NERSC Data Seminars Series - Lawrence Berkeley National Laboratory
The NERSC Data Seminar Series are held at Berkeley Lab. The series hosts speakers to:
- Learn about latest science and methods results from researchers
- Learn from software vendors on their product offerings
- Facilitate communications between NERSC and other lab CS staff
Time:
Talks are held at 11am-12pm on Tuesdays and are posted on the CS Seminars Calendar.
If you are affiliated with Berkeley Lab you can sign up to receive announcements about the Machine Learning seminars at the ML4Sci mailing-list.
Remote attendance:
Contacting the speakers:
Feel free to contact the host with questions or requests for time with the speaker.
Videos:
Video recordings are available on YouTube.
Past years:
2020 Seminars
| Date | Title | Speaker | Host | Material |
|---|---|---|---|---|
| 1/10 | Independent metadata updating for large scale parallel I/O systems (abstract) | Tonglin Li (NERSC) | Prabhat | pdf, vid |
| 1/31 | Data skeletons: IO workload characterization for the modern age (abstract) | Avani Wildani (Emory University) | Taylor Groves | vid |
| 2/07 | Time-series Analysis of ESnet Network Traffic: Statistical and Deep Learning models (abstract) | Mariam Kiran (ESNet) | Steven Farrell | vid |
| 2/14 | Intrinsic computation and physics-based machine learning for emergentself-organization in far-from-equilibrium systems (abstract) | Adam Rupe (UC Davis) | Karthik Kashinath | vid |
| 2/28 | The Superfacility project: 2019 year in review (abstract) | The Superfacility Project Team | Debbie Bard | vid |
| 3/06 | Intersections of AI/ML and Chemistry in Catalyst Design and Discovery (abstract) | Zachary Ulisii (CMU) | Mustafa Mustafa | pdf, vid |
| 3/13 | ECP HDF5 - New features and applications (abstract) | Suren Byna (CRD), Quincey Koziol (NERSC) | Quincey Koziol | pptx, vid |
| 4/17 | A Data-Driven Global Weather Model Using Reservoir Computing (abstract) | Troy Arcomano (Texas A&M) | Jaideep Pathak | vid |
| 5/01 | Deep learning for PDEs, and scientific computing with JAX (abstract) | Stephan Hoyer (Google) | Karthik Kashinath | vid coming soon |
| 5/15 | Deep learning production capabilities at NERSC (abstract) | Steven Farrell & Mustafa Mustafa | Prabhat | slides, vid |
| 5/22 | Learned discretizations for passive scalar advection in a 2-D turbulent flow (abstract) | Jiawei Zhuang (Harvard Univ.) | Mustafa Mustafa | slides |
| 5/29 | Simulation-based and label-free deep learning for science (abstract) | Ben Nachmann (LBL) | Wahid Bhimji | slides, vid |
| 6/05 | Tuning Floating Point Precision (Using Dynamic and Temporal Locality Program Information) (abstract) | Costin Iancu (CRD, LBL) | Brandon Cook | coming soon |
| 6/19 | Status of Containers in HPC (abstract) | Shane Canon | Prabhat | vid |
| 6/26 | Congestion and Distributed Training of Deep Neural Networks (abstract) | Jacob Balma (HPE) | Steven Farrell | slides |
| 7/02 | Workflows at NERSC: Overview and GNU Parallel Parsl, Papermill demos (abstract) | Bill Arndt, Laurie Stephey, Bjoern Enders | Prabhat | |
| 7/17 | Superconducting Radio-Frequency Cavity Fault Classification Using Machine Learning (abstract) | Christopher Tennant (Jefferson Lab) | Mustafa Mustafa | vid |
| 7/24 | Making the invisible visible (abstract) | Srigokul Upadhyayula | Suren Byna | vid |
| 8/21 | Weights & Biases: system of record to track, optimize, and reproduce ML research (abstract) | Chris Van Pelt, Charles Frye (Weights & Biases) | Mustafa Mustafa | |
| 9/04 | Steps toward holistic control of particle accelerators with neural networks (abstract) | Auralee Edelen (SLAC National Accelerator Laboratory) | Debbie Bard | vid |
| 9/11 | Flux: Overcoming Scheduling Challenges for Exascale Workflows (abstract) | Dong Ahn, Stephen Herbein (LLNL) | Katie Antypas | |
| 9/25 | ExaHDF5: An Update on the ECP HDF5 Project (abstract) | Quincey Koziol (NERSC), Suren Byna (CRD) | Wahid Bhimji | slides, vid |
| 10/02 | Enabling Interactive, On-Demand High Performance Computing for Rapid Prototyping and Machine Learning (abstract) | Albert Reuther (MITLincoln Laboratory Supercomputing Center) | Rollin Thomas | vid, slides |
| 10/09 | Generative neural networks: Data-driven simulations for particle physics (abstract) | Ramon Winterhalder (Heidelberg University) | Wahid Bhimji | |
| 10/16 | Using Machine Learning to Augment Coarse-Grid Computational Fluid Dynamics Simulations (abstract) | Jaideep Pathak (NERSC) | Mustafa Mustafa | vid |
| 11/02 | Robust prediction of high-dimensional dynamical systems using Koopman deep networks (abstract) | Omri Azencot (Ben-Gurion University) | John Wu |