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Current MIT Graduate Students

Please Note: The CSE PhD application deadline was December 1; the portal is now closed and we are no longer accepting applications for 2024 enrollment.

Thank you for considering the academic programs of the MIT Center for Computational Science and Engineering (CCSE) as part of your graduate education options.

CCSE offers a master’s degree and two doctoral programs in computational science and engineering (CSE) – one leading to a standalone doctoral degree in CSE offered entirely by CCSE (CSE PhD) and the other leading to an i nterdisciplinary doctoral degree offered jointly with participating departments in the School of Engineering and the School of Science (Dept-CSE PhD). Information about the application and admission process for each program is available via the links of the left and summarized below. MIT Registrar’s Office provides graduate tuition and fee rates as set by the MIT Corporation and the Graduate Admissions section of MIT’s Office of Graduate Education (OGE) website contains additional information about costs of attendance and funding .

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 Schwarzman College of Computing. 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.

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 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.

The Master of Science in Computational Science and Engineering (CSE SM) program , which is currently not accepting external applications, is an interdisciplinary program for students interested in the development, analysis, and application of state-of-the-art computational approaches to science and engineering problems. Examples of such approaches include but are not limited to: numerical methods for integral and partial differential equations; molecular and stochastic simulation; model reduction; uncertainty quantification; computational statistics; optimization; high-performance computing; and machine learning applied to science and engineering problems. The curriculum comprises a core set of subjects focused on cross-cutting computational methodologies and an elective component encompassing specific disciplinary topics in science and engineering. Additional information about CSE SM admissions, including any future plans to restart external admissions, will be posted on the CSE SM Admissions webpage if/when they become available. In the interim, external requests for information about the CSE SM program will not be addressed. Thank you in advance for your understanding.

CSE’s graduate offerings require some background in computational science and engineering (CSE) and lead to degrees in CSE. They are not programs in computer science (CS). In particular, they have a different research and education focus from MIT’s computer science programs. Students who are instead interested in a graduate degree in computer science should apply to the graduate program of the Department of Electrical Engineering and Computer Science (EECS) .

MIT Graduate Admissions Statement March 26, 2020

In response to the challenges of teaching, learning, and assessing academic performance during the global COVID-19 pandemic, MIT has adopted the following principle: MIT’s admissions committees and offices for graduate and professional schools will take the significant disruptions of the COVID-19 outbreak in 2020 into account when reviewing students’ transcripts and other admissions materials as part of their regular practice of performing individualized, holistic reviews of each applicant.

In particular, as we review applications now and in the future, we will respect decisions regarding the adoption of Pass/No Record (or Credit/No Credit or Pass/Fail) and other grading options during the unprecedented period of COVID-19 disruptions, whether those decisions were made by institutions or by individual students. We also expect that the individual experiences of applicants will richly inform applications and, as such, they will be considered with the entirety of a student’s record.

Ultimately, even in these challenging times, our goal remains to form graduate student cohorts that are collectively excellent and composed of outstanding individuals who will challenge and support one another.

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

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

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

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

Q: What is computational science and engineering?

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

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

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

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

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

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

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

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

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Center to advance predictive simulation research established at MIT Schwarzman College of Computing

MIT Schwarzman College of Computing leadership team (left to right) David Kaiser, Daniela Rus, Dan Huttenlocher, Julie Shah, and Asu Ozdaglar

A college for the computing age

A new doctoral field offering a computational specialty within MIT's Department of Earth, Atmospheric and Planetary Sciences resonates with the Institute’s growing awareness of the advantages provided by education based in computer science and engineering.

MIT adds computational Earth, atmospheric, and planetary sciences to its PhD offerings

Youssef Marzouk (left) and Nicolas Hadjiconstantinou assume new roles at the Center for Computational Engineering at “an exciting time for computation at MIT,” says School of Engineering Dean Anantha Chandrakasan.

Youssef Marzouk and Nicolas Hadjiconstantinou to direct the Center for Computational Engineering

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Approximately 40% of undergraduates and 17% of graduate students at MIT are enrolled in computing programs within the Schwarzman College of Computing. More still augment or compliment their studies with subjects in computing and data science and classes that blend computational thinking with their chosen discipline. The College includes a breadth of academic offerings — from a traditional degree in Computer Science to a blended major in Computation and Cognition to a minor in Statistics and Data Science.

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Computational and Systems Biology PhD Program

Computational and systems biology.

The field of computational and systems biology represents a synthesis of ideas and approaches from the life sciences, physical sciences, computer science, and engineering. Recent advances in biology, including the human genome project and massively parallel approaches to probing biological samples, have created new opportunities to understand biological problems from a systems perspective. Systems modeling and design are well established in engineering disciplines but are newer in biology. Advances in computational and systems biology require multidisciplinary teams with skill in applying principles and tools from engineering and computer science to solve problems in biology and medicine. To provide education in this emerging field, the Computational and Systems Biology (CSB) program integrates MIT's world-renowned disciplines in biology, engineering, mathematics, and computer science. Graduates of the program are uniquely prepared to make novel discoveries, develop new methods, and establish new paradigms. They are also well-positioned to assume critical leadership roles in both academia and industry, where this field is becoming increasingly important.

Computational and systems biology, as practiced at MIT, is organized around "the 3 Ds" of description, distillation, and design. In many research programs, systematic data collection is used to create detailed molecular- or cellular-level descriptions of a system in one or more defined states. Given the complexity of biological systems and the number of interacting components and parameters, system modeling is often conducted with the aim of distilling the essential or most important subsystems, components, and parameters, and of obtaining simplified models that retain the ability to accurately predict system behavior under a wide range of conditions. Distillation of the system can increase the interpretability of the models in relation to evolutionary and engineering principles such as robustness, modularity, and evolvability. The resulting models may also serve to facilitate rational design of perturbations to test understanding of the system or to change system behavior (e.g., for therapeutic intervention), as well as efforts to design related systems or systems composed of similar biological components.

CSB Faculty and Research

More than 70 faculty members at the Institute participate in MIT's Computational and Systems Biology Initiative (CSBi). These investigators span nearly all departments in the School of Science and the School of Engineering, providing CSB students the opportunity to pursue thesis research in a wide variety of different laboratories. It is also possible for students to arrange collaborative thesis projects with joint supervision by faculty members with different areas of expertise. Areas of active research include computational biology and bioinformatics, gene and protein networks, regulatory genomics, molecular biophysics, instrumentation engineering, cell and tissue engineering, predictive toxicology and metabolic engineering, imaging and image informatics, nanobiology and microsystems, biological design and synthetic biology, neurosystems biology, and cancer biology.

The CSB PhD Program

The CSB PhD program is an Institute-wide program that has been jointly developed by the Departments of Biology, Biological Engineering, and Electrical Engineering and Computer Science. The program integrates biology, engineering, and computation to address complex problems in biological systems, and CSB PhD students have the opportunity to work with CSBi faculty from across the Institute. The curriculum has a strong emphasis on foundational material to encourage students to become creators of future tools and technologies, rather than merely practitioners of current approaches. Applicants must have an undergraduate degree in biology (or a related field), bioinformatics, chemistry, computer science, mathematics, statistics, physics, or an engineering discipline, with dual-emphasis degrees encouraged.

CSB Graduate Education

All students pursue a core curriculum that includes classes in biology and computational biology, along with a class in computational and systems biology based on the scientific literature. Advanced electives in science and engineering enhance both the breadth and depth of each student's education. During their first year, in addition to coursework, students carry out rotations in multiple research groups to gain a broader exposure to work at the frontier of this field, and to identify a suitable laboratory in which to conduct thesis research. CSB students also serve as teaching assistants during one semester in the second year to further develop their teaching and communication skills and facilitate their interactions across disciplines. Students also participate in training in the responsible conduct of research to prepare them for the complexities and demands of modern scientific research. The total length of the program, including classwork, qualifying examinations, thesis research, and preparation of the thesis is roughly five years.

The CSB curriculum has two components. The first is a core that provides foundational knowledge of both biology and computational biology. The second is a customized program of electives that is selected by each student in consultation with members of the CSB graduate committee. The goal is to allow students broad latitude in defining their individual area of interest, while at the same time providing oversight and guidance to ensure that training is rigorous and thorough.

Core Curriculum

The core curriculum consists of three classroom subjects plus a set of three research rotations in different research groups. The classroom subjects fall into three areas described below.

Modern Biology (One Subject): A term of modern biology at MIT strengthens the biology base of all students in the program. Subjects in biochemistry, genetics, cell biology, molecular biology, or neurobiology fulfill this requirement. The particular course taken by each student will depend on their background and will be determined in consultation with graduate committee members.

Computational Biology (One Subject): A term of computational biology provides students with a background in the application of computation to biology, including analysis and modeling of sequence, structural, and systems data. This requirement can be fulfilled by 7.91[J] / 20.490[J] Foundations of Computational and Systems Biology.

Topics in Computational and Systems Biology (One Subject): All first-year students in the program participate in / 7.89[J] Topics in Computational and Systems Biology, an exploration of problems and approaches in the field of computational and systems biology through in-depth discussion and critical analysis of selected primary research papers. This subject is restricted to first-year PhD students in CSB or related fields in order to build a strong community among the class. It is the only subject in the program with such a limitation.

Research Group Rotations (Three Rotations): To assist students with lab selection and provide a range of research activities in computational and systems biology, students participate in three research rotations of one to two months' duration during their first year. Students are encouraged to gain experience in experimental and computational approaches taken across different disciplines at MIT.

Advanced Electives

The requirement of four advanced electives is designed to develop both breadth and depth. The electives add to the base of the diversified core and contribute strength in areas related to student interest and research direction. To develop depth, two of the four advanced electives must be in the same research area or department. To develop breadth, at least one of the electives must be in engineering and at least one in science. Each student designs a program of advanced electives that satisfies the distribution and area requirements in close consultation with members of the graduate committee.

Additional Subjects: As is typical for students in other doctoral programs at MIT, CSB PhD students may take classes beyond the required diversified core and advanced electives described above. These additional subjects can be used to add breadth or depth to the proposed curriculum, and might be useful to explore advanced topics relevant to the student's thesis research in later years. The CSB Graduate Committee works with each graduate student to develop a path through the curriculum appropriate for his or her background and research interests.

Training in the Responsible Conduct of Research: Throughout the program, students will be expected to attend workshops and other activities that provide training in the ethical conduct of research. This is particularly important in interdisciplinary fields such as computational and systems biology, where different disciplines often have very different philosophies and conventions. By the end of the fourth year, students will have had about 16 hours of training in the responsible conduct of research.

Qualifying Exams: In addition to coursework and a research thesis, each student must pass a written and an oral qualifying examination at the end of the second year or the beginning of the third year. The written examination involves preparing a research proposal based on the student's thesis research, and presenting the proposal to the examination committee. This process provides a strong foundation for the thesis research, incorporating new research ideas and refinement of the scope of the research project. The oral examination is based on the coursework taken and on related published literature. The qualifying exams are designed to develop and demonstrate depth in a selected area (the area of the thesis research) as well as breadth of knowledge across the field of computational and systems biology.

Thesis Research: Research will be performed under the supervision of a CSBi faculty member, culminating in the submission of a written thesis and its oral defense before the community and thesis defense committee. By the second year, a student will have formed a thesis advisory committee that they will meet with on an annual basis.

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Aeronautics and astronautics, biological engineering, biophysics graduate certificate program, brain and cognitive sciences, chemical engineering, civil and environmental engineering (environmental microbiology), computational and systems biology, earth, atmospheric, and planetary sciences, hst - health, sciences and technology, materials science and engineering, mechanical engineering, microbiology program, nuclear science and engineering, sloan mba with a health care focus, sts- science, technology, and society, whoi joint program, writing and humanistic studies, life sciences at mit.

Many areas of research today have a Life Sciences focus. This is primarily due to the powerful tools of molecular biology, which form a common language and allow exciting and important interdisciplinary approaches. Experience in Life Sciences-based research opens multiple career paths.

This site collates the broad array of MIT graduate degree programs with a primary focus on biological questions, or that can include a Life Sciences focus. Applications for graduate study should be made through the appropriate program. Please explore this site, and our program offerings!

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Bachelor's Programs

Each year, the School of Computer Science admits students to undergraduate programs ranging from a traditional B.S. in computer science to a bachelor of computer science and arts.

Whatever option you choose, you’re guaranteed to find a rigorous program dedicated to the real-world training and practical problem solving that has been the hallmark of computer science education at CMU since its inception.

B.S. in Computer Science

Carnegie Mellon's undergraduate major in computer science combines a solid core of computer science courses with the ability to gain substantial depth in another area through a required minor in a second subject. The curriculum also gives you numerous choices for science and humanities courses. Computing is a discipline with strong links to many fields, and our program gives you unparalleled flexibility to pursue these fields. Our mathematics and probability component ensures that you'll have the formal tools to remain current as technologies and systems change, but at the same time you'll gain insight into the practical issues of building and maintaining systems by participating in intensive project-oriented courses.

Unlike other universities, where research rarely occurs at the undergraduate level, CMU CS students often have part-time or summer jobs — or receive independent study credit — working on research while pursuing their bachelor's degree. If you're interested in a research/graduate school career, we offer an intensive course of research, equivalent to four classroom courses, culminating in the preparation of a senior research honors thesis.

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Bachelor of Science in Music and Technology

Carnegie Mellon University's Music and Technology program was established in 2009 as a joint project between three of the schools: The School of Music, School of Computer Science, and the Department of Electrical and Computer Engineering. Information regarding this degree is available on the Bachelor of Science in Music and Technology website .

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Mit offers a wide range of degrees and programs..

All graduate students, whether or not they are participating in an interdepartmental program, must have a primary affiliation with and be registered in a single department. Every applicant accepted by MIT is admitted through one of the graduate departments. MIT has a number of established interdepartmental programs, and there are many more opportunities for students to arrange interdepartmental programs with interested faculty members.

All MIT graduate degree programs have residency requirements, which reflect academic terms (excluding summer). Some degrees also require completion of an acceptable thesis prepared in residence at MIT, unless special permission is granted for part of the thesis work to be accomplished elsewhere. Other degrees require a pro-seminar or capstone experience.

Applicants interested in graduate education should apply to the department or graduate program conducting research in the area of interest. Below is an alphabetical list of all the available departments and programs that offer a graduate-level degree.

Interested in reading first-hand accounts of MIT graduate students from a variety of programs? Visit the Grad Blog . Prospective students who want to talk with a current student can reach out to their department(s) of interest for connections or, if they are interested in the MIT experience for diverse communities, can reach out to a GradDiversity Ambassador .

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MyU : For Students, Faculty, and Staff

Clone of CSE welcomes 25 new faculty in 2023-24

Birds-eye view of the UMN Twin Cities campus, with the Minneapolis skyline.

STEM experts from across the world join the University of Minnesota

The University of Minnesota College of Science and Engineering (CSE) welcomes 25 faculty members this 2023-24 academic year—on its way to achieving its goal to hire 60 faculty in three years.

The expertise of this new group of CSE researchers and educators is broad. They range in areas such as hybrid intelligence systems, the reconstruction of past environments and climates, electric machines and magnetic levitation, reinforced concrete structures, and mathematical models to predict the electronic properties of novel materials.

Meet our new science and engineering faculty:

Rene Boiteau is an assistant professor of chemistry. He joins Minnesota from Oregon State University, where he held a joint faculty appointment in the Pacific Northwest National Laboratory. Boiteau earned a bachelor’s in chemistry at Northwestern University, a master’s in earth sciences at University of Cambridge, and a Ph.D. in chemical oceanography at Massachusetts Institute of Technology and Woods Hole Oceanographic Institution. Much of his work is focused on developing analytical chemical approaches, especially mass spectrometry.

Zhu-Tian Chen is an assistant professor of computer science and engineering. He received his bachelor’s in software engineering from South China University of Technology and Ph.D. in computer science from Hong Kong University of Science and Technology. Prior to Minnesota, Chen served as a postdoctoral fellow at Harvard University and postdoctoral researcher at the University of California San Diego. His recent work focuses on enhancing human-data and human-AI interactions in both AR/VR environments—with applications in sports, data journalism, education, biomedical, and architecture.

Gregory “Greg” Handy is an assistant professor of mathematics . He comes to Minnesota from the University of Chicago, where he was a postdoctoral scholar in the Departments of Neurobiology and Statistics. As an applied mathematician and theoretical biologist, Handy’s research strives to use biological applications as inspiration to create new mathematical techniques, and to combine these techniques with classical approaches to examine the mechanisms driving biological processes. This fall, he is teaching Math 2142: Elementary Linear Algebra.

Jessica Hoover is a professor of chemistry. She joins the University of Minnesota from West Virginia University, where she has been a faculty member since 2012. Hoover’s interest in catalysis has been the focus of her work since her undergraduate studies. She graduated with a bachelor’s from Harvey Mudd College before arriving at the University of Washington to pursue her Ph.D. She was a postdoctoral researcher at the University of Wisconsin, Madison.

Harman Kaur is an assistant professor of computer science and engineering—and a University of Minnesota alumna (2016 bachelor’s in computer science). Her research areas are human-centered artificial intelligence, explainability and interpretability, and hybrid intelligence systems. She is affiliated with the GroupLens Research Lab, a group of faculty and students in her department that’s focused on human computing interaction. Prior to Minnesota, Kaur served as a graduate researcher in the interactive Systems Lab and comp.social Lab at the University of Michigan, where she received both her master’s and Ph.D.

Yulong Lu is an assistant professor of mathematics. He joins the faculty from University of Massachusetts, Amherst. Lu received his Ph.D. in mathematics and statistics at the University of Warwick. His research lies at the intersection of applied and computational mathematics, statistics, and data sciences. His recent work is focused on the mathematical aspects of deep learning. This fall, Lu is teaching Math 2573H: Honors Calculus III to undergraduates and Math 8600: Topics in Applied Mathematics, Theory of Deep Learning to graduate students.

Ben Margalit is an assistant professor of physics and astronomy. As a theoretical astrophysicist, he studies the fundamental physics of star explosions, collisions and other examples of intergalactic violence such as a black hole passing near a galaxy and “shredding it to spaghetti.” As part of his job, Margalit works closely with observational astronomers in selecting the kinds of places to look for transient events. He holds bachelor’s and master’s degrees from the Hebrew University of Jerusalem, and a Ph.D. from Columbia University.

Maru Sarazola is an assistant professor of mathematics. She joins Minnesota from Johns Hopkins University, where she was a J.J. Sylvester Assistant Professor. Sarazola received her Ph.D. from Cornell University. Her research is focused on algebraic topology—specifically, her interest lies in homotopy theory (a field that studies and classifies objects up to different notions of "sameness") and category theory (“the math of math,” which looks to abstract all structures to study their behavior). This fall, she is teaching Math 5285H: Honors Algebra I.

Eric Severson is an associate professor of mechanical engineering—and University of Minnesota alumnus (2008 bachelor’s and 2015 Ph.D. in electrical engineering). He returns to his alma mater after being on the University of Wisconsin-Madison faculty for six years. Severson leads research in electric machines and magnetic levitation, with a renewed focus in addressing grand challenges in energy and sustainability through multidisciplinary collaborations. His interests include extreme efficiency, bearingless machines, flywheel energy storage, and electric power grid technology.

Kelsey Stoerzinger is an associate professor of chemical engineering and materials science. She was on the faculty at Oregon State University, with a joint appointment in the Pacific Northwest National Laboratory. She studies the electrochemical transformation of molecules into fuels, chemical feedstocks, and recovered resources. Her research lab designs materials and processes for the storage of renewable electricity. Stoerzinger holds a bachelor’s from Northwestern University, master’s from University of Cambridge, and Ph.D. from MIT.

Lynn Walker is a professor—and the L.E. Scriven Chair in the Department of Chemical Engineering and Materials Science. Previously, she was on the faculty at Carnegie Mellon University. Her research focuses on developing the tools and fundamental understanding necessary to efficiently process soft materials and complex fluids. This expertise is being used to develop systematic approaches to incorporate sustainable feedstocks in consumer products. Walker holds a bachelor’s from the University of New Hampshire and Ph.D. from the University of Delaware. She was a postdoctoral researcher at Katholieke Universiteit Leuven in Belgium.

Alexander “Alex” Watson is an assistant professor of mathematics—and former University of Minnesota postdoctoral researcher in the School of Mathematics. Watson earned his Ph.D. at Columbia University. He works on mathematical models used to predict the electronic properties of materials, especially novel 2D materials such as graphene and twisted multilayer “moiré materials.” In summer 2022 and 2023, he presented at the U’s MathCEP Talented Youth Mathematics Program on topics related to materials research at the University of Minnesota.

Anna Weigandt is an assistant professor of mathematics. She comes to Minnesota from the Massachusetts Institute of Technology, where she was an instructor. Weigandt completed her Ph.D. at the University of Illinois, and she was a postdoctoral assistant professor in the Center for Inquiry Based Learning at University of Michigan. She works in algebraic combinatorics, specifically Schubert calculus. This fall 2023, she is teaching Math 5705: Enumerative Combinatorics.

Michael Wilking is a professor of physics—and University of Minnesota alumnus (2001 bachelor’s in chemical engineering). He holds a master’s and Ph.D. from the University of Colorado. Prior to his return to the Twin Cities campus, Wilking served on the faculty at Stony Brook University. He completed his post-doc at TRIUMF, Canada's national particle accelerator center. Wilking was part of the Stony Brook research team honored with the 2016 Breakthrough Prize in Fundamental Physics.

Benjamin "Ben" Worsfold is an assistant professor of civil engineering —and a licensed professional engineer in both California and Costa Rica. His research interest lies in large-scale structural testing, finite element analysis of reinforced concrete structures, and anchoring to concrete. Worsfold earned his master’s and Ph.D. from the University of California, Berkeley, and bachelor’s from the University of Costa Rica.

Yogatheesan Varatharajah is an assistant professor of computer science and engineering —and a visiting scientist in neurology at the Mayo Clinic. His research lies broadly in machine learning for health. Varatharajah earned his master’s and Ph.D. from the University of Illinois Urbana-Champaign. Prior to Minnesota, he was a research assistant professor of bioengineering at the University of Illinois and faculty affiliate for the Center for Artificial Intelligence Innovation with the National Center for Supercomputing Applications.

Starting in January 2024:

Emily Beverly is an incoming assistant professor of earth sciences. Prior to joining the University of Minnesota, she was on the faculty at University of Houston. She earned a bachelor’s from Trinity University, a master’s from Rutgers University, and a Ph.D. from Baylor University. Beverly was a postdoctoral researcher at Georgia State University and University of Michigan. Her research focuses on understanding environmental drivers of human and hominin evolution. Beverly uses stable isotopes and geochemistry to answer questions about past and future climates with a firm foundation in sedimentary geology and earth surface processes.

Alexander “Alex” Grenning is an assistant professor of chemistry. He comes to Minnesota from the University of Florida, where he was a tenured faculty. Grenning earned a bachelor’s in chemistry and music from Lake Forest College, and a Ph.D. in organic chemistry from the University of Kansas. He was a postdoctoral researcher at Boston University. His work is focused on chemical synthesis and drug discovery.

Yu Cao is an incoming professor of electrical and computer engineering. Prior to Minnesota, Cao was a professor at Arizona State University. He holds a bachelor’s in physics from Peking University and a master’s in biophysics plus a Ph.D. in electrical engineering and computer sciences from the University of California-Berkeley. His research includes neural-inspired computing, hardware design for on-chip learning, and reliable integration of nanoelectronics. Cao served as associate editor of the Institute of Electrical and Electronics Engineers’s monthly Transactions on CAD .

Edgar Peña is an incoming assistant professor of biomedical engineering—and a University of Minnesota alumnus (2017 Ph.D. in biomedical engineering). He is a neuromodulation scholar who is interested in vagus nerve stimulation. Peña earned his bachelor’s degrees in electrical engineering and biomedical engineering from the University of California, Irvine. During his doctoral studies at the University of Minnesota Twin Cities, he used computational models to optimize deep brain stimulation.

Seongjin Choi is an incoming assistant professor of civil engineering. He received his bachelor’s, master’s, and Ph.D. from the Korea Advanced Institute of Science and Technology. He was a postdoctoral researcher at McGill University. His work involves using data analytics to draw valuable insights from urban mobility data and applying cutting-edge AI technologies in the field of transportation.

Pedram Mortazavi is an incoming assistant professor of civil engineering— and a licensed structural engineer in Canada . His interests lie in structural resilience, steel structures, large-scale testing, development of damping and isolation systems, advanced simulation methods, and hybrid simulation. Mortazavi holds a bachelor’s from the University of Science and Culture in Iran, a master’s from Carleton University in Ottawa, and Ph.D. from the University of Toronto.

Gang Qiu is an incoming assistant professor of electrical and computer engineering. He received his bachelor’s degree from Peking University in microelectronics and his Ph.D. in electrical and computer engineering from Purdue University. (He is currently a postdoctoral researcher at the University of California, Los Angeles.) Qiu’s research focuses on novel low-dimensional materials for advanced electronics and quantum applications. His current interest includes employing topological materials for topological quantum computing.

Qianwen Wang is an incoming assistant professor of computer science and engineering. She received her bachelor’s from Xi’an Jiao Tong University and her Ph.D. from Hong Kong University of Science and Technology. Prior to Minnesota, Wang served as a post-doctoral researcher at Harvard University in the Department of Biomedical Informatics. As a visualization researcher, she created interactive visualization tools that enable humans to better interpret AI and generate insights from their data.

Katie (Yang) Zhao is an incoming assistant professor of electrical and computer engineering. Her research interest resides in the intersection between Domain-Specific Acceleration Chip and Computer Architecture. In particular, her work centers around enabling AI-powered intelligent functionalities on resource-constrained edge devices. Zhao received her bachelor’s and master’s from Fudan University, China, and Ph.D. from Rice University. (She is currently a postdoctoral researcher at Georgia Institute of Technology.)

Learn more about our goal to hire 60 new faculty in three years at the CSE recruiting website .

If you’d like to support faculty research in the University of Minnesota College of Science and Engineering, visit our CSE Giving website .

Join our winning team

Our unique combination of science and engineering within one college in a vibrant, metropolitan area means more opportunities for you. Learn about faculty openings.