Vinod Vydiswaran

VG Vinod Vydiswaran, PhD

Associate Professor of Learning Health Sciences
Associate Professor, School of Information
Accepting HILS PhD Students? Yes
Accepting PIBS Students? Yes


V.G.Vinod Vydiswaran is an Associate Professor in the Department of Learning Health Sciences and the School of Information in the University of Michigan. His research interests are primarily in clinical and consumer natural language processing, information trustworthiness, large-scale text mining and analysis, and medical information science. His current research focuses on mining and analyzing health information from multiple sources, including scientific literature, community health forums, and social and information networks., with special interest in analyzing online medical textual information to infer credibility of sources and the claims they make.

Dr. Vydiswaran is a Review Editor of the Journal of the Association for Information Science and Technology (JASIST) and a Guest Editor on the journal supplement on Big Data Analytics for Health of the Biomedical Engineering and Computational Biology (BECB). He received his Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign and Master of Technology from the Indian Institute of Technology Bombay, India.

Vinod Vydiswaran teaches LHS 610: Exploratory Data Analysis for Health and LHS 712: Natural Language Processing on Health Data. 

Areas of Interest

Research and scholarly interests: clinical and consumer natural language processing, information trustworthiness, text and data mining, information retrieval, medical information science

Subject-matter expertise: natural language processing, text mining, information retrieval, machine learning

Other professional highlights: 

  • Distinguished Paper Award, at the American Medical Informatics Association (AMIA) Annual Symposium, 2014.
  • Best Paper Award, at the Eleventh International Conference on Management of Data (COMAD), 2005.
  • Outstanding Teaching Assistant Award for Fall 2011, awarded by the Department of Computer Science, University of Illinois.
  • Co-inventor of US Patent US8239387 on Structural clustering and template identification for electronic documents, 2012.
  • Co-inventor of US Patent US8046681 on Techniques for inducing high quality structural templates for electronic documents, 2011.
  • Co-inventor of US Patent US7668942 on Generating document templates that are robust to structural variations, 2010.
  • Co-organized three international workshops: (a) HealthIQ 2015, the first workshop on Health Information Quality, Dallas, TX, USA (2015); (b) TextGraphs-9; the ninth workshop on Graph-based methods for Natural Language Processing, Doha, Qatar (2014), and (c) TextGraphs-10; the tenth workshop on Graph-based methods for Natural Language Processing, San Diego, CA (2016)
  • Reviewer for numerous professional journals and conferences, including JAMIA, AMIA, TKDE, IPM, TAC, JNLE, TOIS, WWW, ECIR, ACL, AAAI, CIKM.

Published Articles or Reviews

  • Zheng K, Vydiswaran VGV, Liu Y, Wang Y, Stubbs A, Uzuner Ö, Gururaj AE, Bayer S, Aberdeen J, Rumshisky A, Pakhomov S, Liu H, Xu H. Ease of adoption of clinical natural language processing software: An evaluation of five systems. J Biomed Inform. 2015 Dec;58 Suppl:S189-96.
  • Vydiswaran VGV, Zhai CX, Roth D, Pirolli P. Overcoming bias to learn about controversial topics. Journal of the American Society for Information Science and Technology (JASIST), 2015;66(8):1655–72. DOI: 10.1002/asi.23274
  • Vydiswaran VGV, Mei Q, Hanauer DA, Zheng K. Mining consumer health vocabulary from community-generated text. Proc. AMIA Annual Symposium, 2014. pp. 1150–9.
  • Vydiswaran VGV, Liu Y, Zheng K, Hanauer DA, Mei Q. User- created groups in health forums: What makes them special? Proc. 8th International AAAI Conference on Weblogs and Social Media (ICWSM), 2014. pp. 515–24.
  • Sondhi P, Vydiswaran VGV, Zhai CX. Reliability prediction of webpages in the medical domain. Proc. 34th European Conference on Information Retrieval (ECIR), 2012. pp. 219–31.
  • Vydiswaran VGV, Zhai CX, Roth D. Content-driven trust propagation framework. Proc. 17th SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2011. pp. 974–82.