About Me
Hello, I’m Ruinan Jin, and I am currently the PhD student focus on medical and trustworthy machine learning. I hold a B.S. (Hon.) in Computer Science from the University of British Columbia (UBC). I have also gained rish industry experiences at Google, Amazon Web Services and Sierra Wireless, working on machine learning research and IoT solutions. Below is a list of my research interests, and I am open to various forms of collaboration. If you share any of these interests, please feel free to reach out!
Research Directions and Past Experiences
My research focuses on building trustworthy machine learning systems for medicine, with an emphasis on foundation and vision-language models (VLMs), medical AI agents, and clinical reasoning and dialogue systems deployed in real-world healthcare settings. I am particularly interested in understanding and improving how medical AI systems behave beyond simulation, including their reliability, safety, fairness, and robustness under real-world clinical use (e.g., longitudinal decision-making, distribution shift, and heterogeneous patient populations).
A central theme of my work is to diagnose failure modes in medical AI systems, such as spurious reasoning, memorization, privacy leakage, bias, and vulnerability to adversarial or distributional perturbations, and to develop defenses and evaluation methodologies with measurable guarantees. My research spans both model development and rigorous evaluation, with a strong focus on high-impact, sensitive domains such as healthcare.
Backdoor attacks and defenses in medical and multimodal models; robustness to adversarial perturbations and deployment-time shifts; safety-oriented evaluation for clinical AI systems.
Memorization and data exposure in medical VLMs; machine unlearning; differential privacy; privacy-preserving synthetic data generation for sensitive medical domains.
Generalization of medical foundation models under distribution shift, heterogeneous clients, and longitudinal use; robustness-oriented evaluation for real-world clinical deployment.
Bias measurement and group-fairness in medical foundation models; fairness-aware benchmarks connecting algorithmic behavior to downstream clinical risk and real-world impact.
Open-source Projects
Community Service
Conferences
- NeurIPS (2025) Top Reviewer
- MICCAI (2025)
Journals
- IEEE Transactions on Medical Imaging Distinguished Reviewer (Bronze)
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- IEEE Transactions on Neural Networks and Learning Systems
- IEEE Journal of Biomedical and Health Informatics
- IEEE Transactions on Artificial Intelligence
- ACM Computing Surveys
- Medical Image Analysis
- Neural Networks
- Pattern Recognition