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

Dr. Albert Reuther is the manager 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. 

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”.  

Nicolò Riva (PSFC) -  Superconductivity in the Era of Artificial Intelligence, High Parallel Computing and Big Data

 Nicolò Riva is a postdoctoral associate at MIT and collaborates with startups Commonwealth Fusion System and Type One Energy on the design, modeling, fabrication, and testing of superconducting coils for fusion devices. Nicolò has been actively involved in innovative solutions development, including leading a modeling student team in the SpaceX Hyperloop Competition. Nicolò aspires to contribute to the acceleration of fusion and superconductivity-based technologies' design, while also engaging in scientific dissemination. His current work tackles the numerical analysis, fabrication, and testing of non-planar demonstrators for stellarators using VIPER cable, showcasing its viability even after bending into non-planar shapes. His work has been recognized with the IEEE CSC Graduate Study Fellowship 2019 and the EPFL IC School Special Prize 2019.

Alex Creely (CFS) - Q&A about SPARC endeavor and computational challenges

Alex Creely is dedicated to putting fusion energy on the grid on a timescale that is relevant to combat climate change.  As the Head of Tokamak Operations at Commonwealth Fusion Systems (CFS), Alex leads physics work and operational planning for the world's first net-energy fusion device, the SPARC tokamak.  Originally from St. Louis, Missouri, Alex studied Mechanical Engineering at Princeton University as an undergraduate before completing his PhD in Applied Plasma Physics at MIT.  He has worked on fusion devices around the world, including Alcator C-Mod at MIT, ASDEX Upgrade at IPP Garching in Germany, and LHD at NIFS in Japan.

Megan Wart (CFS) - Q&A about SPARC endeavor and computational challenges

Megan Wart is a Nuclear and Radiological Engineer with Commonwealth Fusion Systems (CFS). She received her Master’s degree in Nuclear Engineering from Penn State in 2017 where her studies and research were focused on Nuclear Securities. After graduation, she worked in radiation detection before joining CFS in 2020. At CFS, Megan does neutronics analysis and development of workflows, including the implementation of HPC for nuclear codes. 

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. 

Erika Ye (MIT) - Quantum and Quantum-Inspired Methods for Solving PDEs

Erika was a postdoc at the PSFC but will be starting at Lawrence Berkeley National Lab as an Alvarez fellow. Her research is focused on developing quantum-inspired tensor network algorithms for solving partial differential equations, which rely on making low-rank approximations to perform calculations with reduced computational resources. Though the algorithms themselves are problem agnostic, over the past two years, she has been looking at their performance specifically for solving the collisionless Vlasov-Maxwell’s equations. Erika also dabbles with quantum computing algorithms for solving nonlinear PDEs because of the close connections between the two fields.

Robert Gramacy (Virginia Tech) - Surrogate Modeling and Bayesian Optimization

Robert Gramacy is a Professor of Statistics in the College of Science at Virginia Polytechnic and State University (Virginia Tech/VT) and affiliate faculty in VT's Computational Modeling and Data Analytics program. Previously, he was an Associate Professor of Econometrics and Statistics at the Booth School of Business, and a fellow of the Computation Institute at The University of Chicago. My research interests include Bayesian modeling methodology, statistical computing, Monte Carlo inference, nonparametric regression, sequential design, and optimization under uncertainty. 

Nathan Cummings (UKAEA) - Database development for magnetic fusion applications

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.

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.

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.