Di Luo Associate Professor (Adjunct)
E-mail: diluo（AT）tsinghua.edu.cn
Education background
2012–2016: B.Sc. in Physics and Mathematics (Double Major), The University of Hong Kong
2016–2018: M.S. in Physics, University of Illinois Urbana-Champaign
2018–2021: M.S. in Mathematics, University of Illinois Urbana-Champaign
2016–2021: Ph.D. in Physics, University of Illinois Urbana-Champaign
Experience
2021-2024: IAIFI Fellow and Postdoc Associate, MIT & Harvard
2024-2025: Assistant Professor, University of California, Los Angeles
2025-Now: Associate Professor, Tsinghua University
Areas of Research Interests/ Research Projects
AI + Physics, Quantum Many-body Physics, Quantum Information and Computation
Honors And Awards
IAIFI Fellowship The NSF AI Institute for Artificial Intelligence and Fundamental Interactions
Academic Achievement
All Publications: https://scholar.google.com/citations?user=OxZytTQAAAAJ&hl=en&oi=ao
1. Di Luo, Bryan K. Clark, Backflow Transformations via Neural Networks for Quantum Many-Body Wave-Functions, Phys. Rev. Lett. 122, 226401(2019).
2. Di Luo, Giuseppe Carleo, Bryan K. Clark, James Stokes, Gauge Equivariant Neural Networks for Quantum Lattice Gauge Theories, Phys. Rev. Lett. 127, 276402 (2021).
3. John M. Martyn, Khadijeh Najafi, Di Luo#, Variational Neural-Network Ansatz for Continuum Quantum Field Theory, Phys. Rev. Lett. 131, 081601 (2023) Editor’s Suggestion.
4. Di Luo†, Zhuo Chen†, Juan Carrasquilla, Bryan K. Clark, Autoregressive Neural Network for Simulating Open Quantum Systems via a Probabilistic Formulation, Phys. Rev. Lett. 128, 090501 (2022).
5. Luuk Coopmans†, Di Luo†, Graham Kells, Bryan K. Clark, Juan Carrasquilla, Protocol Discovery for the Quantum Control of Majoranas by Differentiable Programming and Natural Evolution Strategies, PRX Quantum 2, 020332 (2021).
6. Di Luo†, Jiayu Shen†, Rumen Dangovski, Marin Soljačić, QuACK: Accelerating Gradient-Based Quantum Optimization with Koopman Operator Learning, Advances in Neural Information Processing Systems (NeurIPS) 36 (2024) Spotlight.
7. Zhuo Chen, Jacob McCarran, Esteban Vizcaino, Marin Soljačić, Di Luo#, TENG: Time-Evolving Natural Gradient for Solving PDEs with Deep Neural Net, ICML 2024
8. Owen Dugan, Peter Y. Lu, Rumen Dangovski, Di Luo#, Marin Soljačić, Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows, International Conference on Machine Learning (ICML) 2023, 8879-8901.
9.Zhuo Chen, Rumen Dangovski, Charlotte Loh, Owen Dugan, Di Luo#, Marin Soljačić, QuanTA: Efficient High-Rank Fine-Tuning of LLMs with Quantum-Informed Tensor Adaptation, 38th Conference on Neural Information Processing Systems (NeurIPS 2024).
