Ron(Rongyu) Lin | Quinnipiac University

I am Ron (Rongyu) Lin, a tenure-track Assistant Professor of Computer Science in the School of Computing & Engineering at Quinnipiac University.

I received my Ph.D. in Electrical and Computer Engineering from King Abdullah University of Science and Technology (KAUST) and an MBA with a STEM concentration in Data Science and Business Analytics from Santa Clara University, and I am currently pursuing a Doctor of Education at Johns Hopkins University. Before joining Quinnipiac, I was a Visiting Assistant Professor of Computer Science at Clark University; before entering academia, I was a Principal Data Scientist at Capital One, where I built valuation models for the auto loan portfolio.

My research develops artificial intelligence methods for scientific discovery, with semiconductor materials and devices as the central application. Beyond semiconductors, my research also spans AI for healthcare, LLM multi-agent simulation of marketing and consumer behavior, and energy-efficient AI systems:

• AI for Semiconductor Materials and Device Design: Machine learning and physics-based simulation for wide-bandgap devices, including III-nitride tunnel junctions, AlGaN deep-ultraviolet LEDs, GaN/AlGaN heterostructure transistors, and β-Ga₂O₃ thin films and photodetectors, combining TCAD simulation with machine learning surrogate models. For two-dimensional materials, property prediction and first-principles calculation, automated identification of exfoliated flakes, and modeling of 2D memristors and graphene diodes. Current work develops multi-fidelity and LLM-agent frameworks that link atomic-scale prediction, device simulation, and experimental characterization for automated device design.
• Multimodal AI for Medication and Drug Recommendation: Interpretable, multimodal models that combine patient records, molecular structure, and drug-drug interaction knowledge to recommend safe and effective medications.
• LLM Multi-Agent Simulation for Marketing and Consumer Behavior: Generative agents that simulate consumer decisions, social interaction, and marketplace dynamics to test marketing strategies before deployment.
• Energy-Efficient AI Systems: Reducing the energy consumption of foundation models and agentic AI across the stack, from hardware and system architecture to agent orchestration and harness design, model fine-tuning, and smaller, more efficient models.

My work has appeared in venues including Nature, Nature Communications, Materials Science and Engineering: R: Reports, Journal of Materials Chemistry C, ACL, and IEEE conferences. I serve on the program committees of AAAI, IJCAI-ECAI, and IEEE ICEBE, as a guest editor for special issues of MDPI Electronics and Computers, and as a reviewer for journals including Nature Electronics and IEEE Transactions on Fuzzy Systems.

I am looking for highly motivated students who are interested in AI for semiconductor materials and device design, and in the broader applications above. If you are passionate about developing machine learning frameworks for scientific discovery, please email me your CV and research interests.

I am open to research collaborations and discussions. Please feel free to reach out via email!

Our Research Group

Our research group at Quinnipiac University, Spring 2026

Our research team