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Stephen Wu, PhD

Associate Professor

Stephen Wu, PhD joined UTHealth as an Associate Professor in August of 2018, after holding faculty positions at Mayo Clinic, Oregon Health & Science University (OHSU), and Addis Ababa University, and serving in a Scientist role for the tech startup Trapit, Inc.

Dr. Wu’s research interests lie in the areas of Natural Language Processing (NLP) and Information Retrieval (IR), especially in the domain of medical language. He was the Co-PI of 2 NIH-funded research grants on these topics, and has authored over 40 peer-reviewed manuscripts. He is currently exploring how to represent meaning alongside grammar, how social networks influence textual behavior, and deep learning-based methods for these tasks. Dr. Wu is also interested in AI Ethics, particularly in non-Western settings. He has helped start a North African non-profit organization, Tibyan (“Clarifying AI”), for local research and training in AI.

Dr. Wu has taught and mentored at the graduate and undergraduate level, designing curriculum on Analyzing Sequences, NLP, and Social Network Analysis. He has developed a unique, principles-oriented approach to technical education (techied), and has adapted this philosophy to international and multicultural teaching settings.

“Medicine is a complex, multi-stakeholder environment run by real people. Imperfection is unsurprising, yet progress is expected,” says Wu. “Thus the frontier of science and medicine increasingly turns towards technology for its benefit and improvement. But simultaneously, the technological research enterprise increasingly realizes that it is itself fundamentally human, and therefore reflective of the strengths, liabilities, and biases of its people. So we live in a historic time in which humanity must walk the fine line between ignorant optimism on one side, and alarmist criticality on the other, to achieve a responsibly technology-enabled future for human health.”

Staff Support

 Leticia Flores
Phone: 713-500-3912


  • PhD, Computer Science, 2010, University of Minnesota
  • MS, Electrical Engineering, 2006, University of Minnesota
  • BSE, Electrical Engineering and Biomedical Engineering, 2004, Duke University

Areas of Expertise

  • Natural Language Processing
  • Information Retrieval
  • AI Ethics


  1. Wu, Stephen, Kirk Roberts, Surabhi Datta, Jingcheng Du, Zongcheng Ji, Yuqi Si, Sarvesh Soni, Qiong Wang, Qiang Wei, Yang Xiang, Bo Zhao, Hua Xu. Deep Learning in Clinical Natural Language Processing: A Methodical Review. J Am Med Inform Assoc. Volume 27, Issue 3, March 2020, Pages 457–470.
  2. Wu, Stephen, Sijia Liu, Sunghwan Sohn, Sungrim Moon, Chung-il Wi, Young Juhn, Hongfang Liu. Modeling Asynchronous Event Sequences with RNNs. J Biomed Inform, 83:167-77. Jul 2018.
  3. Wu, Stephen, Andrew Wen, Yanshan Wang, Sijia Liu, and Hongfang Liu. Aligned-layer text search in clinical notes. Proceedings of the 16th World Congress on Medical and Health Informatics (MedInfo 2017). Hangzhou, China. 2017.
  4. Wu, Stephen T, Sijia Liu, Yanshan Wang, Tamara Timmons, Harsha Uppili, Steven Bedrick, William Hersh, and Hongfang Liu. Intra-institutional EHR Collections for Patient-Level Information Retrieval. Journal of the American Society for Information Science and Technology. doi:10.1002/asi.23884. September 2017.
  5. Wu, Stephen T, Timothy Miller, James Masanz, Matt Coarr, Scott Halgrim, David Carrell, Cheryl Clark. Negation's not solved: Generalizability versus Optimizability in clinical natural language processing.  PLoS One. Nov 2014. [NLP Best Paper, 2015 IMIA Yearbook]
  6. Wu, Stephen, Hongfang Liu, Dingcheng Li, Cui Tao, Mark Musen, Christopher Chute, and Nigam Shah.  UMLS Term Occurrences in Clinical Notes: A Large-scale Corpus AnalysisJ Am Med Inform Assoc.  Published online first: 4 April, 2012.  doi:10.1136/amiajnl-2011-000744