University of California, Berkeley

Haichen Wang

Associate Professor of Physics

Biography

I am an Associate Professor in the Physics Department at the University of California, Berkeley, and a Faculty Scientist in the Physics Division at Lawrence Berkeley National Laboratory (LBNL).

Experience & Education

  • Jul. 2024 – Present

    Associate Professor of Physics

    University of California, Berkeley

  • Jan. 2019 – Jun. 2024

    Assistant Professor of Physics

    University of California, Berkeley

  • Jan. 2019 – Present

    Faculty Scientist

    Lawrence Berkeley National Laboratory (LBNL)

  • Aug. 2013 – Dec. 2018

    Owen Chamberlain Research Fellow

    Lawrence Berkeley National Laboratory (LBNL)

  • Aug. 2007 – Jul. 2013

    Ph.D. in Physics

    University of Wisconsin-Madison

  • Sep. 2003 – Jul. 2007

    Bachelor of Science

    Peking University

Research

My research program stands on the energy frontier of particle physics, developing experimental instrumentation, and applying AI/ML methods to collider data.

LHC Particle Physics Data Analysis

Analyzing data from the ATLAS experiment at the LHC. Focuses on measuring the total width of the Higgs boson, characterizing the Higgs–top Yukawa coupling, and investigating the CP properties of top-Higgs interactions in the diphoton and \(b\bar{b}\) decay channels.

Improving Detector Performance

Applying graph neural networks and graph transformers to calibrate electron and photon signals at the cell-level in the ATLAS electromagnetic calorimeter. This approach treats electromagnetic showers as graphs, yielding a 20% improvement over traditional calibration methods.

AI & Machine Learning for Physics

Developing advanced AI models, including normalizing-flow-based detector response simulations, pretrained event-classification foundation models (trained on 120M simulated collision events) to reduce data training barriers, and LLM-powered agents to automate complex computational tasks.

ATLAS Inner Tracker Upgrade

Contributing to the development and qualification of the all-new LBNL silicon Inner Tracker (ITk) for the High-Luminosity LHC. Involved in strip/pixel module site qualification, quality control, and diagnostics (such as resolving the pixel "core column" defect).

Publications

  1. ATLAS Collaboration, "Constraint on the total width of the Higgs boson from Higgs boson and four-top-quark measurements in pp collisions at \(\sqrt{s} = 13\text{ TeV}\) with the ATLAS detector," Phys. Lett. B861 (2025), p. 139277. arXiv:2407.10631
  2. ATLAS Collaboration, "Evidence of off-shell Higgs boson production from ZZ leptonic decay channels and constraints on its total width with the ATLAS detector," preprint (2023). arXiv:2304.01532
  3. ATLAS Collaboration, "Constraining off-shell Higgs boson production and the Higgs boson total width using \(WW \rightarrow \ell\nu\ell\nu\) final states with the ATLAS detector," preprint (Apr. 2025). arXiv:2504.07710
  4. Allison Xu et al., "Generative machine learning for detector response modeling with a conditional normalizing flow," JINST 19.02 (2024), P02003. arXiv:2303.10148
  5. ATLAS Collaboration, "Measurement of the \(H \rightarrow \gamma\gamma\) and \(H \rightarrow ZZ^* \rightarrow 4\ell\) cross-sections in pp collisions at \(\sqrt{s} = 13.6\text{ TeV}\) with the ATLAS detector," Eur. Phys. J. C84.1 (2024), p. 78. arXiv:2306.11379
  6. Joshua Ho et al., "Pretrained Event Classification Model for High Energy Physics Analysis," preprint (Dec. 2024). arXiv:2412.10665
  7. Eli Gendreau-Distler, Luc Le Pottier, and Haichen Wang, "Transforming Simulation to Data Without Pairing," 38th Conference on Neural Information Processing Systems (NeurIPS 2024) (Apr. 2025). arXiv:2504.12343

Mentorship

Research Group Photo

Postdoctoral Researchers

  • Dongwon Kim Postdoctoral Researcher

Current Graduate Students

  • Luc Le Pottier 6th-Year Ph.D. Student
  • Charles Hultquist 5th-Year Ph.D. Student
  • Chengxi Yang 5th-Year Ph.D. Student
  • Jose Esparza 3rd-Year Ph.D. Student
  • Joshua Ho 2nd-Year Ph.D. Student

Alumni