Postdoctoral Research Scholar · Arizona State University
Li-Li Ye
Physics & Machine Learning
Focus on AI for physical problem-solving, physics-informed machine learning models, and physical insights for AI.
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Postdoctoral Research Scholar · Arizona State University
Physics & Machine Learning
Focus on AI for physical problem-solving, physics-informed machine learning models, and physical insights for AI.
I am a Postdoctoral Research Scholar in the Dr. Ying-Cheng Lai's Lab at Arizona State University, where I received my Ph.D. in Electrical Engineering in 2026. My research bridges physics and machine learning, applying reinforcement learning, reservoir computing, and deep learning to problems in quantum many-body physics, photonic systems, and nonlinear dynamics.
I am a recipient of the Dean's Dissertation Award of the Ira A. Fulton Schools of Engineering, presented to up to 5% of the prior year's Ph.D. graduates.
Leveraging quantum dynamical phases as computational substrates, bridging the classical edge-of-chaos paradigm with quantum mechanical phenomena.
Applying RL to control quantum many-body systems, and engineer entanglement in optomechanical systems.
Studying exotic electronic properties — Berry phase, spin-dependent edge states, Bloch-Zener oscillations, and geometry-induced wave-function collapse — in 2D quantum materials.
Designing optimal sparse networks and disorder-mediated synchronization mechanisms for large arrays of coupled semiconductor lasers using physics-informed machine learning.
Reservoir Computing: From Classical Edge of Chaos to Quantum Dynamical Phases
Optimal Sparse Networks for Synchronization of Semiconductor Lasers
Physics-Informed Network Optimization for Taming 1,000 Disordered Lasers
Machine Learning with Small Data for Nonlinear Dynamics
Reinforcement Learning in Physics: A Survey
Tailoring Laser Coupling for Efficient Synchronization
Disorder-Mediated Synchronization Resonance in Coupled Semiconductor Lasers
Optimizing Disorder with Machine Learning to Harness Phase Synchronization
Entanglement Engineering of Optomechanical Systems by Reinforcement Learning
Controlling Nonergodicity in Quantum Many-Body Systems by Reinforcement Learning
Floquet Quantum Many-Body Scars in the Tilted Fermi-Hubbard Chain
Optical Properties of Two-Dimensional Dirac-Weyl Materials with a Flatband
Experimental Scheme for Determining the Berry Phase in Two-Dimensional Quantum Materials with a Flat Band
Higher-Order Exceptional Points in Noise-Assisted Sensing Structure
Deep-Learning Design of Graphene Metasurfaces for Quantum Control and Dirac Electron Holography
Irregular Bloch-Zener Oscillations in Two-Dimensional Flat-Band Dirac Materials
Spin-Dependent Edge States in Two-Dimensional Dirac Materials with a Flat Band
Continuous Variational Quantum Algorithms for Time Series
Geometry-Induced Wave-Function Collapse
Relativistic Quantum Scarring, Spin-Induced Phase, and Quantization in a Symmetric Dirac Billiard System
Journal Referee: Nature Communications, PRX Quantum, Physical Review A/B/E/Research/Applied, Annals of Physics (2023–Present) · Session Chair: APS DAMOP (2025), APS March Meeting (2025), APS 4CS (2023)
Reservoir Computing: From Classical Edge of Chaos to Quantum Dynamical Phases
Quantum Reservoir Computing for Noisy, High-Dimensional Magnetic Navigation
Machine Learning Applications in Complex and Quantum Systems
Machine Learning Applications in Complex and Quantum Systems
Entanglement Engineering of Optomechanical Systems by Reinforcement Learning
Disorder-Induced Synchronization in Laser Models
Entanglement Engineering of Optomechanical Systems by Reinforcement Learning
Irregular Bloch-Zener Oscillations in 2D Flat-Band Dirac Materials
Li-Li Ye
School of Electrical, Computer and Energy Engineering
Arizona State University
650 E Tyler Mall, Tempe, AZ 85281
(+1) 480-886-5286