Berkeley Lab’s Computing Sciences Area performs exciting research in artificial intelligence and machine learning, exascale computing, quantum computing, mathematical advances to fuel science, and much more. Twelve of our postdoctoral research fellows presented their work in various disciplines at Berkeley Lab’s Computing Sciences Area 2022 Postdoc Symposium on February 8, 9, and 10.
The three days of presentations are part of a more extensive program aimed at developing postdoctoral scholars’ skills and experience. The symposium includes communications training, mentorship, and coaching sessions.
Watch all the symposium speakers on our Youtube channel, or select individual videos below.
ParticleinCell Simulations of Relativistic Magnetic ReconnectionHannah KlionCenter for Computational Sciences and Engineering (CCSE)Applied Mathematics and Computational Research Division (AMCRD) 
Faster Algorithms for Tensor Ring DecompositionOsman Asif MalikScalable Solvers Group

Exascale Modeling of Electromagnetics with Applications to Microelectronics and Particle AcceleratorsPrabhat KumarCenter for Computational Sciences & EngineeringApplied Mathematics and Computational Research Division (AMCRD) 
HYPPO: A surrogatebased, UQinformed, and multilevel parallelism HPO toolVince DumontCenter for Computational Sciences & Engineering
Applied Mathematics and Computational Research Division (AMCRD) 
Adaptive Variational Approach for Quantum SimulationsNiladri GomesApplied Computing for Scientific Discovery Group
Applied Mathematics and Computational Research Division (AMCRD) 
GPTune: Advanced problems in surrogatebased blackbox optimization with applicationsHengrui LuoApplied Computing for Scientific Discovery GroupApplied Mathematics and Computational Research Division (AMCRD) 
3D CNNS Utilizing Molecular Topological Features for Accurate Atomization Energy PredictionsAnkur Kumar GuptaApplied Computing for Scientific Discovery GroupApplied Mathematics and Computational Research Division (AMCRD) 
FunFact: A Tensor algebra language with applications in deep learningDaan CampsScalable Solvers Group
Applied Mathematics and Computational Research Division (AMCRD) 
Enabling Secure Learning for Clean Energy SystemsYize ChenIntegrated Data Frameworks Group

Mosaic Flow: A transferable deep learning framework for solving PDEs on unseen domainsHengjie WangCenter for Computational Research & EngineeringApplied Mathematics and Computational Research Division (AMCRD) 
Optomechanical Quantum Transduction Control ProtocolHuo ChenApplied Computing for Scientific Discovery

Towards Understanding I/O Behavior with Interactive ExplorationJean Luca BezScientific Data Management GroupScientific Data Division (SciData) 
Performance Portability of Linear Algebra Routines in GW CalculationsSoham GhoshNational Energy Research Scientific Computing Center (NERSC) 
About Computing Sciences at Berkeley Lab
High performance computing plays a critical role in scientific discovery. Researchers increasingly rely on advances in computer science, mathematics, computational science, data science, and largescale computing and networking to increase our understanding of ourselves, our planet, and our universe. Berkeley Lab’s Computing Sciences Area researches, develops, and deploys new foundations, tools, and technologies to meet these needs and to advance research across a broad range of scientific disciplines.