Bio: Sudip Dosanjh is the director of the National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory. NERSC’s mission is to accelerate scientific discovery at the US Department of Energy’s Office of Science through high performance computing and extreme data analysis. NERSC deploys leading-edge computational and data resources for over 10,000 users from a broad range of science disciplines. NERSC will be partnering with computer companies to develop and deploy pre-exascale and exascale systems during the next decade.

Previously, he headed extreme-scale computing at Sandia National Laboratories. He was co-director of the Los Alamos/Sandia Alliance for Computing at the Extreme Scale from 2008-2012 and served on the US Department of Energy’s Exascale Initiative Steering Committee for several years.

Dosanjh had a key role in establishing co-design as a methodology for reaching exascale computing. He has numerous publications on exascale computing, co-design, computer architectures, massively parallel computing, and computational science.

He earned his bachelor’s degree in engineering physics in 1982, his master’s degree (1984) and Ph.D. (1986) in mechanical engineering, all from the University of California, Berkeley.

Abstract

The first generation of exascale computing systems are online along with powerful new application capabilities and system software. At the same time, demands for high performance computing continue to grow for more powerful simulations, adoption of machine learning methods, and huge data analysis problems arising for new instruments and increasingly ubiquitous data collection devices.  In its broadest sense, computational science research is expanding beyond physical and life sciences into social sciences, public policy, and even the humanities.

With chip technology facing scaling limits and diminishing benefits of weak scaling, it will be increasingly difficult to meet these new demands.  Disruptions in the computing marketplace, which include supply chain limitations, a shrinking set of system integrators, and the growing influence of cloud providers are changing underlying assumptions about how to acquire and deploy future supercomputers.  At the same time there are discussions around cloud computing, specialized hardware, and the role of AI, both as a technique in science and as a driver of computing hardware and software.

In this talk I’ll present some of the findings of a US National Academies consensus report on the future of post-exascale computing, which states that business as usual will not be sufficient.  I will also give my own perspectives on some of the challenges and opportunities faced by the research community. 

Speaker Bio

Katherine Yelick is the Vice Chancellor for Research at the University of California, Berkeley, where she also holds the Robert S. Pepper Distinguished Professor of Electrical Engineering and Computer Sciences. She is also a Senior Faculty Scientist at Lawrence Berkeley National Laboratory. She has been recognized for her research and leadership in high performance computing and is a member of the National Academy of Engineering and the American Academy of Arts and Sciences.

Abstract

I will review some of the key discoveries in particle physics and cosmology over the past thirty years that have been enabled with NERSC, and highlight future opportunities.

Speaker Bio

Natalie Roe is a particle physicist and observational cosmologist whose career has spanned searches for new particles at colliders, studies of fundamental symmetries in rare particle decays, and large-scale surveys of the universe to study dark energy and the early universe.  She has led the design and construction of advanced instrumentation to enable experiments at the cutting edge of both particle physics and cosmology, including the forward tracker for the Anomalous Single Photon experiment and the Silicon Vertex Tracker for the BaBar experiment, both at SLAC, the fabrication of fully-depleted CCDs at Berkeley Lab’s MicroSystems Laboratory for cosmological surveys, and a major upgrade of the spectrographs for the Sloan Digital Sky Survey Baryon Oscillation Spectroscopic Survey.

Roe is a Fellow of the American Association for the Advancement of Science and the American Physical Society. She has served as president of the APS Division of Particles and Fields, as well as numerous national and international advisory committees including the DOE/NSF High Energy Physics Advisory Panel (HEPAP), CERN Scientific Policy Committee, Fermilab Physics Advisory Committee and the Visiting Committee for the NSF Division of Mathematical and Physical Sciences. 

As Director of the Physics Division, Roe oversaw an expansion of research into dark matter, dark energy, cosmic microwave background studies, and quantum information science. Appointed as the Associate Laboratory Director for Physical Sciences in July 2020, Natalie Roe champions LBNL’s highly-accomplished research divisions in accelerator technology, engineering, nuclear science, particle physics and cosmology.  She led the formation of the first mentoring program in the Physical Sciences Area, and serves as the executive sponsor for the Lab’s Early Career Employee Resource Group. She was a founding member of the Lab’s Women Scientists and Engineers Council (WSEC).

Natalie Roe received her PhD in Physics in 1989 from Stanford University. She joined the Lab as a postdoctoral fellow and held positions as Staff Scientist, Senior Scientist and Physics Division Director prior to taking on the ALD role.

Abstract

Studying low-likelihood high-impact climate events in a warming world requires massive ensembles of hindcasts and forecasts to capture their statistics. At present, it is extremely challenging to generate these ensembles using traditional weather or climate models, especially at sufficiently high spatial resolution.

We describe how to bring the power of machine learning (ML) to generate climate hindcasts at four to five orders-of-magnitude lower computational cost than conventional numerical methods. We show how to evaluate ML climate emulators using the same rigorous metrics developed for operational numerical weather prediction.

Furthermore, we illustrate the power of this approach by generating a huge ensemble (HENS)  initialized for each day of June through August 2023, the second-hottest summer in at least the last 2000 years.  We show how HENS can be used to quantify the intensity of atmospheric rivers in the Southern Hemisphere, the diffusion of tropical cyclones in the general circulation, and the severity of unprecedented heatwaves characteristic of last summer.  

We conclude with the prospects of extending machine-learning emulators to make skillful predictions of future climate change.

Speaker Bio

William Collins is an internationally recognized expert in climate modeling and climate change science. His personal research concerns the interactions among greenhouse gases and aerosols, the coupled climate system, and global environmental change.  

Dr. Collins is a Fellow of the American Association for the Advancement of Science (AAAS), the American Physical Society (APS), the American Geophysical Union (AGU), and the American Meteorological Society (AMS).  He was awarded the AGU’s Tyndall History of Global Environmental Change Lectureship in 2019 and their Jule Charney Lectureship in 2024.  He was a Lead Author on the Fourth Assessment of the Intergovernmental Panel on Climate Change (IPCC), for which the IPCC was awarded the 2007 Nobel Peace Prize, and has also served as Lead Author on the Fifth and Sixth Assessments.  His role as Chief Scientist in launching the Department of Energy’s Accelerated Climate Model for Energy (ACME) program was awarded the U.S. Department of Energy Secretary’s Achievement Award on May 7, 2015.  

Before joining Berkeley and Berkeley Lab, Dr. Collins was a senior scientist at the National Center for Atmospheric Research (NCAR) and served as Chair of the Scientific Steering Committee for the DOE/NSF Community Climate System Model project. 

Dr. Collins received his undergraduate degree in physics from Princeton University and earned an M.S. and Ph.D. in astronomy and astrophysics from the University of Chicago.

Abstract

AI is accelerating scientific modeling, simulations, and design. This includes diverse domains such as weather and climate modeling, energy transition solutions, design of medical devices etc. AI yields 4-5 orders of magnitude speedups over current numerical methods. We have developed a principled AI method called Neural Operators that learn mappings between function spaces. It enables zero-shot generalization beyond the training discretization or resolution that makes them ideal for capturing multi-scale processes.

Speaker Bio

Anima Anandkumar has done pioneering work on AI algorithms that have revolutionized scientific domains including weather forecasting, drug discovery, scientific simulations and engineering design. She invented Neural Operators that extend deep learning to modeling multi-scale processes in these scientific domains. Anima is a Bren professor at Caltech, a fellow of the IEEE, ACM and AAAI. 

She has received several awards including the Guggenheim and Alfred P. Sloan fellowships, the Schmidt Sciences AI2050 senior fellow, the NSF Career award, and best paper awards at venues such as Neural Information Processing and the ACM Gordon Bell Special Prize for HPC-Based COVID-19 Research. She recently presented her work on AI+Science to the White House Science Council. She received her B. Tech from the Indian Institute of Technology Madras and her Ph.D. from Cornell University and did her postdoctoral research at MIT. She was previously principal scientist at Amazon Web Services and senior director of AI research at NVIDIA.

Abstract

Over the past 50 years, countless events and transformations have shaped our journey. Join us for an exciting mix of fun facts, technological advancements, and stories of evolution at NERSC.

Have you ever wondered how NERSC's mission shifted from focusing on fusion to supporting all areas of science? What technological breakthroughs played a pivotal role in advancing research here? We’ll explore these questions and more, sharing a diverse array of insights and engaging anecdotes along the way.

Don’t miss this opportunity to delve into the fascinating history and future of NERSC!

Speaker Bio

Tina Declerck is the NERSC Deputy of Operations and  leads the NERSC Data Center Department that is composed of the Storage Systems, Security, Networking, Operations Technology, and Building Infrastructure groups. Her accomplishments include managing the configuration, installation, and acceptance of the NERSC-9 (Perlmutter) system, and project lead on NERSC-8 (Cori). Tina has experience with almost all of the systems installed at NERSC since 1996 as a system analyst since its move to Lawrence Berkeley National Lab from Livermore. During a short hiatus from NERSC (2001-2006) she focused on storage startups.  Prior to joining NERSC in 1997, she worked at The Liposome Company and was an officer in the USAF.

Speaker Bio

Dr. Jordan Thomas is the NERSC Program Manager at the Department of Energy, Office of Science. She joined the Department of Energy in 2020 as a AAAS Science and Technology Policy Fellow and then spent a two years as the ASCR Leadership Computing Challenge program manager. Prior to working at the Department of Energy, Dr. Thomas worked as a data scientist contractor for the Department of Defense and Department of Homeland Security.

Dr. Thomas obtained her B.S. from Penn State University in Meteorology, and her Ph.D. from Johns Hopkins University in Oceanography, where her dissertation focused on modeling the dynamical interactions between the atmosphere and ocean.

Abstract

Advanced data and computing systems are vital to LCLS operations, data interpretation, and overall scientific productivity.  SLAC scientists have long relied on computing resources such as NERSC for simulations and for running analysis codes.  The transition to MHz-era operation marks a fundamental change in scale that requires new infrastructure and architectures to link LCLS to the required scale of computing needed for scientific interpretation.  The LCLS-II Data System leverages access to NERSC compute to reduce time to science, improve the efficiency and quality of acquired data sets, and solve exascale problems that cannot be solved by other means.  Feature extracted information generated in the data analysis pipeline - at the edge, local compute, or remote computational resources - can be used to steer experiments and inform user decisions during beam time.  We will describe how the ExaFEL project lays the foundation to enable LCLS to rapidly analyze large datasets, direct experiments, and enhance experimental output.  We discuss the impact that NERSC has had helping LCLS face these challenges and explore the opportunities afforded by fully leveraging the remote computing resources of the DOE complex.

Speaker Bio

Dr. Jana Thayer is the Division Director for the Linac Coherent Light Source (LCLS) Data Systems at the SLAC National Accelerator Laboratory, responsible for data acquisition, data management, and analysis support for the LCLS facility and development of the next-generation data system to support the LCLS-II upgrade. She is the PI for the Intelligent Learning for Light Source and Neutron Source User Measurements, including Navigation and Experiment Steering (ILLUMINE) project, which facilitates rapid data analysis and autonomous experiment steering capabilities to support cutting-edge research tightly coupling high-throughput experiments, advanced computing architectures, and novel AI/ML algorithms to significantly reduce the time to science at the light and neutron sources.  Jana started at SLAC in 2004 working on the Fermi Gamma-Ray Space Telescope and led the Large Area Telescope Flight Software Team from 2006 - 2009. Jana has a Ph.D. in Elementary Particle Physics from The Ohio State University and has long nurtured an interest in data acquisition systems and high-performance software in the fields of HEP, astrophysics, and photon science. 

Abstract

Useful quantum computing of the future will inherently be hybrid with CPUs, GPUs, and QPUs working in tandem to solve the world’s most important problems. In this talk we’ll discuss how NVIDIA is enabling the entire quantum ecosystem to build and leverage heterogeneous quantum-classical systems to accelerate quantum computing today.

Speaker Bio

Jin-Sung Kim leads partnerships and alliances for quantum computing at NVIDIA and is based out of San Francisco. Prior to NVIDIA, he was a Research Staff Member at IBM Quantum. Jin-Sung holds a PhD in Electrical Engineering and Materials Science from Princeton University.

Abstract

Materials science is entering the era of the fourth paradigm of science: data-driven materials design. The Materials Project (www.materialsproject.org) uses supercomputing and a sophisticated software infrastructure together with state-of-the-art quantum mechanical theory to compute the properties of all known inorganic materials and beyond, design novel materials and offer the data for free to the community together with online analysis and design algorithms. The current release contains data derived from quantum mechanical calculations for over 150,000 materials and millions of properties. The resource supports a growing community of data-rich materials research, currently supporting over 500,000 registered users and millions of data records served each day through the API. Our resource is inspiring data-driven work across the community and in response, we are seeing a rapid increase in the development of machine learning algorithms for the prediction of materials properties, characteristics, and synthesizability.

Speaker Bio

Kristin Persson is the Daniel M. Tellep Distinguished Professor at the University of California, Berkeley and a Senior Faculty Scientist at Lawrence Berkeley National Laboratory.  She is also the Director and founder of the Materials Project (materialsproject.org) which is a world-leading resource for materials data and design. She has received the DOE Secretary of Energy’s Achievement Award twice, the TMS Cyril Stanley Smith Award, the TMS Faculty Early Career Award, the Falling Walls Science and Innovation Management Award, the LBNL Director’s award for Exceptional Scientific Achievement and she is a member of the Royal Swedish Academy of Science, an MRS Fellow, a AAAS Fellow and an APS Fellow. She holds several patents in the clean energy space and has co-authored more than 300 peer-reviewed publications.  

Abstract

Determining the value and return from basic research is a complicated endeavor. Concrete results may not be apparent until years after the research is completed. In this session you’ll hear perspectives from the industry analyst and scientific research communities regarding the value of scientific research at leadership computing sites in general, and specifically at NERSC.

Bios

Mark Nossokoff is a Research Director at Hyperion Research, a leading industry analyst and market research firm that focuses on the HPC-AI market. Mark is the lead analyst for cloud, storage, and interconnects, and also assists clients with their strategic planning and market assessment needs.

Abstract

I will discuss the long-running and central role that supercomputing has played in defining and testing the Standard Model in particle and nuclear physics through the numerical approach of lattice quantum chromodynamics (LQCD).

LQCD describes the interactions of the quarks and gluons that combine to for composite states such as the proton, and from early crude calculations of the particle masses, to precision test of the Standard Model through theory—experiment comparisons, LQCD has always been at the forefront of applications of HPC at NERSC and elsewhere.

In this talk I will discuss some history and recent results from the field, focusing on problems related to the emergence on complex systems such as nuclei and dense matter from the underlying quark and gluon degrees of freedom and provide an outlook for the future.

Speaker Bio

Detmold did his undergraduate and graduate studies at the University of Adelaide, Australia, and after spending time at the University of Washington and College of William and Mary has been a Professor of Physics at MIT since 2012.

His research interests are in strong interaction dynamics in theoretical particle and nuclear physics.

Detmold uses analytic methods and supercomputers to solve the complex equations of quantum chromodynamics (QCD) that describe the strong interaction and seeks to understand the emergence of hadrons and nuclei from the underlying Standard Model of particle physics.

He aims to determine the properties and interactions of these systems from first principles to confront experiment and to make predictions for regimes such as in the interior of neutron stars where experiments are not possible.

Abstract

From its inception under John Killeen's visionary leadership, the National Energy Research Scientific Computing Center (NERSC) has been instrumental in advancing fusion energy research through computational innovation. Killeen's pioneering work in kinetic theory and resistive magnetohydrodynamics laid the groundwork for some of the most significant scientific discoveries in fusion plasma physics.

In this talk, I will draw from my personal experience with major milestones in fusion research that have shaped our modern understanding. Emphasizing the interplay between modeling and experimentation over time—and the role of serendipity in computational discoveries such as the numerical identification of Toroidal Alfvén Eigenmodes (TAEs), zonal flows, and magnetic island properties—I will highlight how these findings have profoundly impacted the field. This integrative approach to computational research has been part of NERSC’s DNA from the outset, reflecting its distinctive culture and commitment to accelerating the coupling between theory and experiment.

Building upon this legacy, NERSC now stands at the forefront of deploying artificial intelligence, machine learning, and digital twin technologies in fusion research. Central to these advancements is the Department of Energy's Advanced Scientific Computing Research (DOE-ASCR) Integrated Research Infrastructure (IRI), which facilitates the creation of digital twins for fusion devices. By merging real-time experimental data with high-fidelity simulations, these initiatives provide unprecedented insights into plasma dynamics and reactor performance, significantly reducing the time scale of interaction between modeling and experimental optimization—from years in past examples to mere days.

By tracing the arc from Killeen's early contributions to today's cutting-edge research, this presentation will highlight the profound impacts NERSC has had on fusion science and demonstrate how the Integrated Research Infrastructure marks a significant leap forward in the pursuit of fusion energy.

Speaker Bio
Dr. Raffi Nazikian is the Senior Director for Fusion Data Science at General Atomics, where he leads initiatives to integrate advanced data analytics and computational methods into fusion energy research. With over three decades of experience in plasma physics, Dr. Nazikian has made significant contributions to the understanding of plasma turbulence, instabilities, and confinement in magnetic fusion devices. He is a Fellow of the American Physical Society and a two-time recipient of the Kaul Award for Excellence in Plasma Physics Research. Prior to joining General Atomics, Dr. Nazikian led tokamak research and international collaborations at the Princeton Plasma Physics Laboratory (PPPL). His leadership at PPPL advanced fusion science by fostering global partnerships that bridged theoretical models with experimental observations across fusion facilities.

Panelist Bios

Dr. Erik Draeger is the Director of the High Performance Computing Innovation Center and RADIUSS project at Lawrence Livermore National Laboratory as well as the Scientific Computing group leader at the Center for Applied Scientific Computing.  Erik earned a Bachelor's degree in Physics from the University of California, Berkeley in 1995 and received a PhD in theoretical condensed matter physics from the University of Illinois, Urbana-Champaign in 2001.  He has been a finalist for the Gordon Bell Prize six times since 2005 and won the prize in 2006.

Panelist Bios

Bronson Messer has been a NERSC user for more than 28 years. He is a Distinguished Staff Scientist and the Director of Science for the Oak Ridge Leadership Computing Facility (OLCF) at ORNL. He is also Joint Faculty Professor in the Department of Physics & Astronomy at the University of Tennessee. He recently served on the American Physical Society’s Committee on Informing the Public (2018-2020), and in 2020 he was awarded the Department of Energy Secretary’s Honor Award for his part in enabling the COVID-19 High Performance Computing Consortium. Dr. Messer holds undergraduate and graduate degrees from the University of Tennessee, earning his PhD in physics in 2000.