Machine Learning from the Perspective of Physics | In-person

Master of Science in Applied Computing @ UofT

This talk will explore the perspective that physics may bring towards understanding neural network architectures and algorithms.

700 University Ave | 9th Floor | Rooms: UY9171,UY9173,UY9195,UY9199

Join us for MScAC Talks, a yearly speaker series where academic and industry leaders share applied research driving real-world advances, from September to April at the University of Toronto.AbstractNeural networks are the backbone of artificial intelligence and machine learning systems. Despite the immense success of neural networks at a variety of real-world problems, the theory of deep (multi-layer) neural networks is still in its infancy. There are many tantalizing analogies between neural networks and situations we encounter in all branches of physics: the interactions of a large number of entities which give rise to simple collective behaviour is strongly reminiscent of statistical mechanics and condensed matter physics, and the data structures encountered in physics may provide tractable models for how neural networks learn from complex real-world data. This talk will explore the perspective that physics may bring towards understanding neural network architectures and algorithms.Speaker BiographyYonatan Kahn is an assistant professor in the <a href="https://www.physics.utoronto.ca/" title="https://www.physics.utoronto.ca/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Department of Physics</a>. He is a theoretical physicist whose research is focused on dark matter and its detection strategies, as well as the theory of machine learning from a high-energy physics perspective. Prior to joining the Faculty of Arts &amp; Science, Kahn was an assistant professor at University of Illinois Urbana-Champaign and held postdoctoral positions at the Kavli Institute for Cosmological Physics (KICP) at the University of Chicago and Princeton University before that.Professor Kahn received his PhD in 2015 from the Massachusetts Institute of Technology. He holds degrees in music, physics, and mathematics from Northwestern University (BA, BMus 2009) and completed Part III of the Mathematical Tripos with Distinction at the University of Cambridge in 2010, supported by a Churchill Scholarship. In 2016, he received the American Physical Society’s J.J. and Noriko Sakurai Dissertation Award in Theoretical Particle Physics, and in 2022 he was selected as a Kavli Frontiers of Science Fellow by the National Academy of Sciences in the US.Kahn’s recent work includes several review articles on novel detection methods for dark matter, published in Reports on Progress in Physics, Nature Reviews Physics and Annual Reviews of Nuclear and Particle Science. He has a strong interest in physics education and is the co-author of "Conquering the Physics GRE," a comprehensive study guide for preparing for the Physics GRE exam.About the MSc in Applied Computing ProgramHoused in the <a href="https://web.cs.toronto.edu/" title="https://web.cs.toronto.edu/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Department of Computer Science</a> at the University of Toronto, the <a href="https://mscac.utoronto.ca/" title="https://mscac.utoronto.ca/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Master of Science in Applied Computing (MScAC) program</a> is committed to educating the next generation of world-class innovators. The program bridges cutting-edge research with industry innovation, equipping students to translate ideas into real-world solutions. By fostering collaboration among students, faculty, alumni, and industry partners, MScAC creates a vibrant community where research excellence drives meaningful impact.

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Machine Learning from the Perspective of Physics | In-person

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