Machine Learning for the Physical Sciences (ML4PS) 2021


Machine learning methods have had great success in learning complex representations of data that enable novel modeling and data processing approaches in many scientific disciplines. Physical sciences span problems and challenges at all scales in the universe: from finding exoplanets in trillions of sky pixels, to developing solutions to the quantum many-body problem and combinatorial problems, to detecting anomalies in event streams from the Large Hadron Collider, to predicting how extreme weather events will vary with climate change. Tackling a number of associated data-intensive tasks including, but not limited to, segmentation, computer vision, sequence modeling, causal reasoning, generative modeling, and probabilistic inference are critical for furthering scientific discovery in these and many other areas. In addition to using machine learning models for scientific discovery, the ability to interpret what a model has learned is receiving an increasing amount of attention.

Dec 13, 2021 3:06 PM — Dec 16, 2021 3:06 PM
Online, ZOOM - Organized during Neurips 2021
Enrico Rinaldi
Enrico Rinaldi
Research Scientist

My research interests include artificial intelligence and quantum computing applied to particle physics and quantum many-body systems.