phd in computer science uitm

• 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 2025 ;
• 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 receive a response.

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 2025 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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Information-theoretic measures in machine learning

May 15, 2024 By Katie Satterlee

• Current Students
• Electrical and Computer Engineering
• Information Systems and Data Science

From recognizing complex patterns to making predictions about future outcomes, the applications of machine learning are constantly evolving. 

Dr. Ruida Zhou, a recent electrical and computer engineering doctoral graduate, is working on designing and analyzing algorithms to solve machine learning problems using information theory. Information theory is understanding how to store, transmit, process and measure information.

One concept he’s researching is called generalization. 

“Imagine you give a machine images of cats and dogs,” Zhou said. “Somehow, it can understand the concepts of cats and dogs. This is called generalization.”

Zhou is using information theory to quantify and interpret errors that occur in generalization. 

Generalization error refers to the difference in how well a model performs when given new data versus training data, which is data it is already familiar with. Information-theoretic measures capture the amount of information acquired by the model from the training data, giving a reasonable generalization interpretation.

We have used information-theoretic measures to facilitate the design of a safe and robust learning agent that can make decisions such as autonomous driving.

“Interpreting generalization is one example of what information-theoretic measures are capable of doing,” Zhou said. “We have used information-theoretic measures to facilitate the design of a safe and robust learning agent that can make decisions such as autonomous driving.”

Zhou took this research a step further to study an aspect of machine learning called reinforcement learning by collaborating with professors from the Department of Electrical and Computer Engineering, which include his advisor, Dr. Chao Tian, along with Dr. Dileep Kalathil and Dr. P. R. Kumar. 

Reinforcement learning is when a computer program learns to make decisions through trial and error. The program receives feedback in the form of rewards and punishments, adjusting its behavior over time.

Zhou received the 2024 Association of Former Students Distinguished Graduate Excellence in Research Doctoral Award for his work. This accolade recognizes exceptional contributions to research and dedication to innovation. 

“We’re witnessing the rapid development of artificial intelligence technologies,” Zhou said. “This is an exciting time for conducting machine-learning research as an information theorist.”

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Nevada Today

Father and son set to receive doctoral degrees May 17

College of Engineering will graduate Jay and Nathan Thom with Ph.Ds in Computer Science & Engineering

Interest in computer science runs in the family.

There’s nothing like 700-level computer science classes to bring on the father-son bonding: just ask Jay and Nathan Thom.

Jay will be receiving a doctorate in Computer Science & Engineering at the May 17 Engineering graduation ceremony, and so will his son, Nathan.

“Graduating with a Ph.D. is a really satisfying accomplishment for me, but graduating with one of my sons will make it one of the most memorable experiences of my life,” said Jay, who also works in the College of Engineering’s Computer Science & Engineering Department as a senior information security engineer.

The two studied together, supported each other and maybe once or twice Jay kept Nathan on track.

“He was a good influence,” Nathan, who goes by Nate, said. “He was the friend you needed to have.”

When Nate joined the University of Nevada, Reno in 2015, Jay helped him with calculus. Years later, Nate would return the favor when Jay needed help with the math in a game theory class. The two have shared lab space and even co-authored two conference papers about Internet of Things (IoT) device identification.

Additional co-authors on those papers were Professor Shamik Sengupta and Assistant Professor Emily Hand, each of whom served as Ph.D. advisors for Jay and Nate, respectively.

“Jay and Nate have been extremely helpful, cooperative and hardworking people,” Sengupta said. “They are extremely friendly and always ready to help on a moment’s notice.”

Sengupta added that Jay will be his 10 th   Ph.D. student to graduate; for Hand, the experience of mentoring a Ph.D. student through graduation is new.

“Nate has been wonderful to work with,” she said. “He and Jay both have been an asset to the (CSE) department and college. They serve as our resident IT guys, helping with anything and everything in our labs.”

Anomaly in the data set

Parent-child graduations are somewhat unusual, but not for Jay. In 2015, he received his Bachelor of Science degree alongside his son Ben. 

Also out of the ordinary: Jay was a teaching assistant for a class in which his two other sons, Max and Nick, were students.

Nate, the youngest, remembers hanging out with his brothers on the University campus even before he enrolled as a student. When he was 13, Nate was homeschooled by Jay, and would tag along with his father to the University and study on campus. His three older brothers also were students at that time, and as Nate remembers, “Mom had just graduated.”

“Mom” is Shendry Thom, who earned bachelor’s, master’s and doctoral degrees in nursing from the University.

And of course, the University is where Shendry met Jay, back in the 1980s.

The campus has been somewhat of a stomping ground for the Thom family.

“It feels like home,” Nate said.

Family affair

If campus feels like home, computer science is where everyone seems to gather: Nate’s older brothers Ben, Max and Nick are software engineers in the Reno area; Nate’s wife Kathleen currently studies computer science at the University; and Nick’s fiancé, Maddy, is pursuing a master’s degree in computer science, also at the University. Jay might be responsible for this family trend, according to Nate.

Jay originally studied electrical engineering in the 1980s, but when he returned to the University to study computer science, Nate said, “that was the same year Max had started college. That influenced him and me. We’re all in computer science.”

Their areas of expertise vary, however. Jay’s dissertation, “AI Enabled IOT Network Traffic Fingerprinting with Locality Sensitive Hashing,” deals with training smart devices to communicate with each other securely. Nate’s dissertation, “Attributes in Face Processing: Novel Methods for Explanation, Training and Representation,” is about improving AI systems that recognize faces.

What they have in common — besides genetics — is a strong interest in advancing the field of computer science.

“We’re really good at coming up with big ideas,” Nate said. “One of the things we say is ‘create value.’ Every time we show up somewhere, we try to create value.”

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Elaine Liu: Charging ahead

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MIT senior Elaine Siyu Liu doesn’t own an electric car, or any car. But she sees the impact of electric vehicles (EVs) and renewables on the grid as two pieces of an energy puzzle she wants to solve.

The U.S. Department of Energy reports that the number of public and private EV charging ports nearly doubled in the past three years, and many more are in the works. Users expect to plug in at their convenience, charge up, and drive away. But what if the grid can’t handle it?

Electricity demand, long stagnant in the United States, has spiked due to EVs, data centers that drive artificial intelligence, and industry. Grid planners forecast an increase of 2.6 percent to 4.7 percent in electricity demand over the next five years, according to data reported to federal regulators. Everyone from EV charging-station operators to utility-system operators needs help navigating a system in flux.

That’s where Liu’s work comes in.

Liu, who is studying mathematics and electrical engineering and computer science (EECS), is interested in distribution — how to get electricity from a centralized location to consumers. “I see power systems as a good venue for theoretical research as an application tool,” she says. “I'm interested in it because I'm familiar with the optimization and probability techniques used to map this level of problem.”

Liu grew up in Beijing, then after middle school moved with her parents to Canada and enrolled in a prep school in Oakville, Ontario, 30 miles outside Toronto.

Liu stumbled upon an opportunity to take part in a regional math competition and eventually started a math club, but at the time, the school’s culture surrounding math surprised her. Being exposed to what seemed to be some students’ aversion to math, she says, “I don’t think my feelings about math changed. I think my feelings about how people feel about math changed.”

Liu brought her passion for math to MIT. The summer after her sophomore year, she took on the first of the two Undergraduate Research Opportunity Program projects she completed with electric power system expert Marija Ilić, a joint adjunct professor in EECS and a senior research scientist at the MIT Laboratory for Information and Decision Systems.

Predicting the grid

Since 2022, with the help of funding from the MIT Energy Initiative (MITEI), Liu has been working with Ilić on identifying ways in which the grid is challenged.

One factor is the addition of renewables to the energy pipeline. A gap in wind or sun might cause a lag in power generation. If this lag occurs during peak demand, it could mean trouble for a grid already taxed by extreme weather and other unforeseen events.

If you think of the grid as a network of dozens of interconnected parts, once an element in the network fails — say, a tree downs a transmission line — the electricity that used to go through that line needs to be rerouted. This may overload other lines, creating what’s known as a cascade failure.

“This all happens really quickly and has very large downstream effects,” Liu says. “Millions of people will have instant blackouts.”

Even if the system can handle a single downed line, Liu notes that “the nuance is that there are now a lot of renewables, and renewables are less predictable. You can't predict a gap in wind or sun. When such things happen, there’s suddenly not enough generation and too much demand. So the same kind of failure would happen, but on a larger and more uncontrollable scale.”

Renewables’ varying output has the added complication of causing voltage fluctuations. “We plug in our devices expecting a voltage of 110, but because of oscillations, you will never get exactly 110,” Liu says. “So even when you can deliver enough electricity, if you can't deliver it at the specific voltage level that is required, that’s a problem.”

Liu and Ilić are building a model to predict how and when the grid might fail. Lacking access to privatized data, Liu runs her models with European industry data and test cases made available to universities. “I have a fake power grid that I run my experiments on,” she says. “You can take the same tool and run it on the real power grid.”

Liu’s model predicts cascade failures as they evolve. Supply from a wind generator, for example, might drop precipitously over the course of an hour. The model analyzes which substations and which households will be affected. “After we know we need to do something, this prediction tool can enable system operators to strategically intervene ahead of time,” Liu says.

Dictating price and power

Last year, Liu turned her attention to EVs, which provide a different kind of challenge than renewables.

In 2022, S&P Global reported that lawmakers argued that the U.S. Federal Energy Regulatory Commission’s (FERC) wholesale power rate structure was unfair for EV charging station operators.

In addition to operators paying by the kilowatt-hour, some also pay more for electricity during peak demand hours. Only a few EVs charging up during those hours could result in higher costs for the operator even if their overall energy use is low.

Anticipating how much power EVs will need is more complex than predicting energy needed for, say, heating and cooling. Unlike buildings, EVs move around, making it difficult to predict energy consumption at any given time. “If users don't like the price at one charging station or how long the line is, they'll go somewhere else,” Liu says. “Where to allocate EV chargers is a problem that a lot of people are dealing with right now.”

One approach would be for FERC to dictate to EV users when and where to charge and what price they'll pay. To Liu, this isn’t an attractive option. “No one likes to be told what to do,” she says.

Liu is looking at optimizing a market-based solution that would be acceptable to top-level energy producers — wind and solar farms and nuclear plants — all the way down to the municipal aggregators that secure electricity at competitive rates and oversee distribution to the consumer.

Analyzing the location, movement, and behavior patterns of all the EVs driven daily in Boston and other major energy hubs, she notes, could help demand aggregators determine where to place EV chargers and how much to charge consumers, akin to Walmart deciding how much to mark up wholesale eggs in different markets.

Last year, Liu presented the work at MITEI’s annual research conference. This spring, Liu and Ilić are submitting a paper on the market optimization analysis to a journal of the Institute of Electrical and Electronics Engineers.

Liu has come to terms with her early introduction to attitudes toward STEM that struck her as markedly different from those in China. She says, “I think the (prep) school had a very strong ‘math is for nerds’ vibe, especially for girls. There was a ‘why are you giving yourself more work?’ kind of mentality. But over time, I just learned to disregard that.”

After graduation, Liu, the only undergraduate researcher in Ilić’s MIT Electric Energy Systems Group, plans to apply to fellowships and graduate programs in EECS, applied math, and operations research.

Based on her analysis, Liu says that the market could effectively determine the price and availability of charging stations. Offering incentives for EV owners to charge during the day instead of at night when demand is high could help avoid grid overload and prevent extra costs to operators. “People would still retain the ability to go to a different charging station if they chose to,” she says. “I'm arguing that this works.”

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Building Mentoring Relationships and Peer Networks at CRA-WP Grad Cohort Workshops

The annual Computing Research Association (CRA) Widening Participation (WP) Grad Cohort for Women and Grad Cohort for Inclusion, Diversity, Equity, Accessibility, and Leadership Skills (IDEALS) workshops provide an opportunity for women, historically underrepresented groups, and people with disabilities in the early stages of their graduate studies in computing to build mentoring relationships and develop peer networks.

As a member of the CRA, Northwestern Computer Science received full support for three graduate students to attend the 2024 conference, held April 11 – 13 in Minneapolis. In addition, the department funded one additional student to attend the conference.

Of the more than 600 students who applied to participate, Herminio Bodon, Mandi Cai, Monisola Jayeoba, and Mara Ulloa were among the 275 participants at the conference.

Russ Joseph , associate professor of electrical and computer engineering and associate professor of computer science at Northwestern Engineering, is a founding steering committee member of the CRA-WP Grad Cohort for IDEALS. He was also among the esteemed group of leaders and senior researchers in academia, industry, and government laboratories that led workshop sessions. Joseph and Chad Jenkins (University of Michigan) facilitated the interactive session “ Balancing Graduate School and Personal Life .” In addition, Joseph provided one-on-one academic and career advising.

We asked the students about the importance and benefits of attending this type of mentoring conference, their experience attending the CRA-WP workshops, and an especially resonant piece of advice or perspective they gained from the event.

Herminio Bodon

Bodon , a PhD student in Northwestern’s Technology and Social Behavior program (TSB) , attended the CRA-WP Grad Cohort Workshop for IDEALS.

His research — situated at the intersection of human-computer interaction, data science, and learning sciences — focuses on exploring how children learn in complex environments and building tools to support learning.

A member of the Technological Innovations for Inclusive Learning and Teaching Laboratory , Bodon is advised by Marcelo Worsley , Karr Family Associate Professor of Computer Science at the McCormick School of Engineering and associate professor of learning sciences at Northwestern’s School of Education and Social Policy .

Cai noted that conferences like the CRA-WP Grad Cohort for Women are helpful reminders of the importance of building and maintaining support systems within computing. She appreciated meeting creative people navigating similar research challenges, exchanging ideas and guidance, and experiencing the mutual support among her peers.

“Another researcher at the conference told me that ‘perfection is the enemy’ and to just go for it,” Cai said. “It was the shove I needed to get a project I’m working on off the ground without overthinking it so much and to be open to what my findings could look like.”

A member of the Midwest Uncertainty Collective (MU Collective) , Cai is advised by Matthew Kay , an associate professor of computer science at Northwestern Engineering and of communication studies at Northwestern’s School of Communication .

Monisola Jayeoba

She also gained constructive early feedback, practiced public speaking, and honed her presentation skills via the opportunity to present a three-minute lightning talk on her research, which lies at the intersection of human-computer interaction, computer-supported cooperative work, and health informatics.

Jayeoba highlighted a resonating message of keynote speaker Dilma Da Silva , Ford Motor Company Design Professor II of Computer Science and Engineering at Texas A&M University and acting assistant director of the US NSF Directorate for Computer & Information Science and Engineering. Da Silva emphasized the importance of diversifying knowledge and acquiring a wide range of relevant skills.

“This is crucial in a fast-paced and rapidly evolving labor market,” Jayeoba said “It reminded me to make intentional plans and decisions in the remaining years of my doctoral studies.”

Jayeoba is advised by Maia Jacobs , Lisa Wissner-Slivka and Benjamin Slivka Professor of Computer Science at Northwestern Engineering and assistant professor of preventive medicine at Northwestern’s Feinberg School of Medicine .

Ulloa is a member of the Center for Advancing Safety of Machine Intelligence (CASMI) research project team studying stress levels during pregnancy and developing prevention and intervention approaches for improving maternal and fetal health outcomes leveraging novel Machine Learning and Human-Computer Interaction methods.

As Ulloa prepares to share her research findings from the CASMI project, she was particularly inspired by the CRA-WP Grad Cohort Workshop for IDEALS session “ Publishing Your Research ,” led by Shaun Kane (Google Research) and Gonzalo Ramos (Microsoft Research).

“The session highlighted the essence of a significant research contribution and the importance of building a publishing muscle by constantly sharing your work through posters abstracts, doctoral symposia, and workshop papers as you progress towards publishing full-length articles at top venues,” Ulloa said.

Jacobs and Nabil Alshurafa , associate professor of preventive medicine at Feinberg and (by courtesy) associate professor of computer science and electrical and computer engineering at Northwestern Engineering, are co-principal investigators of the CASMI-funded research project, “ Co-Designing Patient-Facing Machine Learning for Prenatal Stress Reduction .”

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