Accelerated AI Algorithms for Data-Driven Discovery Institute (A3D3)
Accelerated AI Algorithms for Data-Driven Discovery Institute (A3D3)
Past Events
- Accelerating Scientific Discovery in the Brain in the Age of AI (Lu Mi, Allen Institute for Brain Science & University of Washington) -
- Advancing Energy Reconstruction in Collider Experiments Using Machine Learning (Yongbin Feng, Texas Tech University) -
- Deep learning algorithms in GW astrophysics: explaining their success (Jess McIver, University of British Columbia) -
- Large-scale pretraining on neural data allows for transfer across individuals, tasks and species (Eva Dyer, Georgia Institute of Technology) -
- Hunting the Unexpected: Anomaly Detection and Real-Time Triggers at the Large Hadron Collider (Jennifer Ngadiuba, Fermilab) -
- Machine Learning for Ge-Based Neutrinoless Double-Beta Decay (Julieta Gruszko, UNC Chapel Hill ) -
- Accelerating Discovery in Particle Physics with Anomaly Detection (Gregor Kasieczka, Universität Hamburg) -
- The Real AI Revolution in Astronomy Hasn't Happened Yet (Joshua Bloom, University of California, Berkeley) -
- Enabling big neuroscience through computational advances (Adam Charles, Johns Hopkins University) -
- Is machine learning good or bad for astrophysics? (David Hogg, New York University) -
- AI models of population dynamics precisely link state space trajectories with behavior in the mammalian spinal cord (Chethan Pandarinath, Emory University & Georgia Tech) -
- Fast in Slow: AI in Rare Event Search (Aobo Li, UCSD) -
- Polymathic AI: Foundation Models for Science (Miles Cranmer, University of Cambridge) -
- A3D3 High-Throughput AI Methods and Infrastructure Workshop - , , , ,
- Next-Generation Event Filtering at LHC: Leveraging Real-Time ML for Handling Massive LHC Data Streams (Thea Aarrestad, ETH Zürich) -
- Real-time modeling with active interventions (Anne Draelos, University of Michigan) -
- Challenges and Opportunities for Optical Neural Network (Arka Majumdar, University of Washington) -
- Closing the Virtuous Cycle of AI for IC and IC for AI (David Z. Pan, University of Texas at Austin) -
- Distributed coding of vision, action, and cognition in the mouse brain (Nick Steinmetz, University of Washington & International Brain Lab) -
- Machine Learning in IceCube -
- Towards Online Anomaly Detection for Particle Physics (Ben Nachman, LBNL) -
- Rapid and robust parameter estimation for gravitational wave observations (Jonathan Gair, Max Planck Institute for Gravitational Physics) -
- Machine Learning for Fundamental Physics Discovery with High Resolution Particle Imaging Detectors (Georgia Karagiorgi, Columbia University) -
- A Pursuit of Efficient and Accurate Binary Neural Networks (Zhiru Zhang, Cornell University) -
- Understand The Brain Using Interpretable Machine Learning Models (Anqi Wu, Georgia Institute of Technology) -
- Time-domain Astrophysics in the Era of Big Data (Ashley Villar, Pennsylvania State University) -
- A3D3: Accelerating Simulation-based Inference (Kyle Cranmer (New York University)) -