Matthew D. Schwartz

Matthew Schwartz works on quantum field theory, particle physics, and machine learning. His research spans foundational questions in QFT — including the analytic structure of the S-matrix, factorization and resummation in effective field theories, and non-perturbative phenomena such as renormalons and vacuum stability — as well as precision calculations in collider physics, where his group develops new methods for jet substructure and extracts fundamental parameters like the strong coupling constant from LHC and LEP data. 

A major current focus is machine learning for physics. Schwartz is a founding member of IAIFI, the NSF Institute for Artificial Intelligence and Fundamental Interactions. His group pioneered the use of deep learning for collider physics and more recently has developed symbolic regression techniques for more formal problems in high energy theory. Schwartz is now leading efforts to use AI to facilitate and eventually automate scientific discovery.

Schwartz is the author of the textbook Quantum Field Theory and the Standard Model (Cambridge University Press, 2014).

•  Selected References
  • "Resummation of the C-Parameter Sudakov Shoulder Using Effective Field Theory," M.D. Schwartz, arXiv:2601.02484 (2026)
  • "Renormalons as Saddle Points," A. Bhattacharya, J. Cotler, A. Dersy, M.D. Schwartz, arXiv:2410.07351 (2024)
  • "Applications of the Landau Bootstrap," H.S. Hannesdottir, A.J. McLeod, M.D. Schwartz, C. Vergu, Phys.Rev.D 111 (2025) 085003
  • "Scale Invariant Instantons and the Complete Lifetime of the Standard Model," A. Andreassen, W. Frost, M.D. Schwartz, Phys.Rev.D 97 (2018) 056006, arXiv:1707.08124
  • "Deep Learning in Color: Towards Automated Quark/Gluon Jet Discrimination," P.T. Komiske, E.M. Metodiev, M.D. Schwartz, JHEP 01 (2017) 110
• Full List of Publications (INSPIRE)

More details of Schwartz's research can be found on www.mattschwartzphysics.net.

Administrative Support:   Jennifer Pollock