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.

Santiago Cadena (Proxima Fusion) - QI stellarator plasma boundaries: a new benchmark and dataset for optimization

Santiago Cadena is an ML Researcher and Engineer at Proxima Fusion, where he works on data-driven approaches for stellarator optimization. Previously, he has applied ML to real-world products around mapmaking at Lyft, and neuromotor interfaces at Meta. He completed M.Sc. and Ph.D. degrees in Computational Neuroscience and Machine Learning at the University of Tübingen and the Max Planck Institute for Intelligent Systems in Germany where he used deep learning modeling and large‑scale single‑cell neural recordings to study visual processing in the brain. He holds bachelors degrees in Electrical and Biomedical engineering from Universidad de los Andes. Passionate about cross‑disciplinary innovation, he thrives on applying AI/ML to novel scientific and engineering challenges. Full abstract of the talk available here.

Christopher Hansen (Columbia University) - Application of Open FUSION Toolkit to tokamak engineering and design

Chris Hansen is a Research Scientist at Columbia University and Associate Director of the Columbia Fusion Research Center. His research focuses on development, validation, and application of computational tools to bridge the gap between plasma/fusion science and engineering for commercial fusion devices. He is the lead developer of the Open FUSION Toolkit and has created or contributed to a number of community tools that support a high degree of design and physics fidelity. In addition to his core physics research, Dr. Hansen is also focused on expanding and broadening fusion education through open software, like OFT, and hardware platforms, like the Columbia University Tokamak for Education (CUTE). Full abstract of the talk available here.

Oak Nelson (Columbia University) - Utilizing Open Source Models for Fusion Analysis

Oak Nelson is an Associate Research Scientist at Columbia University, where he focuses on integrated modeling efforts aimed at advancing the readiness of tokamak scenarios for future fusion pilot plants. This work necessitates the development of large databases and semi-automated physics modules as well as dedicated investigations on numerous experimental tokamak facilities. Oak is also a co-lead of USFusionEnergy.org and several fusion outreach programs and leads numerous efforts related to the training, education, and support of the next generation of fusion physicists. Full abstract of the talk available here.

Tamara Norman (Google DeepMind) - Intro to JAX, its application for scientific simulation, and TORAX

Tamara Norman is a Software Engineer at Google DeepMind and the Tech Lead of the Fusion project there. She works on TORAX as well as other infrastructure for Fusion research and in particular how to create intuitive abstractions that are easy to interact with. Previously, she worked on training of large-scale AI models in JAX, especially on model partitioning strategies and associated user API through PartIR.  Full abstract of the talk available here.

Samuel Jackson & Nathan Cummings (UKAEA) - Sharing fusion data FAIRly with the Pandata Stack

Samuel Jackson is a Principal Data Scientist at the UKAEA. He holds a M.Eng degree in software engineering from the University of Aberystwyth. He has previously worked in various roles within the UK's large scale experimental facilities and national labs. He has been involved in numerous projects towards improving scientific data analysis and reduction workflows. His expertise are in machine learning, software engineering, and high performance computing.

Nathan is a Data Engineer at the UK Atomic Energy Authority with a background in Physics. He specializes in data quality, advocating for FAIR (Findable, Accessible, Interoperable and Reusable) and open data policies. He was involved in the FAIR4fusion project focusing 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.

Full abstract of the talk available here.