Dr. Ricky Renjie Li

Welcome to my world of AI for Scientific Discovery, or AI4Science!!

Postdoctoral Research Associate at the Holonyak Micro & Nanotechnology Lab, University of Illinois Urbana-Champaign.

I build agentic AI systems for scientific discovery, with work spanning microchips, deep learning, LLMs, reinforcement learning, nanophotonics, chemistry, and materials science.

Check out my agentic playground: ravenllm.com!

I received my Ph.D. in Computer and Information Engineering from The Chinese University of Hong Kong in June 2024, advised by Prof. Zhaoyu Zhang.

Email: renjie2@illinois.edu

Publications Code LinkedIn

Current Focus

AI4Sci: AI for photonics and materials autonomous discovery, with an emphasis on model-guided experimentation and inverse design.

Research Style

Interdisciplinary work connecting machine learning, nanophotonic devices, fabrication constraints, and scientific automation.

Background

Started in Mechanical engineering, grounded in semiconductor and optoelectronics, and ended up in AI/ML.

Career Status

Postdoctoral Research Associate at UIUC, advised by Prof. L. Goddard.
Founder and CEO of Synthera.

Education

Ph.D. in Computer and Information Engineering, CUHK, advised by Prof. Zhaoyu Zhang, co-mentored by Prof. Feng Yin and Prof. Yin Zhang, 2020.08-2024.06.
Research Assistant, Mechanical Engineering Department, UCLA, mentored by Prof. Artur Davoyan, 2017.09-2020.03.
B.S. in Mechanical Engineering (Robotics track), UCLA, 2013.09-2017.05.

Work Experience

Walt Disney Company, Ride & Show Engineering, Orlando, Florida, R&D Engineering Intern, 2018.06-2018.12.
Taiyuan Heavy Industry Group, Wind Turbine Department, Shanxi, China, Simulation & Modelling Intern, 2017.06-2017.09.
Center for Translational Applications of Nanoscale Multiferroic Systems (TANMS), UCLA, Research Intern, supervised by Prof. Christopher Lynch, 2017.09-2019.02.

Research

My work sits at the intersection of machine learning, photonic device design, and scientific discovery.

AI for Photonics

Materials Discovery

Deep Learning and LLMs

Self-Driving Labs

Photonic Neuromorphic Computing

Semiconductor Materials and Fabrication

Publications

View the full publication list on Google Scholar.

Journal Publications

[J1] Li, R., et al., Photonics for Neuromorphic Computing: Fundamentals, Devices, and Opportunities
Advanced Materials, 2025. Journal IF 26.8
Paper

[J2] Li, R., et al., What Is Next for LLMs? Pushing the Boundaries of Next-gen AI Computing Hardware with Photonic Chips
Nanophotonics, 2025. Journal IF 6.6
Paper

[J3] Xin, Q.#, Li, R.#, et al., Filling the simulation-to-reality gap: High-degree-of-freedom AI-optimized photonic crystal nanobeam resonators with fabrication tolerance
Optics Letters, 2024. Journal IF 3.3
Paper

[J4] Li, R., et al., Deep reinforcement learning empowers automated inverse design and optimization of photonic crystals for nanoscale laser cavities
Nanophotonics, 2023. Journal IF 6.6
Paper

[J5] Li, H., et. al, Li, R., et al., Monolithically integrated photonic crystal surface emitters on silicon with a vortex beam by using bound states in the continuum
Optics Letters, 2023. Journal IF 3.3
Paper

[J6] Li, R., et al., Smart and Rapid Design of Nanophotonic Structures by an Adaptive and Regularized Deep Neural Network
Nanomaterials, 2022. Journal IF 4.3
Paper

[J7] Chen, X.#, Li, R.#, et al., POViT: Vision Transformer for Multi-Objective Design and Characterization of Photonic Crystal Nanocavities
Nanomaterials, 2022. Journal IF 4.3
Paper

[J8] Li, R., et al., Deep learning-based modeling of photonic crystal nanocavities
Optical Materials Express, 2021. Journal IF 3.1
Paper

[J9] Ling, H., Li, R., et al., All van der Waals integrated nanophotonics with bulk TMDCs
ACS Photonics, 2021. Journal IF 6.7
Paper

[J10] Li, R., et al., Optimization Strategies for CNN-Based Modeling of Photonic Crystal Nanocavities
2025. Under Review
Preprint

[J11] Li, R., et al., Lynford Goddard, Leveraging Large Language Models for Accelerated Discovery of Novel Semiconductor and Polymer Materials
2026. Under Review
Project Link

[J12] Li, R., R. Wang, Lynford Goddard, Photoelectrochemical-etched porous intrinsic Si and Ge thin films as scaffold substrates for subsurface two-photon direct-laser writing
2026. In Preparation
Public link pending

Conference Publications

[C1] Li, R, Y. Zeng, H. Tong, et al., Graph Convolutional Networks for Organic Molecular Properties Prediction
2026. Under Review
Preprint

[C2] Zeng, Y.#, Li, R.#, QuantumChem-300K: A Large-Scale Open Organic Molecular Dataset for Quantum-Chemistry Property Screening and Language Model Benchmarking
AAAI 2026 Workshop on AI4Research, Jan. 2026, Singapore. Conference Poster
Preprint

[C3] Li, R., et al., LLM4Laser: Large Language Models Automate the Design of Lasers
AAAI 2026 Workshop on AI4Research, Jan. 2026, Singapore. Conference Poster
Preprint

[C4] Suwandi, R., et al., Li, R., et al., Adaptive Kernel Design for Bayesian Optimization Is a Piece of CAKE with LLMs
NeurIPS 2025, San Diego, USA. Accepted Poster
Paper

[C5] Zhang, C.#, Li, R.#, et al., Inverse Design of Photonic Crystal Surface Emitting Lasers is a Sequence Modeling Problem
AAAI 2024 Workshop on AI to Accelerate Science and Engineering (AI2ASE), Feb. 2024, Vancouver, Canada. Conference Poster
Paper

[C6] Li, R., et al., Predicting the Q factor and modal volume of photonic crystal nanocavities via deep learning
SPIE Photonics Asia, 2021. Conference Oral + Poster
Paper

Patents

Zhaoyu Zhang, Wenye Li, Renjie Li, Yueyao Yu. Method for Encoding Photonic Crystals Using Self-Attention-Based Transformer Deep Neural Networks, China Patent 2022115464376, granted March 24, 2023.
Renjie Li, Zhaoyu Zhang. Method for Inverse Design and Optimization of Optical Resonator Cavities Based on Reinforcement Learning, China Patent 2022103349663, granted November 11, 2022.
Renjie Li, Ceyao Zhang, Feng Yin, Zhaoyu Zhang. Meta Reinforcement Learning-based Optimization Methods for Topological Insulator Lasers, China Patent 2024103706320, granted May 11, 2024.

Awards and Activities

1st Place in the Doctoral Research Conference organized by SSE, CUHK, 2023.
Oral presentations at the Doctoral and Postdoctoral Dao Yuan Forum organized by SRIBD, CUHK, in 2022 and 2024.
Poster presentation at the 38th AAAI Conference on Artificial Intelligence, Vancouver, 2024.
Outstanding Ph.D. Candidate Scholarship from SRIBD, CUHK, 2022-2023.
Graduate Student Research Assistantship, UCLA, 2018-2020.
Teaching assistant for calculus, electrodynamics, general physics, thermodynamics, semiconductor laboratory, digital circuitry, and computer architecture courses.