Big Learning Workshop: Algorithms, Systems, and Tools for Learning at Scale at NIPS 2011
Invited Talk: Machine Learning's Role in the Search for Fundamental Particles by Daniel Whiteson
Daniel Whiteson is an Associate Professor in the Department of Physics & Astronomy at UC Irvine. His research area is experimental particle physics, using data from the world's most powerful colliders to answer questions about the fundamental nature of matter and interactions at the smallest scales. He has a long-standing interest in machine learning and has collaborated with machine learning researchers to apply new ideas to the problems of particle physics.
Abstract: High-energy physicists try to decompose matter into its most fundamental pieces by colliding particles at extreme energies. But to extract clues about the structure of matter from these collisions is not a trivial task, due to the incomplete data we can gather regarding the collisions, the subtlety of the signals we seek and the large rate and dimensionality of the data. These challenges are not unique to high energy physics, and there is the potential for great progress in collaboration between high energy physicists and machine learning experts. I will describe the nature of the physics problem, the challenges we face in analyzing the data, the previous successes and failures of some ML techniques, and the open challenges.
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