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Yu Tian

Assistant Professor

BIOGRAPHY

Yu Tian is an assistant professor at the UCF Department of Computer Science, with a joint appointment in the Department of Clinical Sciences at the College of Medicine. He is also affiliated with the UCF Artificial Intelligence Initiative and directs the AI & Imaging in Medicine Research Lab.

His research focuses on computer vision, machine learning and their applications in the biomedical domains. His expertise includes trustworthy artificial intelligence (AI) anomaly detection, out-of-distribution detection, fairness learning, interpretability, federated learning, multimodal learning and generative AI. Tian has extensively published in top-tier conferences and journals, earning significant recognition from the research community.

Before joining UCF, Tian was a postdoctoral researcher at Harvard University and the University of Pennsylvania. He completed both his doctoral degree (in just three years) and his bachelor’s degree with first class honors in computer science at the Australian Institute for Machine Learning at Adelaide University.

EDUCATION

  • Ph.D. in Computer Science, Australian Institute for Machine Learning (AIML), Adelaide University, 2022
  • B.S. in Computer Science, First Class Honors, Adelaide University, 2018

RESEARCH

  • Computer Vision
  • Machine Learning
  • Biomedical AI
  • Medical Imaging
  • AI for Science

PUBLICATIONS

  • Yu Tian, Yan Luo, Min Shi, Ava Kouhana, Tobias Elze, and Mengyu Wang, in “FairSeg: A Large-Scale Medical Image Segmentation Dataset for Fairness Learning Using Segment Anything Model with Fair Error-Bound Scaling” in International Conference on Learning Representations (ICLR), 2024.
  • Min Shi, Yan Luo, Yu Tian, Lucy Q Shen, Tobias Elze, Nazlee Zebardast, Mohammad Eslami, Saber Kazeminasab, Michael V Boland, David S Friedman, and others, “Equitable Artificial Intelligence for Glaucoma Screening with Fair Identity Normalization” in npj Digital Medicine, 2025.
  • Qihang Zhou, Guansong Pang, Yu Tian, Shibo He, and Jiming Chen, “AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection” in International Conference on Learning Representations (ICLR), 2024.
  • Yan Luo, Muhammad Osama Khan, Congcong Wen, Muhammad Muneeb Afzal, Titus Fidelis Wuermeling, Min Shi, Yu Tian, Yi Fang, and Mengyu Wang, “FairDiffusion: Enhancing Equity in Latent Diffusion Models via Fair Bayesian Perturbation” in Science Advances, 2025.
  • Yu Tian, Congcong Wen, Min Shi, Muhammad Muneeb Afzal, Hao Huang, Muhammad Osama Khan, Yan Luo, Yi Fang, and Mengyu Wang, “FairDomain: Achieving Fairness in Cross-Domain Medical Image Segmentation and Classification” in European Conference on Computer Vision (ECCV), 2024.
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