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Special Symposium to Showcase Postdocs' Research

January 30-31 Event Features Wide Range of Emerging Research

January 14, 2020


Twenty-two postdoctoral fellows in the Computing Sciences Area have been chosen to present their research during a two-day symposium that is the culmination of a targeted communications training program designed specifically for postdocs. The symposium, open to the wider Berkeley Lab community, takes place January 30-31 in Wang Hall (Building 59), room 3101.

Off-site visitors are welcome but must register by end of day Friday, January 24, 2020 to ensure site access. No registration is necessary for lab staff or remote attendees. (Please contact Dionne Myers with questions.)

The program will showcase ongoing research projects in exascale computing; machine learning; data management and analysis for experimental science; modeling and simulation of complex scientific problems; computer networking; and quantum computing. 

20 CR CS Area Postdoc Symposium Poster

Select image to enlarge.

 

“Developing excellent communications and presentation skills is one of the most important things that a young scientist can do,” said David Brown, director of the Computational Research Division at Berkeley Lab. We created this educational program to provide our postdocs with communications training, mentorship, and coaching sessions along with an opportunity to present their research in a public forum.” 

Speakers and Talks

Thursday, January 30, 2020


9:00 a.m., Hugo Brunie – Mixed Precision Tuning on HPC Applications

9:30 a.m., Michael Rowan – Use of CUDA Profiling Tools Interface (CUPTI) for Profiling Asynchronous GPU Activity

10:00 a.m., Muaaz Awan – GPU-BSW: A GPU Based Sequence Alignment Algorithm for Accelerating Bioinformatics Applications

10:30 a.m., Nan Ding – An Instruction Roofline Model for GPUs

11:00 a.m., Wenjing Wang –Bilevel Optimization and Data Analysis for Efficient Tuning of High Energy Physics Event Generators

11:30 a.m., Jangho Park – Input Structure Selection for Time-Series Prediction with Machine Learning

1:00 p.m., Reetik Sahu – Predicting Daily Groundwater Levels with Deep Learning Models

1:30 p.m. Venkitesh Ayyar – Building Compact Convolutional Neural Networks for Signal-Background Classification in Particle Physics Experiments

2:00 p.m., Yu-Hang Tang – GraphDot: A GPU-Accelerated Python Package for High-Throughput Graph Kernel Computation

2:30 p.m., Daniel Murnane – Graph Neural Networks for Particle Tracking

3:00 p.m., Bashir Mohammed – DeepRoute: A Deep Reinforcement Learning approach for Dynamic Network Routing Optimization and SDN on Chameleon Testbed

Friday, January 31, 2020


9:00 a.m., Mike MacNeil – Distributed Digital Volume Correlation by Optimal Transport

9:30 a.m., Zhe Bai – Computed Tomography (CT) Image Registration and Segmentation for Traumatic Brain Injury (TBI) Analysis

10:00 a.m., Jie Luo – An Alternative Architecture for Computing with Exponential Acceleration

10:30 a.m., Adam Peterson – Numerical Construction of Vortices in a Strongly Coupled Superconductor

11:00 a.m., David Williams-Young – Parallel Shift-Invert Spectrum Slicing for Symmetric Self-Consistent Eigenvalue Computation

11:00 a.m., Don Willcox – Towards Exascale Supernovae Simulations

1:00 p.m., Doreen Fan – Modeling Type Ia Supernovae: The Key to Understanding the Cosmic Universe

1:30 p.m., Katie Klymko – A Low Mach Number Fluctuating Hydrodynamics Model for Room Temperature Ionic Liquids

2:00 p.m., Revathi Jambunathan – 2:00 pm Towards Exascale Modeling of Pulsar Magnetospheres Using WarpX

2:30 p.m., Roberto Porcu – A Hybrid PIC-DEM Approach for Multi-Phase Computational Fluid Dynamics

3:00 p.m., Vincenzo Gulizzi – High-order Numerical Schemes for the Exascale


About Computing Sciences at Berkeley Lab

The Computing Sciences Area at Lawrence Berkeley National Laboratory provides the computing and networking resources and expertise critical to advancing Department of Energy Office of Science research missions: developing new energy sources, improving energy efficiency, developing new materials, and increasing our understanding of ourselves, our world, and our universe.

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.