Education and Employment

Education：

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

Employment：

2021-2024: IAIFI Fellow and Postdoc Associate, MIT & Harvard

2024-2025: Assistant Professor, University of California, Los Angeles

2025-Now: Associate Professor, Tsinghua University

 Research interests

AI + Physics, Quantum Many-body Physics, Quantum Information and Computation

 Awards&Services

Awards：

IAIFI Fellowship The NSF AI Institute for Artificial Intelligence and Fundamental Interactions

 Selected Publications

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).