The BIDS Machine Learning and Science Forum (formerly the Berkeley Statistics and Machine Learning Forum) was launched in Spring 2018 and currently meets biweekly (during the spring and fall semesters) to discuss current applications across a wide variety of research domains in the physical sciences and beyond. These active sessions bring together domain scientists, statisticians, and computer scientists who are either developing state-of-the-art methods or are interested in applying these methods in their research. This Forum is organized by BIDS Faculty Affiliates Uroš Seljak (professor of Physics at UC Berkeley) and BIDS Research Affiliate Ben Nachman (Physicist at Lawrence Berkeley National Laboratory), Vanessa Böhm and Ben Erichson. All interested members of the UC Berkeley and Berkeley Lab communities are welcome and encouraged to attend. To receive email notifications about upcoming meetings, or to request more information, please contact berkeleymlforum@gmail.com.
Upcoming Sessions
Spring 2021
Machine Learning and Science Forum
Date: Biweekly on Mondays, January 25 through June 28, 2021
Time: 11:00 AM - 12:00 PM Pacific Time
Location: Participate remotely using this Zoom link
- January 25: AI at the exascale, Debbie Bard, NERSC
- February 8: TBD
- February 22: TBD
- March 8: TBD
- March 22: TBD
- April 5: TBD
- April 19: TBD
- May 3: TBD
- May 17: TBD
- May 31: No meeting, Memorial Day Holiday
- June 14: TBD
- June 28: TBD
Previous Sessions
2020
- Dec 7: Mutual Information Estimation for Tensor Network Machine Learning, Ian Convy, UC Berkeley
- Nov 23: Set and Sequence Machine Learning for Particle Identification at the Large Hadron Collider, Nicole Hartman, SLAC
- Nov 9: First-class machine learning model for Science: Graph Neural Network, Xiangyang Ju, Lawrence Berkeley National Laboratory
- Oct 26: The Large Learning Rate Phase of Deep Learning, Yasaman Bahri, Research Scientist, Google Brain
- Oct 12: Machine-Learned Epidemiology, Adam Sadilek, Google Research
- Sept 28: FlowPM: Distributed TensorFlow Implementation of Cosmological N-body Solver, Chirag Modi, Berkeley Center for Cosmological Physics
- Sept 14: Comprehension is compression: understanding neural networks through pruning and the lottery ticket hypothesis, Michela Paganini, Facebook AI Research (FAIR)
- June 1: Identifying topological order through unsupervised machine learning, Joaquin Rodriguez Nieva, Physics, UC Berkeley
- May 11: Over-fitting in Modern Supervised Learning: Memorization, Interpolation, and Decomposition of Errors, Jason Rocks, Physics, Boston University
- May 4: Representation Learning for Particle Collider Events, Jack Collins, Physics, SLAC
- April 20: Robust Mean Estimation in Nearly-Linear Time, with Applications to Outlier Removal, Sam Hopkins, EECS, UC Berkeley
- April 6: Estimating Gradients of Distributions for Generative Modeling, Yang Song, Computer Science, Stanford
- March 23: Deep generative models for scientific applications, Vanessa Böhm, BCCP
- March 9: BoltGAN Machines and Applications in Medicine, Charles K. Fisher, Unlearn.AI
- Feb 24: Deep learning for PDEs, and scientific computing with JAX, Stephan Hoyer, Google
- Feb 10: Probabilistic programming at scale for science, Wahid Bhimji, NERSC
- Jan 27: Reinforcement Learning to Control Quantum Systems, Marin Bukov, Physics, UC Berkeley
2019
- December 16, 2019: A family of algorithms for interpreting manifold embedding coordinates in molecular dynamics data (Samson Koelle)
- December 2, 2019: Probing Dark Matter with Strong Gravitational Lensing (Simon Birrer)
- November 18, 2019: The role of machine learning in building an earthquake disaster platform (Qingkai Kong)
- November 4, 2019: Anomaly Detection Meets Deep Learning
- October 21: 2019: Uncertainties with Neural Networks: Methods and Applications
- October 7, 2019: Neural Ordinary Differential Equations
- September 23, 2019: Machine learning for approximating sub-grid physics in electromagnetic geophysics (Lindsey Heagy)
- September 9, 2019: The Recurrent Inference Machine: applications to Astronomical and Medical imaging
- April 15, 2019: Interpretable machine learning - what does it actually mean? (Jamie Murdoch)
- April 1, 2019: Learning on unstructured spherical grids (Max Jiang)
- March 18, 2019: Power of gradients and accept-reject step in MCMC algorithms (Raaz Dwivedi)
- March 4, 2019: JUNIPR: a framework for unsupervised and interpretable machine learning in particle physics (Anders Andreassen)
- February 4, 2019: Efficient coding and language evolution: the case of color naming (Noga Zaslavskya)
2018
- December 10, 2018: Sampling vs Optimization (Yian Ma)
- November 26, 2018: Deep Learning with symmetries (Tess Smidt)
- October 15, 2018: Latest development in Recurrent Neural Networks (Ravi Krishna)
- October 8, 2018: Fundamentals of graph theory and application to population migrations (Wooseok Ha)
- October 1, 2018: Deep Learning in Bio-imaging (Henry Pinkard)
- September 24, 2018: Applications of ML for High Energy Physics (Ben Nachman)
