Curriculum Vitae

Education

University of Illinois Urbana–Champaign — Aug 2023 – present
Ph.D. in Physics

• Selected coursework: Representation-theoretic methods in quantum information; Many-Body Meets Quantum Information

University of Cambridge — 2023
Master of Mathematics (Part III) and B.A. in Mathematics

• Part III (Master of Mathematics): Distinction, ranked 11th of 282
• John Blythe Scholar (award for exceptional undergraduate mathematics performance)
• Jardine Scholar (full-ride merit scholarship for undergraduate and master’s study)

Research Experience

Quantum Information Theory Research (Ph.D.), University of Illinois Urbana–Champaign — Jan 2025 – present
Advisor: Prof. Marius Junge

• Analyze stability of open quantum systems using quantum Markov maps and functional inequalities
• Construct two-dimensional area-law models using Poissonization as a proxy for third quantization
• Study time-dependent controlled open quantum systems via operator-algebraic methods
• Analyze quantum error-correcting codes to establish complexity lower bounds
• Study approximate quantum Markov states and their preparation

High Energy Theory Research (Ph.D.), University of Illinois Urbana–Champaign — Aug 2023 – Dec 2024
Advisors: Prof. Patrick Draper, Prof. Robert Leigh

• Explored holographic principles for causal diamonds and area-law entropy in de Sitter spacetime
• Investigated gravitational path integrals for black hole thermodynamics
• Applied the Mackey machine to quantize corner symmetry groupoids
• Organized and presented weekly journal clubs on generalized symmetries

Vacuum Transitions in the String Theory Landscape, University of Cambridge — Jul 2023 – Aug 2023
Research Assistant; Supervisor: Prof. Fernando Quevedo

• Analyzed moduli stabilization mechanisms in string compactifications
• Modeled vacuum decay via Coleman–de Luccia instantons using Mathematica

Machine Learning for Out-of-Distribution Data, Shanghai Jiao Tong University — Feb 2022 – May 2022
Research Assistant; Supervisor: Dr. Nanyang Ye

• Developed distributionally robust optimization methods for adversarial robustness
• Implemented and validated algorithms in PyTorch

Quantitative Research Intern, Shanghai Wenbo Investment Management — Jan 2022 – May 2022

• Built adversarially trained GBDT and LSTM models for equity forecasting

Skills

Programming Languages: Python, R, MATLAB, Mathematica
Libraries & Frameworks: PyTorch, paddle-quantum, scikit-learn, pandas, NumPy, DRMG

Advanced Training & Workshops

IPAM Long Program (UCLA); Simons Center for Geometry and Physics Workshops — Apr–Jun 2025; Aug 2024

• Quantum optimal transport seminars and string theory landscape workshops

Publications

• N. Ye, L. Zhu, Jia Wang, et al.,
  Certifiable Out-of-Distribution Generalization,
  Proceedings of the AAAI Conference on Artificial Intelligence, 2023.
  DOI: 10.1609/aaai.v37i9.26295

• M. Junge and Jia Wang,
  Generalized Poincaré Inequality for Quantum Markov Semigroups,
  arXiv:2601.06005