Yonatan Kahn

Yonatan Kahn is an Assistant Professor in the Department of Physics at the University of Toronto's Faculty of Arts & Science. A theoretical physicist, his work focuses on dark matter detection strategies, axions, machine learning in high-energy physics, and new physics at colliders and beam dumps. He has held previous positions at the University of Illinois Urbana-Champaign, Kavli Institute for Cosmological Physics (University of Chicago), and Princeton University.

• PhD in Physics from Massachusetts Institute of Technology (2015)
• Bachelor’s degrees in Physics and Music from Northwestern University (2009)
• Part III of the Mathematical Tripos with Distinction at University of Cambridge (2010, Churchill Scholarship)

His research bridges dark matter theory with innovative applications of machine learning in particle physics. He has contributed to review articles in Reports on Progress in Physics, Nature Reviews Physics, and Annual Reviews of Nuclear and Particle Science. As a physics education advocate, he co-authored Conquering the Physics GRE, a widely acclaimed study guide.

Recent accolades include the Sakurai Dissertation Award (2016) and Kavli Frontiers of Science Fellowship (2022). He teaches graduate courses in Quantum Field Theory and Physics of Machine Learning at the University of Toronto.