Research Opportunities
Join our cutting-edge research in AI and computational materials discovery
Prof. Ron (Rongyu) Lin's Research
As an Assistant Professor of Computer Science at Quinnipiac University, Prof. Lin conducts research at the intersection of artificial intelligence and scientific discovery. His work focuses on developing innovative AI solutions for real-world applications.
Current Research Areas:
- Multi-fidelity and Multi-agent Interactive Engineering: Integrating large language models with domain-specific data for semiconductor design optimization
- Computational Materials Discovery: Developing advanced tools for predicting atomic-level material properties using machine learning
- Intelligent Design Automation: Creating AI-driven systems for semiconductor device modeling and optimization
- AI Cross-domain Applications: Applying machine learning techniques to finance, education, and healthcare challenges
Prof. Lin's research has been published in prestigious journals including Nature, Nature Communications, and Journal of Physics, demonstrating the impact and quality of his work in the scientific community.
Research Opportunities with Joint Faculty Supervision
Students will have the unique opportunity to work under the joint supervision of myself and Prof. Dr. Taskin Kocak, Dean of the School of Computing and Engineering at Quinnipiac University. Dean Kocak is a distinguished researcher with extensive expertise across multiple domains:
- Machine Learning: Advanced algorithms and applications
- Computer Networks: Distributed systems and network optimization
- High-Performance Computing: Parallel processing and supercomputing
- Energy Efficiency: Green computing and sustainable technology solutions
Together, we offer joint mentorship on several cutting-edge interdisciplinary research areas, including:
- AI-Powered Energy Efficiency: Developing machine learning models to optimize energy consumption in high-performance computing systems
- Intelligent Network Optimization: Applying AI techniques to enhance distributed system performance and resource allocation
- Sustainable Computing Solutions: Creating innovative approaches to green computing and environmentally conscious technology design
- High-Performance AI Systems: Integrating advanced AI algorithms with parallel processing architectures
For exceptional students interested in these research areas: Please feel free to contact me at rongyu.lin@qu.edu or reach out directly to Dean Kocak for potential research opportunities and guidance.
Prof. Dr. Taskin Kocak
Dean, School of Computing and Engineering
Student Research Success Stories
Our research group has achieved remarkable success, with undergraduate students making significant contributions to high-impact publications:
Kadin Reed - Sophomore Research Success
After joining the research group as a sophomore student for one year, Kadin co-authored a publication in Materials Science and Engineering: R: Reports, a top-tier journal with an impact factor of 31.6. This achievement demonstrates the high-quality research opportunities available to undergraduate students in our group.
Toral Banerjee - Nature Publication
In just six months as a research assistant, Toral achieved the remarkable distinction of being first author on a publication in Nature, one of the world's most prestigious scientific journals. This correspondence article addresses critical issues in medical AI and patient data privacy.
What This Means for You:
These achievements showcase the exceptional research environment and mentorship available in our group. Students gain:
- Hands-on experience with cutting-edge research projects
- Opportunity to publish in top-tier journals
- Collaborative research experience with faculty
- Skills development in AI, machine learning, and computational science
- Strong preparation for graduate school or industry careers
Join Our Research Team
We are actively seeking motivated undergraduate and graduate students interested in AI/ML applications, computational materials science, and cross-domain AI research.
Interested students are encouraged to email Prof. Lin at:
Please include your academic background, research interests, and any relevant experience in your inquiry.