Albert Reuther (MIT Lincoln Lab) - Practical HPC, Architectures & Workflows

Dr. Albert Reuther is a technical lead of the MIT Lincoln Laboratory Supercomputing Center (LLSC). He brought supercomputing to Lincoln Laboratory through the establishment of LLGrid, founded the LLSC, and leads the LLSC Computational Science and Engineering team. He developed the gridMatlab high-performance computing (HPC) cluster toolbox for pMatlab and is the computer system architect of the MIT Supercloud and numerous interactive supercomputing clusters based on Supercloud, including those in the LLSC. 

Lauren Milechin (MIT) - CPS-FR Computer Resources

Lauren Milechin is Lead Facilitator at MIT Office of Research Computing and Data, where she leads the training, education, and support of MIT's research computing resources. She started her career with the Lincoln Laboratory Supercomputing Center as Associate Technical Staff. Lauren's interests and projects have involved high performance computing (HPC), HPC education and training, database technology, machine learning, and applications of these topics in other fields. 

Tim Mattson (The Human Learning Group) - Parallel Programming with OpenMP & Programming Clusters (MPI) and GPUs

Tim Mattson (Ph.D. Chemistry, UCSC, 1985) is a retired senior principal engineer from Intel’s parallel computing lab.  He has been with Intel since 1993 and has worked on: (1) the first TFLOP computer (ASCI Red), (2) MPI, OpenMP and OpenCL, (3) two different research processors (Intel's TFLOP chip and the 48 core SCC), (4) Data management systems (Polystore systems and Array-based storage engines), and (5) the GraphBLAS API for expressing graph algorithms as sparse linear algebra. Tim has well over 150 publications including five books on different aspects of parallel computing, the latest (Published November 2019) titled “The OpenMP Common Core: making OpenMP Simple Again”.  

Tamara Broderick (MIT) - Surrogate Modeling and Bayesian Optimization

Tamara Broderick is an Associate Professor with tenure in the Department of Electrical Engineering and Computer Science at MIT. She is a member of the MIT Laboratory for Information and Decision Systems (LIDS), the MIT Statistics and Data Science Center, and the Institute for Data, Systems, and Society (IDSS). She completed her Ph.D. in Statistics at the University of California, Berkeley in 2014. Previously, she received an AB in Mathematics from Princeton University (2007), a Master of Advanced Study for completion of Part III of the Mathematical Tripos from the University of Cambridge (2008), an MPhil by research in Physics from the University of Cambridge (2009), and an MS in Computer Science from the University of California, Berkeley (2013). Her recent research has focused on fast, easy-to-use, and reliable methods for quantifying uncertainty and robustness. She has been awarded designation as an IMS Fellow (2024), selection to the COPSS Leadership Academy (2021), an Early Career Grant (ECG) from the Office of Naval Research (2020), an AISTATS Notable Paper Award (2019), an NSF CAREER Award (2018), a Sloan Research Fellowship (2018), an Army Research Office Young Investigator Program (YIP) award (2017), Google Faculty Research Awards, an Amazon Research Award, the ISBA Lifetime Members Junior Researcher Award, the Savage Award (for an outstanding doctoral dissertation in Bayesian theory and methods), the Evelyn Fix Memorial Medal and Citation (for the Ph.D. student on the Berkeley campus showing the greatest promise in statistical research), the Berkeley Fellowship, an NSF Graduate Research Fellowship, a Marshall Scholarship, and the Phi Beta Kappa Prize (for the graduating Princeton senior with the highest academic average).

Michelle Kuchera (Davidson College) - Machine and Deep Learning

Michelle Kuchera is a computational scientist and Associate Professor of Physics and Computer Science at Davidson College in North Carolina. She is the PI of the Algorithms for Learning in Physics Applications (ALPhA) group, where she collaborates with scientists at the Facility for Rare Isotope Beams, Jefferson Lab, and CERN on machine learning applications. She is particularly interested in uncertainty quantification in machine learning.