A. 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.

J. Wright (PSFC) - PSFC/MIT Computing

John Wright is a plasma physicist working on the theory and simulation of radio frequency heating and current drive in fusion plasmas. He has also dabbled in high temperature superconductors. In the course of developing massive parallel versions of the popular radio frequency codes, TORIC and TORLH, he has played a role in maintaining and operating several computer clusters, including Engaging, the computer used for this class. He is interested in parallel programming, scientific data management, and fusion. He has been awarded the Infinite Mile Award at MIT for supporting parallel computer infrastructure and the Landau-Spitzer award for outstanding technical contributions to heating scenarios in collaboration with European colleagues. He received his Doctorate in Plasma Physics from Princeton University in 1998.

T. Mattson (Intel) - Parallel Programming with OpenMP & Programming Clusters (MPI) and GPUs

Tim Mattson (Ph.D. Chemistry, UCSC, 1985) is a senior principal engineer in 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”.

P. Rodriguez-Fernandez (PSFC) - Optimization projects for the advancement of fusion energy

Dr. Pablo Rodriguez-Fernandez works as a research scientist at the Massachusetts Institute of Technology (MIT) Plasma Science and Fusion Center. Pablo studied Industrial Engineering at the Polytechnic University of Madrid (2013), and holds a master’s degree in Mechanical Engineering from Drexel University (2014). Despite his engineering background, Pablo focused on the study of fundamental transport phenomena with computational plasma physics for this doctoral degree, which he received in 2019 from MIT. In his work, Pablo utilizes state-of-the-art machine learning techniques to solve complex physics and engineering optimization problems. He has been leading the efforts of predicting the performance of the SPARC tokamak, poised to be the first magnetic-confinement fusion device to study net-energy plasmas, and his papers on the topic are among the most read in Nuclear Fusion. Despite his young age, Pablo has authored over 30 peer-reviewed journal papers, a third of which as first author. He has been the recipient of important awards during his academic career, including the Manson Benedict Award and Del Favero Doctoral Thesis Prize at MIT, and was listed by Forbes magazine as part of their 30 Under 30 list in Science in 2021.

N. Mandell (PSFC) - Optimizing the performance of fusion reactors with transport in the loop

Noah Mandell received his Bachelors degree in Physics from University of Maryland - College Park (2014) and his PhD in Astrophysical Sciences (Plasma Physics) from Princeton University (2021). He joined the Loureiro group at the MIT PSFC in 2021 as a DOE Fusion Energy Sciences postdoctoral fellow. His research interests primarily focus on modeling turbulent transport in fusion devices. His PhD dissertation, advised by Greg Hammett, focused on electromagnetic gyrokinetic modeling of the tokamak scrape-off layer. As an undergraduate, he worked with Bill Dorland on gyrofluid and gyrokinetic modeling of turbulence in the core of tokamaks. Noah is one of the primary developers of the gyrokinetic codes Gkeyll and GX. Noah also shared the 2022 Frederick A. Howes Scholar in Computational Science award, which recognizes research accomplishments and outstanding leadership, integrity and character.

A. 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.

M. 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.

A. Pavone (IPP Greifswald) - Machine learning and Bayesian modeling at Wendelstein 7-X

Andrea Pavone obtained his Ph.D. in Physics in 2020 working for the W7-X experiment on combining machine learning and Bayesian methods to interpret experimental data from fusion experiments. Now he is a postdoctoral researcher at the IPP in Greifswald where he continues his work on developing data analysis pipelines for the W7-X experiment using Bayesian modeling of plasma diagnostics and machine learning surrogate inference for the fast reconstruction of the plasma state from diagnostic measurements.

N. 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 his 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.

A. Dubey (ANL) - Software Productivity and Sustainability in Computational Science

Anshu Dubey is a Computational Scientist with deep experience in design, architecture and sustainability of multiphysics scientific software used on high performance computing platforms. Her contributions in the area of software engineering for research scientific software are widely known and respected. She has been the chief software architect for FLASH, now Flash-X, a multiphysics multicomponent software that has been used by several science domains including astrophysics, cosmology, solar physics, bio-mechanical systems, computational fluid dynamics and laser plasma experiments. She serves on the editorial boards of some of the leading journals in high performance computing. She has also served as the lead for Earth and Space Science Applications in the Exascale Computing Project.

R. 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.

M. 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.

L. Pinto (NYU Courant Institute of Mathematical Sciences) - Reinforcement Learning and Decision Making

Lerrel Pinto is an Assistant Professor of Computer Science at NYU Courant working on problems in Robotics and Machine Learning. Lerrel is also affiliated with the Center for Data Science. Lerrel is part of the CILVR (Computational Intelligence, Learning, Vision and Robotics) group. The lab’s goal is to get robots to generalize and adapt in the diverse world we live in. To this end, Lerrel's research touches the areas of Robot Learning, Representation Learning, Reinforcement Learning, and Affordable Robotics.

F. Felici (SPC EPFL) - Magnetic control of TCV tokamak plasmas through Deep Reinforcement Learning

Federico Felici is a Research Scientist at the Swiss Plasma Center (SPC) at EPFL, Lausanne. He holds an MSc degree in Systems & Control from Delft University of Technology (2005) and a PhD in Plasma Physics from the Swiss Plasma Center at EPFL, Switzerland (2011). He currently leads the research activities at SPC-EPFL in the area of advanced plasma control. His current research interests include all aspects of tokamak plasma control, with a strong focus on model-based approaches for practical implementation of control across various current and future fusion research devices.