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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 - April 10, 2025
- Advancing Energy Reconstruction in Collider Experiments Using Machine Learning: Yongbin Feng, Texas Tech University - March 10, 2025
- Deep learning algorithms in GW astrophysics: explaining their success: Jess McIver, University of British Columbia - February 24, 2025
- Large-scale pretraining on neural data allows for transfer across individuals, tasks and species: Eva Dyer, Georgia Institute of Technology - December 9, 2024
- Hunting the Unexpected: Anomaly Detection and Real-Time Triggers at the Large Hadron Collider: Jennifer Ngadiuba, Fermilab - November 18, 2024
- Machine Learning for Ge-Based Neutrinoless Double-Beta Decay: Julieta Gruszko, UNC Chapel Hill - October 21, 2024
- Accelerating Discovery in Particle Physics with Anomaly Detection: Gregor Kasieczka, Universität Hamburg - June 10, 2024
- The Real AI Revolution in Astronomy Hasn't Happened Yet: Joshua Bloom, University of California, Berkeley - May 13, 2024
- Enabling big neuroscience through computational advances: Adam Charles, Johns Hopkins University - April 8, 2024
- Is machine learning good or bad for astrophysics? : David Hogg, New York University - March 11, 2024
- AI models of population dynamics precisely link state space trajectories with behavior in the mammalian spinal cord: Chethan Pandarinath, Emory University & Georgia Tech - February 12, 2024
- Fast in Slow: AI in Rare Event Search: Aobo Li, UCSD - December 11, 2023
- Polymathic AI: Foundation Models for Science: Miles Cranmer, University of Cambridge - November 13, 2023
- A3D3 High-Throughput AI Methods and Infrastructure Workshop - July 10, 2023 to July 14, 2023
- Next-Generation Event Filtering at LHC: Leveraging Real-Time ML for Handling Massive LHC Data Streams: Thea Aarrestad, ETH Zürich - June 12, 2023
- Real-time modeling with active interventions: Anne Draelos, University of Michigan - May 15, 2023
- Challenges and Opportunities for Optical Neural Network: Arka Majumdar, University of Washington - May 1, 2023
- Closing the Virtuous Cycle of AI for IC and IC for AI: David Z. Pan, University of Texas at Austin - November 7, 2022
- Distributed coding of vision, action, and cognition in the mouse brain: Nick Steinmetz, University of Washington & International Brain Lab - October 24, 2022
- Machine Learning in IceCube - September 12, 2022
- Towards Online Anomaly Detection for Particle Physics: Ben Nachman, LBNL - August 8, 2022
- Rapid and robust parameter estimation for gravitational wave observations: Jonathan Gair, Max Planck Institute for Gravitational Physics - July 11, 2022
- Machine Learning for Fundamental Physics Discovery with High Resolution Particle Imaging Detectors: Georgia Karagiorgi, Columbia University - June 6, 2022
- A Pursuit of Efficient and Accurate Binary Neural Networks: Zhiru Zhang, Cornell University - May 2, 2022
- Understand The Brain Using Interpretable Machine Learning Models: Anqi Wu, Georgia Institute of Technology - April 4, 2022
- Time-domain Astrophysics in the Era of Big Data : Ashley Villar, Pennsylvania State University - March 7, 2022
- A3D3: Accelerating Simulation-based Inference : Kyle Cranmer (New York University) - February 7, 2022