ecole Polytechnique Fédérale de Lausanne (EPFL) focused on micro and nanotechnologies for integrated systems. He conducted research in a variety of fields ranging from computational electromagnetics (in Politecnico di Torino) and nanomagnetism (in Politecnico di Milano). In 2022, he joined the Quantum Nanostructures and Nanofabrication group at MIT for his Master’s thesis on the development of superconducting electronics. From January 2023, he joined the Quantum Photonics Laboratory, focusing on color centers in silicon and heterogenous integration for photonic integrated circuits.

MSc (Electrical Engineering) Politecnico di Torino, Italy (2022)

Yin Min Goh

gohymin -at- mit -dot- edu

B.S. (Physics), University of Michigan (2022)

Maxim Sirotin

sirotin -at- mit -dot- edu

Maxim Sirotin received his MSc and BSc in physics from Lomonosov Moscow State University, where, under the guidance of Prof. Andrey Fedyanin, he was developing laser interferometric imaging methods for biomechanics and nanophotonics. As his master's thesis, he created a method of ultrafast optical coherence microscopy. While working in the group of Prof. Peter Hommelhoff (FAU and MPL, Germany) Maxim was developing the theoretical foundations of free-electron quantum optics under the influence of quantum recoil effect. Maxim joined the Quantum Photonics Group at MIT as a PhD student in September 2023 to work on quantum networks with tin-vacancy centers in diamond.

MSc and BSc (Physics), Lomonosov Moscow State University (2022)

Prahlad Iyengar

prahbi90 -at- mit -dot- edu

BSc (Electrical Engineering)

Marc Bacvanski

marcbac -at- mit -dot- edu

B.S. (Computer Science), Northeastern University (2023)

Louis Follet

louisf13 -at- mit -dot- edu

B.S. and M.S. (Physics) Ecole Normale Supérieure Paris-Saclay (2021 & 2023) B.S. and M.S. (Engineering) Institut d'Optique (2021 & 2023)

Sofia Patomaki

Postdoctoral

patomaki -at- mit -dot- edu

Ph.D. (physics) University College London

VISITING/ASSOCIATED

Odiel Hooybergs

odiel -at- mit -dot- edu

Odiel is an ETH Quantum Engineering student, passionate about quantum information processing. During his Semester Project and Master Thesis he got introduced to the field of microwave-optical quantum transduction, which will play an import role in the future quantum internet and the upscaling of quantum computers based on superconducting circuits. Currently he is expanding his experience in experimental quantum physics during a research internship on color centers in silicon. During the first six months he built the measurement setup in Carlos Errando Herranz’s new group at TU Delft in collaboration with the group of Ronald Hanson. Now he is using a similar setup for the investigation of G-centers in photonic integrated circuits in the group of Dirk Englund at MIT. He is also working on a theoretical project about driven quantum matter based on group IV color centers in diamond with strain control.

B.S. Engineering Physics (Ghent University), M.S. Quantum Engineering (ETH Zurich)

shuang_wu -at- honda-ri -dot- com

markdong -at- mit -dot- edu

Mark earned his B.S. from Cornell University and his M.S. and Ph.D. from the University of Michigan in EECS. His research, both theoretical and experimental, covers a broad range of topics including nonlinear optics in fibers, semiconductor laser physics, device fabrication, and optical frequency comb metrology. He has received several Rackham research awards from UM, a Graduate Student Instructor of the Year award, and was a finalist for the Carl E. Anderson Dissertation Award. Currently, he is working in a collaboration between MITRE and MIT on quantum information processing in photonic integrated circuits.

B.S. (Applied Physics & EE) Cornell University 2012, M.S. (EECS) University of Michigan 2013, Ph.D. (EECS) University of Michigan 2018

clarkg -at- mit -dot- edu

Pratyush Anand

anand43 -at- mit -dot- edu

MSc (Physics); BTech (Engineering Physics)

yhgao -at- mit -dot- edu

Dashiell Vitullo

PhD (Physics), University of Oregon (2016), BA (Physics) Reed College (2007)

Mustafa Yucel

yucelm -at- mit -dot- edu

Mustafa Yucel received his B.Sc. in Electrical Engineering from INPG-Phelma in 2020 and his MSc in Electrical Engineering from INPG Phelma, Grenoble France, the Politecnico di Torino, Turin, Italy, and Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland in 2022. In 2021 he worked during his 3 months internship on the guided interactions with rare-earth (Tm:GaN) luminescent centers for quantum information processing at the CNRS, Néel Institute, Grenoble. He joined QPG-MIT for his Master thesis to work on Large Scale Photonics Integrated Circuits for Quantum Information Processing with Artificial Atoms. Currently he is working on CMOS-Integrated Large-Scale Optical Neural Transceivers as research assistant.

BSc (Mathematics, Physics and Electronics) INPG-Phelma and MSc (EECS) INPG-Phelma, Politecnico di Torino & EPFL

Stefan Krastanov

PhD Professor

stefankr -at- mit -dot- edu

Stefan's research interests center on the control and calibration of near term quantum hardware, with the occasional use of machine learning techniques towards that goal. He obtained his doctorate in physics in Liang Jiang's group at the Yale Quantum Institute and his undergraduate degree at ENS, Lyon, France. He spends much of his free time on hobby electronics and STEM outreach, frequently mixing the two by helping middleschoolers build pretty light-emitting gadgets and learn about the physics behind the contraption.

M (Physics) ENS Lyon, PhD (Physics) Yale

Andrew Greenspon

agreensp -at- mit -dot- edu

PhD (Applied Physics) Harvard University 2021

Research Scientist

PhD (Phyiscs) University of Maryland 2021, BA (Physics) Cornell University 2016

Etienne Corminboeuf

etiennec -at- mit -dot- edu

Etienne Corminboeuf holds a BSc in Physics from ETH Zurich, where he is also pursuing his MSc degree. He is currently working on his MSc thesis in the group of Prof. Dirk Englund at MIT. Previously, he was a Research Assistant at Polariton Technologies - a spinoff out of the group of Prof. Leuthold at ETH Zurich - focusing on automated measurements for electro-optic devices. He also worked as an Atmospheric Scientist for Orbio Earth on the detection and quantification of anomalous methane emissions using satellite imagery.

BSc (Physics), ETH Zurich

Zhizhen Zhong

zhizhenz -at- mit -dot- edu

Zhizhen Zhong is a postdoctoral researcher at MIT CSAIL. His research work focuses on the intersection of networked systems and photonics/optoelectronics to build the next-generation reconfigurable computer networks and high-performance networked computing infrastructures. Before joining MIT, he was a visiting researcher at Meta (Facebook). He received his Ph.D. in Electronic Engineering from Tsinghua University in July 2019. His Ph.D. thesis is on "Traffic-Driven Self-Adaptive Optical Networking" that designs reconfigurable optical networks to adapt to spatiotemporal traffic demand dynamics for high throughput and low latency. He was a visiting Ph.D student at the University of California Davis. He received his Bachelor's Degree of Engineering and secondary Bachelor's Degree of Economics from Tsinghua University in 2014 and 2016, respectively.

PhD (Electronic Engineering), Tsinghua; BEng (Electronic Engineering), Tsinghua

Helaman Flores

Undergraduate

floresh2 -at- mit -dot- edu

Helaman Flores is an undergraduate researcher from Brigham Young University. His research focuses on coupling to resonant cavities in diamond and strain tuning of diamond structures, with experience in FDTD and FEM simulation software.

niv -at- mit -dot- edu

BSc, MSc (Applied Physics)

Danielle Braje

Dr LL supervisor

Scott Hamilton

High school

Jasvith Raj Basani

f20170374 -at- hyderabad -dot- bits-pilani -dot- ac -dot- in

Jeffrey Wang

Andy Dienes

Matthew Feldman

mfeldman -at- mit -dot- edu

Mario Acevedo-Portela

Vering, Julius

Lidholm, Johanna

Darwin Cordovilla

Maarten Degen, [email protected]

leeyuan -at- mit -dot- edu

Jared Cochrane

Grecia Castelazo

Lamia Ateshian

ateshian -at- mit -dot- edu

Uttara Chakraborty

uttara -at- mit -dot- edu

Madison Sutula

mmsutula -at- mit -dot- edu

haz -at- mit -dot- edu

UROP and SuperUROP

Charles Hsu

PhD Research Engineer

memstar -at- mit -dot- edu

Mikkel Heuck

mheuck -at- mit -dot- edu

Dalia Ornelas-Huerta

daliaoh -at- mit -dot- edu

Lorenzo De Santis

ldesanti -at- mit -dot- edu

Jordan Goldstein

PhD in EECS, MIT Graduate student

jordango -at- mit -dot- edu

Hyowon Moon

hwmoon -at- mit -dot- edu

Evan D. Walsh

PhD in Physics, Harvard University Graduate student

evanwalsh -at- seas -dot- harvard -dot- edu

Noel H. Wan

PhD Graduate student

noelwan -at- mit -dot- edu

cpeng -at- mit -dot- edu

Tsung-Ju Lu

tsungjul -at- mit -dot- edu

Christopher Foy

cfoy3 -at- mit -dot- edu

Eric Bersin

ebersin -at- mit -dot- edu

Gregory R. Steinbrecher

gstein -at- mit -dot- edu

Michael Walsh

mpwalsh -at- mit -dot- edu

Donggyu Kim

donggyu -at- mit -dot- edu

ruitang -at- mit -dot- edu

ychen123 -at- mit -dot- edu

Christopher Panuski

cpanuski -at- mit -dot- edu

Theodor Isacsson

isacsson -at- mit -dot- edu

Hannah Clevenson

Alexander Lukin

alukin -at- mit -dot- edu

Darius Bunandar

dariusb -at- mit -dot- edu

Nicholas C. Harris

n_h -at- mit -dot- edu

Amir Karamlou

karamlou -at- mit -dot- edu

Dmitri K. Efetov

defetov -at- mit -dot- edu

Ren-Jye Shiue

tedshiue -at- mit -dot- edu

Lukas Mennel

mennel -at- mit -dot- edu

Florian Dolde

f -dot- dolde -at- googlemail -dot- com

Diploma (Physics) Uni Stuttgart Dr. rer. nat. Uni Stuttgart

Margaret Pavlovich

Marisa Sotolongo

Prashanta Kharel

B.S. (Applied Physics) Columbia 2013, B.S. (Physics) Sewanee

Catherine Lee

cath -at- mit -dot- edu

Tamara Dordevic

PhD Undergraduate

tamara -dot- djordjevicc -at- gmail -dot- com

B.S. (Physics) MIT 2015

Rishi Patel

Ophir Gaathon

Jonathan Hodges

luozhou -dot- li -at- gmail -dot- com

MS (Electrical Engineering), Columbia University (2011) BS (Optoelectronics), Huazhong University of Science and Technology (2009)

Jacob Mower

jcmower -at- gmail -dot- com

B.S. (Physics), Stevens Institute of Technology, M.S. (Electrical Engineering), Columbia, PhD (EECS) MIT

Anubhav Sinha

anusinha -at- mit -dot- edu

Tim Schröder

schroder -at- mit -dot- edu

Maarten Degen

PhD Diploma/Masters

mjdegen -at- mit -dot- edu

Sinan Karaveli

karaveli -at- mit -dot- edu

Edward H. Chen

ehchen -at- mit -dot- edu

mpant -at- mit -dot- edu

Kwang-Yong Jeong

kyjeong -at- mit -dot- edu

Jiabao Zheng

jiabao -at- mit -dot- edu

Sara L. Mouradian

smouradi -at- mit -dot- edu

Gabriele Grosso

ggrosso -at- mit -dot- edu

Jacques Carolan

carolanj -at- mit -dot- edu

Frédéric Peyskens

fpeysken -at- mit -dot- edu

Chitraleema Chakraborty

cchakrab -at- mit -dot- edu

Zaijun Chen

chenzj -at- mit -dot- edu

Erik Eisenach

eisenach -at- mit -dot- edu

whdai -at- mit -dot- edu

lbkim -at- mit -dot- edu

Mihika Prabhu

mihika -at- mit -dot- edu

Carlos Errando Herranz

BS&MS (Electrical Engineering) UPV Valencia, Spain; PhD (Micro and Nanosystems) KTH Stockholm, Sweden

Alex Sludds

B.S. (Electrical Engineering and Computer Science) MIT (2018), M.Eng (Electrical Engineering and Computer Science) MIT 2019

Artur Hermans

BS (Engineering: Electronics and Information Technology) Vrije Universiteit Brussel, Belgium (2012); European MS (Photonics) Ghent University, Belgium, Vrije Universiteit Brussel, Belgium, University of St Andrews, UK (2014); PhD (Photonics Engineering) Ghent University, Belgium (2019).

B.S. (Applied Physics) Caltech (2017), S.M. (EECS) MIT (2019)

Mohamed ElKabbash

LL.B. Alexandria Law School (2007), Masters (Law & political economy) Alexandria Law School (2009), B.A. (physics and economics) Illinois Wesleyan University (2012), Ph.D. (physics) (2017).

MEng of Masters thesis as reader

PhD as Reader [automation]

Jelena Notaros

Won Kyu Calvin Sun

Akshay Agarwal

Mohamed Ibrahim

Charles Roques-Carmes

Matthias Ginterseder

Samuel Gyger, EECS

Wenjie Yao, EECS

Matteo Castellani

Bobak T. Kiani

Nobakht, Jahangir

Castelazo, Grecia

Marco Colangelo

MIT Department of Biological Engineering

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Domitilla Del Vecchio, PhD

Lab Website: 

Email: , office: , phone: , administrative assistant: , research-at-a-glance: , biography: .

Domitilla Del Vecchio received the Ph. D. degree in Control and Dynamical Systems from the California Institute of Technology, Pasadena, and the Laurea degree in Electrical Engineering (Automation) from the University of Rome at Tor Vergata in 2005 and 1999, respectively. From 2006 to 2010, she was an Assistant Professor in the Department of Electrical Engineering and Computer Science and in the Center for Computational Medicine and Bioinformatics at the University of Michigan, Ann Arbor. In 2010, she joined Department of Mechanical Engineering at the Massachusetts Institute of Technology (MIT), where she is currently an Full Professor and member of the Synthetic Biology Center. She is a recipient of the 2016 Bose Research Award (MIT), the Donald P. Eckman Award from the American Automatic Control Council (2010), the NSF Career Award (2007), the American Control Conference Best Student Paper Award (2004), and the Bank of Italy Fellowship (2000).

Research: 

Del Vecchio’s group focuses on model-based analysis, design, and control of biomolecular networks in living cells. The objective is to make the engineering of synthetic genetic circuits in living organisms more robust to context, modular, and hence more scalable. Target applications include multiplexed biosensing in bacteria, controlling chromatin state and gene expression against silencing in mammalian cells, and engineering epigenetic cellular memories. Our approach is grounded on rigorous mathematical analysis of physics-based models of biological network dynamics, on control-theoretic tools for design, and on theory-educated experiments in living cells, from bacterial to mammalian.

Research Areas: 

• Synthetic Biology
• Systems Biology

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Engineering In Action

Unlocking mRNA’s cancer-fighting potential

MIT spinout Strand Therapeutics has developed a new class of mRNA molecules that can sense where they are in the body, for more targeted and powerful treatments.

What if training your immune system to attack cancer cells was as easy as training it to fight Covid-19? Many people believe the technology behind some Covid-19 vaccines, messenger RNA, holds great promise for stimulating immune responses to cancer.

But using messenger RNA, or mRNA, to get the immune system to mount a prolonged and aggressive attack on cancer cells — while leaving healthy cells alone — has been a major challenge.

The MIT spinout Strand Therapeutics is attempting to solve that problem with an advanced class of mRNA molecules that are designed to sense what type of cells they encounter in the body and to express therapeutic proteins only once they have entered diseased cells.

“It’s about finding ways to deal with the signal-to-noise ratio, the signal being expression in the target tissue and the noise being expression in the non-target tissue,” Strand CEO Jacob Becraft PhD ’19 explains. “Our technology amplifies the signal to express more proteins for longer while at the same time effectively eliminating the mRNA’s off-target expression.”

Strand is set to begin its first clinical trial in April, which is testing a self-replicating mRNA molecule’s ability to express immune signals directly from a tumor, triggering the immune system to attack and kill the tumor cells directly. It’s also being tested as a possible improvement for existing treatments to a number of solid tumors.

As they work to commercialize its early innovations, Strand’s team is continuing to add capabilities to what it calls its “programmable medicines,” improving mRNA molecules’ ability to sense their environment and generate potent, targeted responses where they’re needed most.

“Self-replicating mRNA was the first thing that we pioneered when we were at MIT and in the first couple years at Strand,” Becraft says. “Now we’ve also moved into approaches like circular mRNAs, which allow each molecule of mRNA to express more of a protein for longer, potentially for weeks at a time. And the bigger our cell-type specific datasets become, the better we are at differentiating cell types, which makes these molecules so targeted we can have a higher level of safety at higher doses and create stronger treatments.”

Making mRNA smarter

Becraft got his first taste of MIT as an undergraduate at the University of Illinois when he secured a summer internship in the lab of MIT Institute Professor Bob Langer.

“That’s where I learned how lab research could be translated into spinout companies,” Becraft recalls.

The experience left enough of an impression on Becraft that he returned to MIT the next fall to earn his PhD, where he worked in the Synthetic Biology Center under professor of bioengineering and electrical engineering and computer science Ron Weiss. During that time, he collaborated with postdoc Tasuku Kitada to create genetic “switches” that could control protein expression in cells.

Becraft and Kitada realized their research could be the foundation of a company around 2017 and started spending time in the Martin Trust Center for MIT Entrepreneurship. They also received support from  MIT Sandbox  and eventually worked with the Technology Licensing Office to establish Strand’s early intellectual property.

“We started by asking, where is the highest unmet need that also allows us to prove out the thesis of this technology? And where will this approach have therapeutic relevance that is a quantum leap forward from what anyone else is doing?” Becraft says. “The first place we looked was oncology.”

People have been working on cancer immunotherapy, which turns a patient’s immune system against cancer cells, for decades. Scientists in the field have developed drugs that produce some remarkable results in patients with aggressive, late-stage cancers. But most next-generation cancer immunotherapies are based on recombinant (lab-made) proteins that are difficult to deliver to specific targets in the body and don’t remain active for long enough to consistently create a durable response.

More recently, companies like Moderna, whose  founders also include MIT alumni , have pioneered the use of mRNAs to create proteins in cells. But to date, those mRNA molecules have not been able to change behavior based on the type of cells they enter, and don’t last for very long in the body.

“If you’re trying to engage the immune system with a tumor cell, the mRNA needs to be expressing from the tumor cell itself, and it needs to be expressing over a long period of time,” Becraft says. “Those challenges are hard to overcome with the first generation of mRNA technologies.”

Strand has developed what it calls the world’s first mRNA programming language that allows the company to specify the tissues its mRNAs express proteins in.

“We built a database that says, ‘Here are all of the different cells that the mRNA could be delivered to, and here are all of their microRNA signatures,’ and then we use computational tools and machine learning to differentiate the cells,” Becraft explains. “For instance, I need to make sure that the messenger RNA turns off when it's in the liver cell, and I need to make sure that it turns on when it's in a tumor cell or a T-cell.”

Strand also uses techniques like mRNA self-replication to create more durable protein expression and immune responses.

“The first versions of mRNA therapeutics, like the Covid-19 vaccines, just recapitulate how our body’s natural mRNAs work,” Becraft explains. “Natural mRNAs last for a few days, maybe less, and they express a single protein. They have no context-dependent actions. That means wherever the mRNA is delivered, it’s only going to express a molecule for a short period of time. That’s perfect for a vaccine, but it’s much more limiting when you want to create a protein that’s actually engaging in a biological process, like activating an immune response against a tumor that could take many days or weeks.”

Technology with broad potential

Strand’s first clinical trial is targeting solid tumors like melanoma and triple-negative breast cancer. The company is also actively developing mRNA therapies that could be used to treat blood cancers.

“We’ll be expanding into new areas as we continue to de-risk the translation of the science and create new technologies,” Becraft says.

Strand plans to partner with large pharmaceutical companies as well as investors to continue developing drugs. Further down the line, the founders believe future versions of its mRNA therapies could be used to treat a broad range of diseases.

“Our thesis is: amplified expression in specific, programmed target cells for long periods of time,” Becraft says. “That approach can be utilized for [immunotherapies like] CAR T-cell therapy, both in oncology and autoimmune conditions. There are also many diseases that require cell-type specific delivery and expression of proteins in treatment, everything from kidney disease to types of liver disease. We can envision our technology being used for all of that.”

Originally published in MIT News on March 27, 2024.

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A new computational technique could make it easier to engineer useful proteins

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To engineer proteins with useful functions, researchers usually begin with a natural protein that has a desirable function, such as emitting fluorescent light, and put it through many rounds of random mutation that eventually generate an optimized version of the protein.

This process has yielded optimized versions of many important proteins, including green fluorescent protein (GFP). However, for other proteins, it has proven difficult to generate an optimized version. MIT researchers have now developed a computational approach that makes it easier to predict mutations that will lead to better proteins, based on a relatively small amount of data.

Using this model, the researchers generated proteins with mutations that were predicted to lead to improved versions of GFP and a protein from adeno-associated virus (AAV), which is used to deliver DNA for gene therapy. They hope it could also be used to develop additional tools for neuroscience research and medical applications.

“Protein design is a hard problem because the mapping from DNA sequence to protein structure and function is really complex. There might be a great protein 10 changes away in the sequence, but each intermediate change might correspond to a totally nonfunctional protein. It’s like trying to find your way to the river basin in a mountain range, when there are craggy peaks along the way that block your view. The current work tries to make the riverbed easier to find,” says Ila Fiete, a professor of brain and cognitive sciences at MIT, a member of MIT’s McGovern Institute for Brain Research, director of the K. Lisa Yang Integrative Computational Neuroscience Center, and one of the senior authors of the study.

Regina Barzilay, the School of Engineering Distinguished Professor for AI and Health at MIT, and Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science at MIT, are also senior authors of an open-access paper on the work , which will be presented at the International Conference on Learning Representations in May. MIT graduate students Andrew Kirjner and Jason Yim are the lead authors of the study. Other authors include Shahar Bracha, an MIT postdoc, and Raman Samusevich, a graduate student at Czech Technical University.

Optimizing proteins

Many naturally occurring proteins have functions that could make them useful for research or medical applications, but they need a little extra engineering to optimize them. In this study, the researchers were originally interested in developing proteins that could be used in living cells as voltage indicators. These proteins, produced by some bacteria and algae, emit fluorescent light when an electric potential is detected. If engineered for use in mammalian cells, such proteins could allow researchers to measure neuron activity without using electrodes.

While decades of research have gone into engineering these proteins to produce a stronger fluorescent signal, on a faster timescale, they haven’t become effective enough for widespread use. Bracha, who works in Edward Boyden’s lab at the McGovern Institute, reached out to Fiete’s lab to see if they could work together on a computational approach that might help speed up the process of optimizing the proteins.

“This work exemplifies the human serendipity that characterizes so much science discovery,” Fiete says. “It grew out of the Yang Tan Collective retreat, a scientific meeting of researchers from multiple centers at MIT with distinct missions unified by the shared support of K. Lisa Yang. We learned that some of our interests and tools in modeling how brains learn and optimize could be applied in the totally different domain of protein design, as being practiced in the Boyden lab.”

For any given protein that researchers might want to optimize, there is a nearly infinite number of possible sequences that could generated by swapping in different amino acids at each point within the sequence. With so many possible variants, it is impossible to test all of them experimentally, so researchers have turned to computational modeling to try to predict which ones will work best.

In this study, the researchers set out to overcome those challenges, using data from GFP to develop and test a computational model that could predict better versions of the protein.

They began by training a type of model known as a convolutional neural network (CNN) on experimental data consisting of GFP sequences and their brightness — the feature that they wanted to optimize.

The model was able to create a “fitness landscape” — a three-dimensional map that depicts the fitness of a given protein and how much it differs from the original sequence — based on a relatively small amount of experimental data (from about 1,000 variants of GFP).

These landscapes contain peaks that represent fitter proteins and valleys that represent less fit proteins. Predicting the path that a protein needs to follow to reach the peaks of fitness can be difficult, because often a protein will need to undergo a mutation that makes it less fit before it reaches a nearby peak of higher fitness. To overcome this problem, the researchers used an existing computational technique to “smooth” the fitness landscape.

Once these small bumps in the landscape were smoothed, the researchers retrained the CNN model and found that it was able to reach greater fitness peaks more easily. The model was able to predict optimized GFP sequences that had as many as seven different amino acids from the protein sequence they started with, and the best of these proteins were estimated to be about 2.5 times fitter than the original.

“Once we have this landscape that represents what the model thinks is nearby, we smooth it out and then we retrain the model on the smoother version of the landscape,” Kirjner says. “Now there is a smooth path from your starting point to the top, which the model is now able to reach by iteratively making small improvements. The same is often impossible for unsmoothed landscapes.” 

Proof-of-concept

The researchers also showed that this approach worked well in identifying new sequences for the viral capsid of adeno-associated virus (AAV), a viral vector that is commonly used to deliver DNA. In that case, they optimized the capsid for its ability to package a DNA payload.

“We used GFP and AAV as a proof-of-concept to show that this is a method that works on data sets that are very well-characterized, and because of that, it should be applicable to other protein engineering problems,” Bracha says.

The researchers now plan to use this computational technique on data that Bracha has been generating on voltage indicator proteins.

“Dozens of labs having been working on that for two decades, and still there isn’t anything better,” she says. “The hope is that now with generation of a smaller data set, we could train a model in silico and make predictions that could be better than the past two decades of manual testing.”

The research was funded, in part, by the U.S. National Science Foundation, the Machine Learning for Pharmaceutical Discovery and Synthesis consortium, the Abdul Latif Jameel Clinic for Machine Learning in Health, the DTRA Discovery of Medical Countermeasures Against New and Emerging threats program, the DARPA Accelerated Molecular Discovery program, the Sanofi Computational Antibody Design grant, the U.S. Office of Naval Research, the Howard Hughes Medical Institute, the National Institutes of Health, the K. Lisa Yang ICoN Center, and the K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics at MIT.

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• Regina Barzilay
• Tommi Jaakkola
• McGovern Institute
• K. Lisa Yang Integrative Computational Neuroscience Center
• Department of Brain and Cognitive Sciences
• Computer Science and Artificial Intelligence Laboratory

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• Biological engineering
• Brain and cognitive sciences
• Computer science and technology
• Artificial intelligence
• Electrical Engineering & Computer Science (eecs)
• Computer Science and Artificial Intelligence Laboratory (CSAIL)
• National Science Foundation (NSF)
• National Institutes of Health (NIH)
• Defense Advanced Research Projects Agency (DARPA)

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Learning to See the World in 3D

Talk Abstract: Looking at just a single image of a scene is sufficient for us to build rich mental representations, enabling us to reason about the underlying 3D world. Inferring such representations from little information is a core aspect of intelligence. It helps us understand and interact with our surroundings and each other. My research aims to build similar computational models—methods that can perceive the rich 3D structured world from sparse observations such as images and videos. I will discuss the main challenges in 3D perception and how my research addresses them by posing vision as an inverse graphics problem. I will demonstrate results on perceiving humans, objects, and large unconstrained scenes.

Speaker Biography: Ayush Tewari is a postdoctoral researcher at MIT CSAIL with Bill Freeman, Vincent Sitzmann, and Josh Tenenbaum. He previously completed his Ph.D. at the Max Planck Institute for Informatics, advised by Christian Theobalt. His research interests lie at the intersection of computer vision, machine learning, and computer graphics, focused on 3D perception. He was awarded the Otto Hahn medal from the Max Planck Society for his scientific contributions as a Ph.D. student.