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Computing Sciences Area 2021 Postdoc Symposium

February 17, 2021

Twenty postdoctoral research fellows presented their work in exascale computing, computational science, machine learning, quantum computing, data management and analysis, and much more at Berkeley Lab's Computing Sciences Area 2021 Postdoc Symposium on February 11 and 12.

CS Postdoc Symposium speaker poster

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The symposium is designed to provide area postdocs with communications training, mentorship, and coaching sessions, plus an opportunity to present their research in a public forum. Following a workshop and dry run, participants gave 30-minute presentations, including 10 minutes for questions, about their research to the wider Lab community.

»Watch the Presentations on Youtube.

Thursday, Feb. 11
9:00 a.m.

Venkitesh Ayyar


Mapping the Universe Using Generative Adversarial Neural Networks

9:30 a.m. Alice Gatti

Deep Reinforcement Learning for Graph Partitioning

10:00 a.m. Aditi Krishnapriyan

Learning Continuous Models for Continuous Physics
(Video to be released on publication of research.)

10:30 a.m. Doru Thom Popovici

Improving Data Locality Across Fourier Transforms and Linear Algebra Operations

11:00 a.m. Qiao Kang

Improving All-to-Many Personalized Communication in Two-Phase I/O

1:00 p.m. Prashant Pandey 

Metagenomic Reads Classification Using Graph Neural Networks

1:30 p.m. Daan Camps

Approximate Quantum Circuit Synthesis Using Block Encodings

2:00 p.m. Alexis Morvan

Quantum Imaginary Time Evolution on the Advanced Quantum Testbed

2:30 p.m. Miro Urbanek

Mitigating Noise on Quantum Computers

3:00 p.m. Oluwamayowa Amusat

Data-Driven Models for Equation-Oriented Optimization

 

Friday, Feb. 12
9:00 a.m.

Zhi (Jackie) Yao


Exascale-Enabled Physical Modeling for Next-Generation Microelectronics

9:30 a.m. Oisin Creaner

Light Simulations for Dark Matter

10:00 a.m. Neil Mehta

Determining the Best Molecular Dynamics Potential for the Job

10:30 a.m. Jialun (Galen) Wang

Modeling Non-equilibrium Phase Transition in Complex Fluids

11:00 a.m. Oscar Antepara

Accurate Numerical Algorithm for Scientific Applications with Complex Geometries
1:00 p.m. Ishan Srivastava

A New Computational Approach for Modeling Nanoscale Electrokinetic Flows

1:30 p.m. Don Willcox

Scalable Computational Modeling of Neutrino Quantum Kinetics in Astrophysics

2:00 p.m. Anne Felden

An AMR Subglacial Hydrology Model – SUHMO

2:30 p.m. Jordi Wolfson-Pou

Comparing the PFASST, MGRIT and Parareal Methods

3:00 p.m. Lisa Claus

 High-Performance Multifrontal Solver with Low-Rank Compression


About Computing Sciences at Berkeley Lab

High performance computing plays a critical role in scientific discovery, and 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.

Founded in 1931 on the belief that the biggest scientific challenges are best addressed by teams, Lawrence Berkeley National Laboratory and its scientists have been recognized with 13 Nobel Prizes. Today, Berkeley Lab researchers develop sustainable energy and environmental solutions, create useful new materials, advance the frontiers of computing, and probe the mysteries of life, matter, and the universe. Scientists from around the world rely on the Lab’s facilities for their own discovery science. Berkeley Lab is a multiprogram national laboratory, managed by the University of California for the U.S. Department of Energy’s Office of Science.

DOE’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.