Leadership

Youssef Marzouk

Aeronautics and Astronautics
  • Principal Investigator
  • Center for Computational Science and Engineering
  • Statistics and Data Science Center

Youssef Marzouk is a professor in the Department of Aeronautics and Astronautics at MIT and co-director of the Center for Computational Science and Engineering. He is also a core member of the Statistics and Data Science Center and director of the Aerospace Computational Design Laboratory. His research interests lie at the intersection of computation and statistical inference with physical modeling. He develops new methodologies for uncertainty quantification, Bayesian modeling and computation, data assimilation, experimental design, and machine learning in complex physical systems. His methodological work is motivated by a wide variety of engineering and environmental applications. He is an avid coffee drinker and occasional classical pianist.

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Nicolas Hadjiconstantinou

Mechanical Engineering
  • Center for Computational Science and Engineering

Nicolas Hadjiconstantinou is a professor in the Department of Mechanical Engineering and is co-director of the Center for Computational Science and Engineering. His research interests are focused in the kinetic transport for small-scale fluid flow and solid-state heat transfer applications, in the molecular and stochastic simulation of nanoscale transport phenomena and in the molecular and multiscale simulation method development.

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Alan Edelman

Mathematics
  • Center for Computational Science and Engineering
  • Computer Science and Artificial Intelligence Laboratory

Alan Edelman is an applied mathematics professor in the Department of Mathematics at MIT. He is also a member of the Computer Science and Artificial Intelligence Laboratory and leads the Julia Lab. His research includes high-performance computing, numerical computation, linear algebra, random matrix theory, and scientific machine learning. He is also chief scientist at Julia Computing. His computational thinking class has been widely viewed worldwide because of the unique way Julia combines computer science, mathematics, science, and engineering.

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Senior Investigators

Jaime Peraire

Aeronautics and Astronautics
  • Center for Computational Science and Engineering

Jamie Peraire is the H.N. Slater Professor in the Department of Aeronautics and Astronautics at MIT. His research interests include computational aerodynamics — he is also the director of the Aerospace Computational Design Lab — as well as simulation-based design and numerical analysis. His work has applications in the areas of computational compressible-fluid dynamics and other multidisciplinary problems in aeronautics. Several software products based on Peraire’s research — in particular, the FELISA suite of codes — are used throughout the aerospace industry.

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Charles E. Leiserson

Electrical Engineering and Computer Science
  • Computer Science and Artificial Intelligence Laboratory

Charles E. Leiserson is a professor of computer science and engineering in the Department of Electrical Engineering and Computer Science at MIT. His research centers on the theory of parallel computing, especially as it relates to engineering reality. He co-authored the first paper on systolic architectures. He invented the re-timing method of digital-circuit optimization and developed the algorithmic theory behind it. On leave from MIT at Thinking Machines Corporation, he designed and led the implementation of the network architecture for the Connection Machine Model CM-5 Supercomputer. This machine was the world’s most powerful supercomputer in the early 1990’s and it incorporated the“universal” fat-tree interconnection network he developed at MIT. Fat-trees are now the preferred interconnect strategy for Infiniband technology. He introduced the notion of cache-oblivious algorithms, which exploit the memory hierarchy near optimally while containing no tuning parameters for cache size or cache-line length. He developed the Cilk multi-threaded programming language and runtime system, which featured the first provably efficient work-stealing scheduler. He led the development of several Cilk-based parallel chess-playing programs, including Socrates and Cilkchess, which won numerous prizes in international competition. On leave from MIT as Director of System Architecture at Akamai Technologies, he led the engineering team that developed a worldwide content-distribution network with tens of thousands of Internet servers. He founded Cilk Arts, Inc., which developed the Cilk++ multi-core concurrency platform. Intel Corporation acquired Cilk Arts in 2009, and Cilk technology is available in many compilers today.

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Heather Kulik

Chemical Engineering
  • Center for Computational Science and Engineering

Heather J. Kulik is an associate professor in the Department of Chemical Engineering at MIT. Her work has been recognized by a Burroughs Wellcome Fund Career Award at the Scientific Interface (2012-2017), Office of Naval Research Young Investigator Award (2018), DARPA Young Faculty Award (2018), AAAS Marion Milligan Mason Award (2019-2020), NSF CAREER Award (2019), the Industrial & Engineering Chemistry Research “Class of Influential Researchers”, the ACS COMP Division OpenEye Award for Outstanding Junior Faculty in Computational Chemistry, the JPCB Lectureship (ACS PHYS), the DARPA Director’s Fellowship (2020), MSDE Outstanding Early-Career Paper Award (2021), and a Sloan Fellowship (2021).

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Asegun Henry

Mechanical Engineering

Asegun Henry is an associate professor in the Department of Mechanical Engineering at MIT where he directs the Atomistic Simulation & Energy (ASE) Research Group. His primary research is in heat transfer, with an emphasis on understanding the science of energy transport, storage, and conversion at the atomic level, along with the development of new industrial scale energy technologies to mitigate climate change. Prof. Henry has made significant advances and contributions to several fields within energy and heat transfer, namely: solar fuels and thermochemistry, phonon transport in disordered materials, phonon transport at interfaces, and he has developed the highest temperature pump on record, which used an all-ceramic mechanical pump, to pump liquid metal above 1400°C. This technological breakthrough, which is now in the Guinness Book of World Records, has opened the door for new high temperature energy systems concepts, such as methane cracking for CO2 free hydrogen production, and a new grid level energy storage approach affectionately known as “Sun in a Box,” that is slated to be cheaper than pumped hydro.

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Saman Amarasinghe

Electrical Engineering and Computer Science
  • Computer Science and Artificial Intelligence Laboratory

Saman Amarasinghe leads the Commit compiler research group in the Computer Science and Artificial Intelligence Laboratory at MIT, which focuses on programming languages and compilers that maximize application performance on modern computing platforms. He is a world leader in the field of high-performance domain-specific languages. Prof. Amarasinghe’s group developed the Halide, TACO, Simit, StreamIt, StreamJIT, PetaBricks, MILK, Cimple, and GraphIt domain-specific languages and compilers, all of which combine language design and sophisticated compilation techniques to deliver unprecedented performance for targeted application domains such as image processing, stream computations, and graph analytics. He also pioneered the application of machine learning for compiler optimizations, from Meta optimization in 2003 to OpenTuner extendable autotuner today. With Prof. Anant Agarwal, he co-led the Raw architecture project, which did pioneering work on scalable multicores.

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Research Scientists

TB Schardl

  • Computer Science and Artificial Intelligence Laboratory

Tao B. (TB) Schardl is a research scientist in the Computer Science and Artificial Intelligence Laboratory at MIT. He runs the Supertech research group with Prof. Charles E. Leiserson, and he is Chief Architect of OpenCilk, a new open-source platform for task-parallel programming. His research aims to make it easy for programmers, including nonexperts, to write fast code in the post-Moore era. His research combines algorithms and systems to develop technologies that enable principled, scientific approaches to software performance engineering. His research spans compilers, runtime systems, software analysis tools, programming models, theories of performance, and parallel algorithms and applications. His work on the Tapir/LLVM compiler received the best paper award at ACM PPoPP in 2017.

Mehdi Pishahang

Mechanical Engineering

Mehdi Pishahang is a research scientist in the Department of Mechanical Engineering at MIT and is a member of the Atomistic Simulation and Energy (ASE) Research Group. Dr. Pishahang’s research focuses on development of crucial components used in high temperature thermal and thermo-chemical energy conversion and storage systems. His main contribution to the CESMIX project is experimental validation of high temperature oxidation and nitridation.

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Cuong Nguyen

Aeronautics and Astronautics
  • Chief Software Architect, CESMIX

Dr.  Cuong Nguyen is a principal research scientist in the Department of Aeronautics and Astronautics. He started his academic career at MIT as a postdoctoral associate after receiving his PhD degree from National University of Singapore in 2005. He was awarded the Springer Computational Science and Engineering (CSE) Prize in 2009 for the development of a software package rbMIT. He is the developer of the open-source software Exasim for solving partial differential equations using the discontinuous Galerkin method. He is currently developing a software package to fit machine learning potentials for atomistic simulations. His research interests include numerical methods for partial differential equations, computational mechanics, molecular dynamics, and parallel computing.

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Postdoctoral Associates

Ralf Meyer

Chemical Engineering
  • Kulik Group

Ralf joined Heather Kulik’s group in the department of Chemical Engineering as a postdoctoral associate in November 2021. He received his Ph.D. from Graz University of Technology in Austria with Prof. Andreas Hauser. His Ph.D. focused on machine learning based local structure search. In the Kulik group, he is working on automated geometry optimization of transition metal complexes, the development of force fields and DFT corrections, and the data-driven discovery of novel mechnophores.

Changwan Hong

  • Computer Science & Artificial Intelligence Laboratory

Kiarash Gordiz

  • Atomistic Simulation & Energy Research Group

Kiarash is a postdoctoral associate in ASE Lab, where he is working on two projects: (1) to develop new strategies for cooling of ultra-high band-gap power electronics by studying different atomic configurations around the interface region, and (2) in collaboration with with Prof. Yang Shao-Horn at MIT, to study the effect of phonons on solid-state ionic diffusion and to find better performing solid-state ionic conductors to be used in next generation solid-state batteries and solid oxide fuel cells. Before joining ASE Lab, Kiarash was a postdoc at Colorado School of Mines working with Prof. Eric Toberer, where he obtained extensive experience in experimental materials discovery and solid-state synthesis for thermoeletric applications. Kiarash obtained his PhD in 2017 from Georgia Tech under the supervision of Prof. Asegun Henry, where they developed the interface conductance modal analysis (ICMA) method to study the solid-solid interfacial heat transfer.

Beside studying atoms, Kiarash enjoys hiking, rock-climbing and playing music.

Yeongsu Cho

Chemical Engineering
  • Kulik Group

Yeongsu joined Heather Kulik’s group in the department of Chemical Engineering as a postdoc in October 2021. She received her Ph.D. at Columbia University with Prof. Timothy Berkelbach. Her PhD focused on developing semiempirical methods for excited states of nanomaterials. In the Kulik group, she is working on machine-learning-guided adaptive methods for multi-resolution materials modeling.

Spencer Thomas Wyant

Materials Science and Engineering

Spencer Thomas Wyant is a doctoral candidate in the Department of Materials Science and Engineering at MIT, working in the research lab of Asegun Henry, associate professor of mechanical engineering. Previously, he obtained his BS in chemical engineering at the University of Massachusetts Amherst. His current research focus is on the development of accurate interatomic potentials to model heat transport at solid-solid interfaces. In particular, he is developing different kinds of machine learning interatomic potentials to model interface systems like Ge-GaAs, Al-Al2O3, and AlN-GaN.

Emmanuel Lujan

  • Computer Science and Artificial Intelligence Laboratory (CSAIL)

Emmanuel Lujan is a postdoctoral associate in the Julia Lab based in the Computer Science and Artificial Intelligence Laboratory at MIT. As part of CESMIX, he works on novel methods, composable software abstractions, and Julia tools to facilitate the development of large-scale material simulations, as well as to contribute to a better integration of the atomistic software ecosystem. In particular, he works on facilitating the learning of interatomic potentials and forces, ensuring fast execution, leveraging integration with state-of-the-art tools such as the latest developments in Julia around scientific machine learning (SciML), automatic differentiation (Enzyme.jl, Zygote.jl) and GPU abstractions (CUDA.jl).

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Graduate Students

Loek Van Heyningen

Aeronautics and Astronautics
  • Center for Computational Science and Engineering

Loek Van Heyningen is a PhD student in the Department of Aeronautics and Astronautics and the Center for Computational Science and Engineering, advised by Prof. Jaime Peraire and Dr. Cuong Nguyen. His research focuses on the development of high-order methods for modern HPC architectures. For CESMIX, he is supporting hypersonic flow simulation and modeling efforts.

August Trollback

Dionysios Sema

Mechanical Engineering
  • Center for Computational Science and Engineering

Dionysios Sema obtained his diploma in chemical engineering from the National Technical University of Athens. He is a PhD student in the Department of Mechanical Engineering affiliated with the Center for Computational Science and Engineering at MIT. As part of CESMIX, he performs research at the interface between classical and ab initio simulations and machine-learned reactive potential development. His current research interests include molecular dynamics, machine learning, material science and quantum computing.

William Moses

  • Computer Science and Artificial Intelligence Laboratory

William (Billy) Moses is a PhD candidate at MIT, where he also received his MEng in electrical engineering and computer science (EECS) and BS in EECS and physics. William researches compilers and program representations that enable performance and use-case portability, enabling non-experts to leverage HPC and ML. He is the lead developer of Enzyme (NeurIPS ’20, SC ’21), an LLVM plugin for generating fast derivatives of programs in multiple languages/architectures, and Polygeist (PACT ’21), a polyhedral compiler and C++ frontend for MLIR. He also worked on the Tensor Comprehensions framework for synthesizing fast GPU kernels, the Tapir compiler for parallel programs (best paper at PPoPP ’17). He is a recipient of the U.S. Department of Energy Computational Science Graduate Fellowship and the Karl Taylor Compton Prize, MIT’s highest student award.

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Julien Luzzatto

  • Center for Computational Science and Engineering

Julien Luzzatto is a SM student in the Center for Computational Science and Engineering at MIT, where he is co-advised by Prof. Youssef Marzouk and Prof. Nicolas Hadjiconstantinou. As part of CESMIX, he performs research at the interface between molecular modeling, multiscale simulation, and long-time integration.

Kate Fisher

Kate Fisher is a student in the Uncertainty Quantification group. Her current research focuses on representing the uncertainty that arises from functional approximation choice in density functional theory. She graduated from the University of Texas at Austin with bachelor’s degrees in computational

Ryan Deng

  • Computer Science & Artificial Intelligence Laboratory

Teo Collin

  • Computer Science and Artificial Intelligence Laboratory

Teo Collin is a PhD student affiliated with the COMMIT group at the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT where he is advised by Prof. Saman Amarasinghe. His broader research interests are at the intersection of compilers, programming languages, scientific computing, computer algebra, and numerical analysis. As part of CESMIX, Collin is working on building domain specific languages that allow scientists to write efficient simulations without too much effort. Currently he is particularly interested in languages for high-level linear algebra across many domains.

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Valentin Churavy

  • Computer Science and Artificial Intelligence Laboratory

Valentin Churavy is a PhD student in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. As part of CESMIX, he is working on distributed heterogeneous computing with Julia.

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Joanna Zou

  • Computational Science and Engineering

Joanna Zou is a PhD student in Computational Science and Engineering at MIT. As a part of CESMIX, she is working on Bayesian inference techniques for interatomic potentials models. Prior to joining MIT, she worked on data assimilation with latent variable models as a visiting scholar at TU Delft and surrogate model development as a Masters student at Stanford University.

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Undergraduate Researchers

Alice Chen

  • Computer Science & Artificial Intelligence Laboratory

External Collaborators

James Schloss

Earth, Atmospheric, and Planetary Sciences
  • Program in Atmospheres, Oceans and Climate

James Schloss is a postdoc in the Program in Atmospheres, Oceans and Climates at MIT. Schloss studies complicated physical systems with HPC methods, including GPU computing. During his PhD, he focused on massively parallel implementations of superfluid simulations on graphics processing units and will be using this expertise on largescale climate models for the Climate Modeling Alliance. His focus will be on writing and optimizing discontinuous Galerkin and timestepping methods for modeling the ocean on distributed graphics processing units and high-performance computers using the Julia language. Schloss has a PhD from the Okinawa Institute of Science and Technology Graduate University and a bachelor’s degree in Physics from Auburn University.

Michael F. Herbst

RWTH Aachen University

Michael F. Herbst is a postdoctoral researcher with Benjamin Stamm at the Applied and Computational Mathematics research lab at RWTH Aachen University in Germany. Having obtained a PhD in theoretical chemistry as well as multiple years of research experience in applied mathematics, Michael’s interests span broadly within the field of materials modeling. His current research concentrates on developing efficient and robust simulation algorithms for electronic-structure theory simulations by combining physical principles as well as rigorous methods to control simulation error. As one of the lead developers of the DFTK Julia density-functional theory software, Michael is involved with both the uncertainty quantification as well as the software development efforts at CESMIX.

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Alumni

Sualeh Asif

Jeremiah DeGreeff

Electrical Engineering and Computer Science
  • Computer Science and Artificial Intelligence Laboratory

Jeremiah DeGreeff is an undergraduate student in the Department of Electrical Engineering and Computer Science at MIT. He is doing undergraduate research in the Computer Science and Artificial Intelligence Laboratory under Alan Edelman. As part of CESMIX, he is working on a composable suite of Julia packages to support various molecular simulation workflows.

Drew Rohskopf

Mechanical Engineering

Drew Rohskopf is a PhD student under Asegun Henry at MIT. His research interests involve predicting how molecules move and interact, and how these interactions influence chemical, material, or biological phenomena. His PhD thesis involved (1) improving the speed & accuracy of molecular simulations by producing quantum-accurate potential energy surfaces, and (2) a formalism for simulating vibrational energy transfer in solids, leading to a new physical picture of phonon transport. In the CESMIX project he develops interatomic potentials to aid more accurate molecular simulations, and is also involved in developing software for next-generation molecular simulation platforms.

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Matthew Swisher

Mechanical Engineering

Mathew Swisher is a postdoctoral associate in the Department of Mechanical Engineering at MIT. His research focuses on using statistical mechanics and molecular dynamics to model material properties and interfacial phenomena. As part of CESMIX, he performs research on molecular modeling of oxygen adsorption and diffusion processes.

Dallas Foster

Aeronautics and Astronautics

Dallas Foster is a postdoctoral associate in the MIT Department of Aeronautics and Astronautics, as a member of the Uncertainty Quantification group within the Aerospace Computational Design Laboratory. Dallas’ research focuses on Bayesian inference techniques for data-driven physical models with additional research interests in data assimilation, scientific computing, and machine learning with applications to climate, geophysics, and chemistry. As part of the CESMIX project, he is developing Uncertainty Quantification techniques that couple probabilistic inference through quantum and molecular scales.

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Isuru Ariayrathna

  • Kulik Group

Isuru Ariyarathna is a postdoctoral associate in the Kulik Group at MIT, having completed his PhD at Auburn University with Prof. Evangelos Miliordos. His dissertation focused on high-level ab initio calculations of molecules to understand their chemical and physical properties. As part of the CESMIX project his investigations are focused on multireference wave function theory and density functional theory.

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Jayanth Jagular-Mohan

Aeronautics and Astronautics

Jayanth Jagalur-Mohan is a research scientist in the Department of Aeronautics and Astronautics at MIT, and is affiliated with the Uncertainty Quantification Group. His research is broadly in the areas of Bayesian methods, statistical inference, machine learning, and experimental design. His work focuses on developing novel algorithms motivated by a variety of complex physical systems, and he is also interested in studying the theoretical limits of such computational methodologies. Within the CESMIX project, his efforts are focused on various aspects related to Uncertainty Quantification and active learning thrust.

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