A-Z Index | Directory | Careers

Postdocs Present Wide-Ranging Research at 2022 Symposium

February 15, 2022

Contact: cscomms@lbl.gov

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.

2022 computing sciences postdoc symposium talks

Particle-in-Cell Simulations of Relativistic Magnetic Reconnection

Hannah Klion

Center for Computational Sciences and Engineering (CCSE)

Applied Mathematics and Computational Research Division (AMCRD)

Faster Algorithms for Tensor Ring Decomposition

Osman Asif Malik

Scalable Solvers Group
Applied Mathematics and Computational Research Division (AMCRD)

Exascale Modeling of Electromagnetics with Applications to Microelectronics and Particle Accelerators

Prabhat Kumar

Center for Computational Sciences & Engineering

Applied Mathematics and Computational Research Division (AMCRD)

HYPPO: A surrogate-based, UQ-informed, and multi-level parallelism HPO tool

Vince Dumont

Center for Computational Sciences & Engineering

Applied Mathematics and Computational Research Division (AMCRD)

 

Adaptive Variational Approach for Quantum Simulations

Niladri Gomes

Applied Computing for Scientific Discovery Group

Applied Mathematics and Computational Research Division (AMCRD)

 

GPTune: Advanced problems in surrogate-based black-box optimization with applications

Hengrui Luo

Applied Computing for Scientific Discovery Group

Applied Mathematics and Computational Research Division (AMCRD)

3D CNNS Utilizing Molecular Topological Features for Accurate Atomization Energy Predictions

Ankur Kumar Gupta

Applied Computing for Scientific Discovery Group

Applied Mathematics and Computational Research Division (AMCRD)

FunFact: A Tensor algebra language with applications in deep learning

Daan Camps

Scalable Solvers Group

Applied Mathematics and Computational Research Division (AMCRD)

Enabling Secure Learning for Clean Energy Systems

Yize Chen

Integrated Data Frameworks Group
Scientific Data Division (SciData)

Mosaic Flow: A transferable deep learning framework for solving PDEs on unseen domains

Hengjie Wang

Center for Computational Research & Engineering

Applied Mathematics and Computational Research Division (AMCRD)


Optomechanical Quantum Transduction Control Protocol

Huo Chen

Applied Computing for Scientific Discovery
Applied Mathematics and Computational Research Division (AMCRD)


Towards Understanding I/O Behavior with Interactive Exploration

Jean Luca Bez

Scientific Data Management Group

Scientific Data Division (SciData)


Performance Portability of Linear Algebra Routines in GW Calculations

Soham Ghosh

National 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 large-scale 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.