Jiangwen Sun

Ph.D., University of Connecticut

M.E., Nanjing University, China

B.M., Secondary Military Medical University, China

Assistant Professor

Department of Computer Science, College of Science

Old Dominion University

Contact Information

3300 Engineering & Computational Sciences Building

Norfolk, Virginia 23529-0162

Office: E&CS 3204

Tel: (757) 683-7712

Email: jsun@odu.edu

I am seeking PhD students who are self-motivated and interested in machine learning and using it to study medicine and its automation.

I mainly work on machine learning approaches for the study of human disease by systemically interrogating data from multiple dimensions of life, including phenome, genome, transcriptome, epigenome, exposome, etc. More generally, I have broad interest in various machine learning and data mining techniques, particularly those that are applicable to problems in areas related to medicine and health, such as biology, drug discovery, image analysis and social networks. The overall goal of my research is to improve the precision of medicine and advance its automation.

I am an assistant professor in the Department of Computer Science within the College of Science at the Old Dominion University (ODU), where I direct the ODU Computational Systems Medicine Lab. My research is currently supported by the university and will soon be funded by federal agencies, such as National Institute of Health (NIH) and National Science Foundation (NSF).

Before Joining ODU, I was a PhD student, PostDoc and assistant research professor in the Department of Computer Science & Engineering at the University of Connecticut (UConn). During this time I worked with Dr. Jinbo Bi (Director of UConn Health Informatics Lab), Dr. Kranzler (Professor of Psychiatry, Perelman School of Medicine, University of Pennsylvania) and Dr. Xiuchun Tian (Professor of Biotechnology, UConn) on developing novel machine learning approaches to address computational problems in human medicine and related biology, including: disease subtyping considering simultaneously the phenotypic and genotypic data, outcome prediction with temporal measurements, conservative gene regulatory module identification using transcriptome profile and phenotype imputation leveraging data on genetic variants.

As a master student in the Department of Computer Science & Technology at Nanjing University, I worked on graphical models for developing novel classification methods and applied data mining techniques on database consisting of prescriptions in Chinese traditional medicine (e.g., combinations of medical use herbs).

Selected Publications (check Google Scholar for more)

Multi-view Bi-clustering

Multi-view Bi-clustering

This set of algorithms includes three multi-view bi-clustering methods. All three methods can be used to identify clusters from multi-view data that are with consensus from all views and simultaneously identify features from each view that are associated with the identified clusters. The implementation of all these methods are in both Matlab ard R.

Reference Papers

Matlab Implementation

R Package

Heritable Component Analysis

Heritable Component Analysis

This package includes two methods that identify heritable component of a complex trait (such as substance use disorder) characterized by multiple low-level phenotypes. One method is maximum likelihood based and the other one is restricted maximum likelihood based. The two methods are currently implemented with Matlab.

Reference Papers

Matlab Implementation

C++ Analysis Pipeline

Program Committees
  • HealthInf 2019
  • HealthInf 2018
  • Workshop of Machine Learning and Big Data Research for Disease Classification and Complex Phenotyping at BIBM 2017
  • Workshop of Machine Learning and Big Data Research for Disease Classification and Complex Phenotyping at BIBM 2016
Ad hoc Reviewer
  • Journal Entropy
  • IEEE Transactions on Big Data
  • Journal of Applied Mathematical Modelling
  • Journal of BioMed Research International
  • IEEE/ACM Transactions on Computational Biology and Bioinformatics
  • IEEE Transactions on Neural Networks and Learning Systems
  • Journal Neurocomputing
  • Journal of Neural Computing and Applications
  • Journal of Computers in Biology and Medicine
  • Advances In Neural Information Processing Systems (NIPS)
Grant Reviewer
  • Swiss National Science Foundation, 2017