NERSC HPC Achievement Awards
The NERSC HPC Achievement Awards are presented annually to recognize extraordinary contributions from early-career scientists who used NERSC in their research. NERSC users, project Principal Investigators, project managers, PI proxies, and DOE Program Managers can nominate any early-career NERSC user whose research was significantly based on work performed using NERSC's facilities and services.
Recipients are listed on the NERSC web site and highlighted in NERSC press releases. Selections are made by members of the NERSC Users' Group Executive Committee and NERSC staff. The cited work and achievements must have made substantial use of NERSC resources, which could be any combination of computational systems, storage systems, edge services, and/or NERSC HPC services.
Early career researchers who used NERSC resources during allocation years 2021 or 2022 are eligible. For the purposes of this award, an early career researcher is defined as a NERSC user who, at the time of the cited accomplishments, was a student or had received their degree after January 1, 2016. We accept nominations in two categories:
- NERSC Early Career Award for Innovative Use of High Performance Computing:
This award honors innovative use of NERSC's HPC resources. Examples include introducing HPC to a new science domain or a novel use of HPC resources. Anything that puts a fresh perspective on HPC or presents a new way to solve a problem is considered.
- NERSC Early Career Award for High Impact Scientific Achievement:
This award recognizes work that has had, or is expected to have, an exceptional impact on scientific understanding, engineering design for scientific facilities, and/or a broad societal impact.
(Awards through 2017 were presented in both Early Career and Open categories.)
High Impact Science Achievement
- 2020: Samuel Kachuck (University of Michigan) for "improving ice sheet and Earth system models to enable accurate projections of future sea level rise."
- 2020: Abigail Polin (Caltech and Carnegie Observatories) for "providing new insight into the origin and nature of Type 1a supernova."
- 2020: David Vartanyan (UC Berkeley) for "contributions to unraveling the characteristics and mechanisms of core-collapse supernova explosions."
- 2018-19 Early Career: Haoming Liang (West Virginia University) for "developing new insights, based on novel entropy-based diagnostics, into plasma simulations."
- 2018-19 Early Career: Gareth Roberg-Clark (University of Maryland) for "advancing our understanding of the fundamental physics of thermal conduction and applying insight into the intracluster medium of galaxy clusters"
- 2018-19 Early Career: Xie Zhang (UC Santa Barbara) for "producing essential insights in recombination mechanisms in hybrid perovskites-based on cutting-edge first-principles simulations."
- 2017: Qimin Yan, Jie Yu, Santosh K. Suram, Lan Zhou, Aniketa Shinde, Paul F. Newhouse, Wei Chen, Guo Li, Kristin A. Persson, John M. Gregoire, and Jeffrey B. Neaton (UC Berkeley, Lawrence Berkeley National Laboratory, California Institute of Technology) "for using NERSC resources to help speed the discovery of commercially viable catalysts that can be used to produce solar fuels."
- 2017 Early Career: Badri Narayanan (Argonne National Laboratory) for "developing atomistic models to understand reactive interfaces of energy applications."
- 2016: Charles Koven and William Riley (Berkeley Lab’s Climate and Ecosystem Sciences Division) and David Lawrence (National Center for Atmospheric Research) for "using an Earth system model to demonstrate the atmospheric effect of emissions released from carbon sequestered in melting permafrost soil."
- 2016 Early Career: Nathan Howard, MIT Plasma Science and Fusion Center, for "pioneering computational work in plasma turbulence simulations."
- 2015: Berkeley Lab’s BELLA (Berkeley Lab Laser Accelerator) team for "its work using NERSC resources to design and configure the world's most powerful compact particle accelerator"
- 2015 Early Career: Ken Chen, Postdoctoral Researcher at UC Santa Cruz, for his "study of the explosion of very massive stars in multiple dimensions."
- 2014: The Planck Collaboration for "the most detailed map ever made of the Cosmic Microwave Background – the remnant radiation from the Big Bang that refined some of the fundamental parameters of cosmology and physics."
- 2014 Early Career: Victor Ovchinnikov, Harvard University for "outstanding contributions to the field of computational modeling of conformational transitions in large biological molecules."
- 2013: Jeff Grossman and David Cohen-Tanugi (Massachusetts Institute of Technology) for "developing a new approach for desalinating seawater using sheets of graphene, a one-atom-thick form of the element carbon."
- 2013 Early Career: Tanmoy Das, Postdoctoral Researchers at Los Alamos National Laboratory for "computational work to understand fundamental materials aspects in three different areas."
Innovative Use of HPC
- 2020: Miha Muskinja (Berkeley Lab) for "developing a new software infrastructure that allows researchers connected to the ATLAS particle physics experiment at CERN to use HPC systems efficiently and effectively."
- 2020: Sierra Villarreal (Argonne National Laboratory) for "developing innovative workflows to enable using HPC at scale in support of the LSST Dark Energy Science Collaboration."
- 2018-19: The two categories of awards were combined for 2018-19. See High Impact Awards above.
- 2017: Abhinav Bhatele, Jae-Seung Yeom, Nikhil Jain, Chris J. Kuhlman, Yarden Livnat, Keith R. Bisset, Laxmikant V. Kale, and Madhav V. Marathe (Virginia Tech University, Lawrence Livermore National Laboratory, University of Utah, University of Illinois at Urbana-Champaign) for "using NERSC resources to demonstrate unprecedented scaling for simulating infectious diseases over realistic national-scale social networks."
- 2017 Early Career: Thomas Heller (Friedrich Alexander University Nuremberg) for "demonstrating that an asynchronous, massively parallel tasking runtime system (HPX) can be used to harness billions of tasks for a scalable hydrodynamics simulation of the merger of two stars."
- 2016: Scott French (UC Berkeley) for "creating a unique 3D scan of the Earth’s interior that resolved some long-standing questions about mantle plumes and volcanic hotspots using one of the first production codes to use UPC++: a new partitioned global address space programming system developed by researchers in the DEGAS group at Berkeley Lab"
- 2016 Early Career: Min Si (University of Tokyo & Argonne National Laboratory) for "developing novel system software in the context of MPI-3 one-sided communication."
- 2015: SPOT Suite Team (Berkeley Lab) for "transforming the way scientists run their experiments and analyze data collected from DOE light sources."
- 2015 Early Career: Taylor Barnes (California Institute of Technology) for "outstanding methodological advances that enhance our ability to harness large-scale computational resources to solve important chemical problems."
- 2014: Jean-Luc Vay (Berkeley Lab’s Accelerator and Fusion Research Division) for "developing innovative algorithms that greatly improved the use of high performance computing to advance the simulation of charged particles, beams and plasmas."
- 2014 Early Career: Anubhav Jain (Berkeley Lab) for "creating innovative HPC workflow tools that enable scientific discovery in materials research."
- 2013: Peter Nugent (Berkeley Lab) and the Palomar Transient Factory Team (Caltech) for "detection of transient events that leads to a greater understanding of astrophysical objects like supernovae, active galaxies and gamma-ray bursts, among a variety of other known and unknown cosmic phenomena"
- 2013 Early Career: Edgar Solomonik (University of California, Berkeley) for "developing novel algorithms for massively parallel tensor contractions and applying them to quantum chemistry problems, specifically coupled-cluster theory, which is the de facto standard for important scientific applications in the thermochemistry of combustion and excited-states of systems where density-functional theory (DFT) breaks down."