The Department of Biomedical Informatics and Data Science is pleased to welcome five new postdoctoral researchers who joined our team since last fall.
These researchers bring expertise in computational genomics, AI-driven precision medicine and privacy-preserving genomic analysis. Their work will contribute to advancing biomedical informatics and data science research at Yale.
Kang Hu is a postdoctoral associate at Yale University with a research focus on computational genomics and transposable element biology. He earned his Ph.D. in Computer Science from Central South University in 2025, where he developed HiTE and panHiTE, two bioinformatics frameworks for accurate detection and annotation of transposable elements in population-scale genomic studies. He previously obtained an M.S. in Computer Science from Central South University and a B.S. in Computer Science from Sichuan University.
Dr. Tian Lan is a Postdoctoral Associate with Dr. Sai Zhang at the Department of Biomedical Informatics & Data Science, School of Medicine at Yale University. His current research is about computational modeling of aging process and related diseases. Dr. Lan earned his Ph.D degree in Computer Science from University of Technology Sydney. Before his current role, Dr. Lan was a Postdoctoral Research Scientist in the Department of System Biology at Columbia University.
Yurui is a computational biomedical researcher developing AI methods for precision medicine and drug discovery. He earned his PhD in Mathematics from the National University of Singapore (NUS), where he developed and applied machine learning models to support drug development. In the final year of his PhD, he also worked as a research scientist at the Agency for Science, Technology and Research (A*STAR).
His research aims to build integrative models across pharmacogenomics, human genetics, single-cell RNA, and clinical/EHR data to predict health outcomes and treatment response. Broadly, his goal is to develop robust, interpretable, and clinically relevant AI models that translate multi-scale biomedical data into actionable insights for prevention, diagnosis, and therapeutic development.
I am a Postdoctoral Associate at Yale University, working in the Hoon Cho lab. My research focuses on secure genomic analysis, particularly applications of Homomorphic Encryption.
My primary research focuses on practical applications of cryptosystems, especially in homomorphic encryption, privacy-preserving machine learning, and genomic data privacy. I work extensively with Fully Homomorphic Encryption (FHE), optimizing FHE schemes algorithmically and developing algorithms to enable non-arithmetic operations within FHE. In privacy-preserving machine learning, I adapt machine learning algorithms to FHE-compatible formats, utilizing polynomial approximation techniques to achieve secure and efficient computation. My research in genomic data privacy emphasizes designing FHE-based frameworks that enable secure transformations of conventional genomic applications, protecting sensitive genomic information. I am also dedicated to advancing functional encryption by creating schemes that offer enhanced efficiency and functionality.