Research Scientists

  • TB Schardl

    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

    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.

  • Cuong Nguyen

    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.