research opportunities
Join our lab in advancing AI-driven scientific discovery and engineering design
Our lab offers exciting opportunities for undergraduate and graduate students to engage in cutting-edge research at the intersection of artificial intelligence, machine learning, and scientific discovery. We provide comprehensive mentorship and support students in publishing high-impact research.
For detailed information about our research areas and current projects, please visit our research page.
How to Apply
Interested in joining our research team? Please email Prof. Ron Lin at rongyu.lin@qu.edu with:
- Your CV/resume
- Brief statement of research interests
- Indicate your preferred research area or collaboration interest (if any)
Email Subject: “Research Opportunity - [Your Name] - [Undergraduate/Graduate]”
We welcome students from Computer Science, Engineering, Data Science, Mathematics, Physics, and other quantitative disciplines. Both undergraduate and graduate students are encouraged to apply.
Interdisciplinary Collaborative Opportunities
Joint Research with Dean Taskin Kocak
Dean Taskin Kocak brings extensive expertise in computer networking, IoT systems, and energy-efficient computing. Our collaboration creates unique opportunities at the intersection of AI/ML and network systems.
Research Areas: AI-driven smart grid optimization, intelligent IoT network design, sustainable computing systems, ML for energy efficiency, federated learning for IoT, edge computing optimization.
Joint Research with Dr. Soumyashree Sahoo
Dr. Sahoo specializes in ubiquitous computing, deep learning for health monitoring, and data-driven insights for mental health assessment. Our collaboration enables innovative research combining AI4Science methodologies with health informatics.
Research Areas: Multimodal health monitoring systems, explainable AI for clinical decision support, longitudinal health data analytics, interactive clinical intelligence systems, mental health treatment outcome prediction, privacy-preserving healthcare AI.
Student Success Stories
Kadin Reed - Undergraduate Research Assistant
Achievement: Co-authored publication in Materials Science and Engineering: R: Reports (Impact Factor: 31.6)
Timeline: Published after one year of research as a sophomore
Research Focus: ML-driven materials discovery and active learning framework for semiconductor optimization
Additional Accomplishments:
- Developed active learning framework for semiconductor optimization
- Presented research at regional AI conference
- Gained expertise in physics-informed neural networks and scientific computing
Kadin's co-authored publication in Materials Science and Engineering: R: Reports
Toral Banerjee - Graduate Research Assistant
Achievement: First author publication in Nature
Timeline: Published in six months of focused research
Research Focus: Medical AI and patient data privacy using physics-informed neural networks
Additional Accomplishments:
- Received Best Student Paper Award at international conference
- Now pursuing PhD at top-tier institution
- Developed novel approaches for privacy-preserving medical AI systems
Toral's first-author publication in Nature