Ron(Rongyu) Lin | Quinnipiac University
Assistant Professor of Computer Science at School of Computing & Engineering, Quinnipiac University. rongyu.lin@qu.edu

Ron(Rongyu) Lin
Principal Investigator
CCE 233
School of Computing & Engineering
Quinnipiac University
275 Mt. Carmel Ave.
Hamden, CT 06518, USA
rongyu.lin@qu.edu
I am Ron(Rongyu) Lin, currently an Assistant Professor of Computer Science in the School of Computing & Engineering at Quinnipiac University. Before joining Quinnipiac University, I was a Visiting Assistant Professor in the Department of Computer Science at Clark University.
I received my Ph.D. in Electrical and Computer Engineering from King Abdullah University of Science and Technology (KAUST) and an MBA with STEM concentration in Data Science from Santa Clara University. Before joining academia, I was the Principal Data Scientist at Capital One.
My research interests lie in the intersection of artificial intelligence and its applications across different domains. My current research focuses on:
- Multi-fidelity and Multi-agent Interactive Engineering: Integration of large language models (LLMs) with domain-specific data (imaging, spectroscopy, electrical measurements) and specialized learning agents for intelligent semiconductor design and optimization.
- Computational Materials Discovery: Development of multi-fidelity tools for atomic-level material properties prediction and device-scale optimization
- Intelligent Design Automation: Building knowledge-augmented frameworks for semiconductor system modeling and optimization
- AI Cross-domain Applications: Leveraging AI/ML solutions across finance, education, healthcare, and other industrial sectors
My research has been published in prestigious journals and conferences proceedings including Nature, Nature Communications, Journal of Physics, Journal of Materials Chemistry C, and ACL. I actively contribute to the scientific community as a reviewer or committee member for Transactions on Computational Science, Applied Intelligence, and Engineering Applications of Artificial Intelligence.
I am looking for highly motivated students who are interested in AI/ML applications in semiconductor design and computational materials science. 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 team
News
Aug 15, 2025 | 🎉 Exciting news! Our correspondence “Don’t train medical AI on patients’ data without their knowledge” has been accepted for publication in Nature ! Congratulations to Toral! |
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Aug 01, 2025 | 🎓 Exciting news! I’ve joined Quinnipiac University’s School of Computing & Engineering as an Assistant Professor. I’m thrilled to be part of the newly launched Informatics programs specializing in Healthcare, Law, and Data Science, with exciting collaborative opportunities between the Schools of Computing, Law, and Medicine. |
Jun 10, 2025 | 🎉 Welcome Toral Banerjee, our new undergraduate research assistant! We’re excited to have you join our research team. |
May 07, 2025 | 🎉 Welcome Tom Ni, our new undergraduate research assistant! We’re excited to have you join our research team. |
Mar 17, 2025 | 🎉 Thrilled to welcome three new undergraduate researchers to our lab: Lucian Terhorst, Samar Khoso, and Matthew Zaluski! Looking forward to their contributions to our research projects. |
Mar 10, 2025 | 🎉 Excited to announce I’m guest editing a Special Issue on “Trends and Applications of Distributed Artificial Intelligence (AI) and Associated Systems” for the MDPI journal Electronics! Interested in contributing? Check out the Special Issue page for more details. Submissions welcome! 📚🤖 |
Nov 24, 2024 | 🎉 Excited to welcome Kendall, an undergraduate researcher, to our group! |
Nov 07, 2024 | Excited to be a collaborator on “Abstract2Appendix: Academic Reviews Enhance LLM Long-Context Capabilities”, now available on arXiv! 🤝 Our work explores how academic peer review data can enhance LLMs’ long-context capabilities. 📚 |
Sep 27, 2024 | 🎉 Excited to welcome Kadin, an undergraduate researcher, to our group! He will be working on Applied AI applications in semiconductor devices. |
Aug 26, 2024 | 👨‍🏫 I officially begin my role as a Visiting Assistant Professor in the Department of Computer Science at Clark University. |
Selected Publications
- Don’t train medical AI on patients’ data without their knowledgeNature, Aug 2025
- Abstract2Appendix: Academic Reviews Enhance LLM Long-Context CapabilitiesarXiv preprint arXiv:2411.05232, Aug 2024
- Multi-modal preference alignment remedies degradation of visual instruction tuning on language modelsProceedings of ACL, Aug 2024
- High-performance van der Waals antiferroelectric CuCrP2S6-based memristorsNature Communications, Aug 2023
- A machine learning study on superlattice electron blocking layer design for AlGaN deep ultraviolet light-emitting diodes using the stacked XGBoost/LightGBM algorithmJournal of Materials Chemistry C, Aug 2022
- BAlN alloy for enhanced two-dimensional electron gas characteristics of GaN/AlGaN heterostructuresJournal of Physics D: Applied Physics, Aug 2020