Curriculum Vitae

Download Full CV (PDF)


Bio

Kyle Cranmer is the David R. Anderson Director of the UW-Madison Data Science Institute and a Professor of Physics with courtesy appointments in Statistics and Computer Science. He is also the Editor in Chief of the journal Machine Learning Science and Technology. Cranmer was a Professor of Physics and Data Science at NYU from 2007 – 2022. He obtained his Ph.D. in Physics from the University of Wisconsin-Madison in 2005. He was awarded the Presidential Early Career Award for Science and Engineering in 2007, the National Science Foundation's Career Award in 2009, and became a Fellow of the American Physical Society in 2021 for his work at the Large Hadron Collider. Professor Cranmer developed a framework that enables collaborative statistical modeling, which was used extensively for the discovery of the Higgs boson in 2012. His current interests are at the intersection of physics, statistics, and machine learning.

Experience

David R. Anderson Director, Data Science Institute, UW-Madison — 2022-Present

  • Professor of Physics with Affiliate Appointments in Computer Science & Statistics
  • Leads the Data Science Institute, a key to the university's strategic initiative in data science
  • Leads UW-Madison's RISE-AI Headquarters

Visiting Scientist, FAIR (Facebook) / Meta AI — 2021-2022 (sabbatical)

  • Worked with Yann LeCun and Léon Bottou focusing on self-supervised learning, probabilistic & causal machine learning, and advising on AI for Science projects

Visiting Junior Faculty, Institute for Advanced Study — 2018

  • Focusing on AI for Science: Lattice Field Theory & Simulation-Based Inference

Professor of Data Science, NYU — 2015-2022

  • Core member of the group that created the Center for Data Science at NYU
  • Served as Executive Director of the Moore-Sloan Data Science Environment (2018-2022)
  • Member of Computational Intelligence, Vision, and Robotics (CILVR) Lab at NYU (2018-2022)

Assistant, Associate, Full Professor of Physics, NYU — 2007-2022

  • Initially focused on experimental particle physics at the Large Hadron Collider; established data analysis and statistical techniques used for the discovery of the Higgs boson in 2012 (Nobel prize in 2013)
  • Received PECASE award from President Bush in 2007 for early work
  • Pivoted to AI/ML and data science for physical sciences in 2014, while maintaining active research in particle physics

Education

  • PhD Physics, University of Wisconsin—Madison — 2005
  • BA in Physics & Mathematics, Rice University — 1999

Awards

  • Inaugural Margot and Tom Pritzker Prize for AI in Science Research Excellence — 2025
  • Breakthrough Prize in Fundamental Physics — 2025
  • Fellow of the American Physical Society — 2021
  • Kavli Frontiers of Science Fellow — 2018 & 2009
  • Visiting Junior Faculty, Institute for Advanced Study — 2018
  • Chaire Georges Lemaître 2017, Université Catholique de Louvain, Belgium
  • Presidential Early Career Award for Science and Engineering (PECASE) — 2007

Publication Highlights

  • >1,200 publications with >349,000 citations (Google Scholar); h-index 241
  • 9th highest citation in Google Scholar for "Deep Learning"
  • 10 authors or less: 99 papers, >13,000 citations (INSPIRE)
  • Higgs discovery paper (1 of 9 core authors), 2012 — 24,000 citations
  • Key paper establishing statistical procedures for particle physics — >10,000 citations
  • Standard citation for Simulation-Based Inference in PNAS, 2020 — 1,000 citations
  • Machine learning and the physical sciences review, 2019 — 2,400 citations
  • 11 papers with DeepMind on AI for field theory (inc. Nature review paper) — 570 citations
  • 2 papers with DeepMind on AI for dynamical systems — 760 citations
  • 1 paper with FAIR/Meta AI on AI for theoretical physics topics

Speaking Highlights

  • Keynote speaker ADSA Data Science and AI Leadership Summit — 2025
  • Keynote speaker Helmholtz AI (Germany's largest research organization) — 2024
  • Invited speaker AAAI workshop on explainable AI — 2024
  • Invited speaker AI for Science workshop at NeurIPS — 2023
  • Keynote speaker Learning on Graphs — 2023
  • Hans Jensen memorial lecture, Heidelberg Graduate Days — 2023
  • Invited panelist for plenary panel at IEEE/CVF CVPR (~10,000 registered) — 2023
  • Keynote speaker AISTATS conference — 2021
  • Sackler Colloquium, National Academy of Sciences — 2019
  • Keynote speaker NeurIPS conference (~6,000 registered) — 2016

Public Engagement Highlights

  • 18K Twitter followers
  • Webby award for Crayfis citizen science project — 2015
  • NPR Here and Now — Dec 2015
  • TEDx Binghamton — 2013
  • Star Talk Radio with Neil deGrasse Tyson and Bill Nye "the science guy" — 2012
  • Invited talk, Strata Conference, NYC — 2011
  • Invited talk, Ideas Economy: Information, Economist magazine — 2011
  • Guest on Charlie Rose News Hour — 2010

Professional Service Highlights

  • Editor-in-Chief Machine Learning: Science and Technology (IOP Publishing) — 2022-present
  • Action Editor Journal of Machine Learning Research (JMLR) — 2024-present
  • Member DOE Basic Needs Assessment for AI in high-energy physics — 2024
  • Member Particle Physics Project Prioritization Panel (P5) for NSF and DOE — 2023
  • Member High Energy Physics Advisory Panel (DOE FACA) — 2016-2019
  • Executive Director Moore-Sloan Data Science Environment — 2018-2022
  • Co-founded Machine Learning for Physical Sciences workshop series at NeurIPS — 2017-present
  • Co-founded Hammers and Nails workshop series — 2017-present
  • Lead organizer of several international conferences and workshops with 100-600 participants