Yijun Sun, Ph.D.

Professor of Bioinformatics

Department of Microbiology and Immunology

Department of Computer Science and Engineering

Associate Director of AI and Health Science

Institute for Artificial Intelligence and Data Science

University at Buffalo, The State University of New York

Email: yijunsun[AT]buffalo[DOT]edu

 

I received a dual BS degree in electrical and mechanical engineering from Shanghai Jiao Tong University in 1995, and obtained MS and PhD degrees in electrical engineering from the University of Florida, Gainesville, in 2003 and 2004, respectively. From 2005 to 2012, I was an Assistant Scientist at the Interdisciplinary Center for Biotechnology Research and an affiliated faculty member at the Department of Electrical and Computer Engineering at the University of Florida. I joined University at Buffalo in 2012 Fall as an Assistant Professor of Bioinformatics at the Department of Microbiology and Immunology and the New York State Center of Excellence in Bioinformatics and Life Sciences. I was promoted to Associate Professor and Full Professor in 2017 and 2023, respectively.

My research interests are primarily on machine learning, data mining, bioinformatics and their applications to microbial ecology and cancer genomics. I am a co-recipient of the 2005 IEEE M. Barry Carlton Best Transactions Paper Award. One of my papers is selected as the Spotlight Paper in the September 2010 issue of the prestigious TPAMI journal. My research is supported by National Science Foundation, National Institutes of Health, Florida Cancer Research Program, and Susan Komen Breast Cancer Foundation, and my work on metagenomics and feature selection has been used by more than 200 research institutes worldwide.

Research Interests

• Bioinformatics/computational biology: next-generation sequencing data analysis, transcriptome data analysis, cancer genomics, metagenomics, microbial community analysis, phylogenetic analysis, gene regulatory network modeling, molecular diagnosis and prognosis, cancer progression modeling, tumor evolution, single-cell sequencing data analysis
• Machine Learning/data Mining: classification, regression, clustering, structure learning, ensemble learning, dimensionality reduction, feature selection/extraction, graphical modeling, large-scale data analysis, deep learning