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.

Tom Body (CFS) - SPARC and Advanced Computing

Tom Body is a Boundary and Divertor Physicist with Commonwealth Fusion Systems. At CFS, Tom uses modelling to plan dissipative scenarios and develop detachment control algorithms for the SPARC and ARC tokamaks. He has contributed to several plasma physics projects throughout his career — including adding scrape-off-layer models to the cfspopcon scoping tool, using Hermes-3 to study detachment dynamics in 1D, and validating GRILLIX turbulence simulations against the "TCV-X21" dataset.

Megan Wart (CFS) - SPARC and Advanced Computing

Megan Wart is the Senior SPARC Neutronics Lead with Commonwealth Fusion Systems (CFS). In her four years at CFS, Megan has done neutronics analysis and development of workflows, including the implementation of HPC for nuclear codes. She has now moved into Technical Leadership focused on SPARC Neutronics and Management on the Nuclear Engineering Team. 

Nick Murphy (Center for Astrophysics - Harvard & Smithsonian) - Open Plasma Science and Best Practices

Nick Murphy is an astrophysicist at the Center for Astrophysics (CfA) in Cambridge, Massachusetts. Nick attended the University of Michigan as an undergraduate, and then went to the University of Wisconsin in Madison for graduate school in astronomy. Nick began believing in open source software while still a student, even going so far as to include Fortran subroutines in an appendix of their thesis.  Nick has been at the Center for Astrophysics for the last decade working largely on magnetic reconnection in solar eruptions.  Nick was a co-organizer of the Inclusive Astronomy 2015 conference and co-founded the American Astronomical Society's Working Group on Accessibility and Disability, and is now a member of the APS DPP Diversity Equity and Inclusion Organizing Collective Committee.  Over the last few years, Nick has been a core contributor to PlasmaPy, which is an open source software package for plasma research and education. 

Aaron Ho (PSFC) - Integrated Tokamak Plasma Modelling and the Need for (More) Speed

Aaron Ho is a research scientist working at the MIT Plasma Science and Fusion Center (PSFC). His research combines plasma transport knowledge with machine learning techniques to build fast surrogate models, with the overall aim of using them in the design and operation of fusion tokamak devices. Originally from Canada, Aaron completed his PhD in the Eindhoven University of Technology along with the Dutch Institute for Fundamental Energy Research (DIFFER).

Nathan Cummings (UKAEA) - FAIR Data for Fusion

Nathan is a Data Engineer at the UK Atomic Energy Authority with a background in Physics. He specialises in data quality, advocating for FAIR (Findable, Accessible, Interoperable and Reusable) and open data policies. He was involved in the FAIR4fusion project focussing on data provenance, as well being involved with FAIR data pipelines for COVID-19 modelling. His current focus is on developing an open and FAIR fusion database for both experimental and simulation data for as many fusion labs as possible, to be leveraged by ML engineers and researchers everywhere to push the boundaries of fusion energy research.

Alessandro Pau (EPFL-SPC) - Disruption Prediction and Avoidance in Tokamaks

Alessandro Pau is a research scientist at the Swiss Plasma Center (EPFL) in Lausanne. He is actively involved in various fusion research activities in the framework of the EUROfusion research programme, the IAEA and the International Tokamak Physics Activity (ITPA), where he coordinates high-level research topics on critical issues in tokamak physics and plasma control. His current research focuses on the study of the complex physics mechanisms leading to disruptions in tokamaks, which must be avoided in order to preserve the integrity of the machines and to enable the control of stable and high-performance plasma regimes. In this context, he has received several grants for the development of tools to enable the application of data-driven models to real-time control, and he is responsible for several collaborations and projects on the use of AI and machine learning in fusion research.