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Dian Hu, PhD

Assistant Professor

Department of Clinical and Health Informatics


Dian Hu, PhD is an Assistant Professor at McWilliams School of Biomedical Informatics. He participates in research at the Center for Translational AI Excellence and Applications in Medicine (TEAM-AI), led by Dr. Hongfang Liu. Before joining UTHealth Houston in 2023, Dian was a research fellow at Mayo ADVANCE Lab, an interdisciplinary lab led by Dr. Hongfang Liu in the Department of Artificial Intelligence and Informatics at Mayo Clinic. Dian's primary research interest is to discover, analyze and validate socioeconomic factors behind public health issues using multifarious and non-traditional data sources.

Dian holds a PhD in Systems Engineering from The George Washington University, advised by Dr. David A. Broniatowski. During the COVID-19 pandemic, Dian served as an ORISE Fellow at U.S. Food and Drug Administration while pursuing his Ph.D.

Dian believes that the burden of solving 21st-century public health issues cannot be carried solely by health professionals. On the contrary, entire society should (1) actively identify vulnerable populations and emerging health problems, (2) formulate pragmatic and focused health programs, and (3) monitor, evaluate and modify these programs in a data-driven, democratic and ethical manner.

  • Tell us about your research center and/or what research you are currently working on?
    The Center for Translational AI Excellence and Applications in Medicine (TEAM-AI) focuses on advancing translational sciences in health data science, informatics, and artificial intelligence for medicine and healthcare. The center aims to accelerate the digital transformation of medicine and healthcare through methodology innovation and team science collaboration. Dr. Hu’s current research is to establish innovative models and methodologies aiming to evaluate various health initiatives and programs in an interactive fashion.
  • What type of student or Postdoctoral Fellow are you looking for to work in your center?
    Dr. Hu is looking for student or Postdoctoral Fellow who is determined to make a niche but significant breakthrough in health informatics in one of these directions: (1) to improve the cost-effective or certain aspect of performance of an existing state-of-art algorithm that is relevant to health. (2) to design, test and validate a well-known literature motivated health-related hypothesis using innovative data and methodology. (3) to make and test a prototype of toolset with the potential to solve a significant and challenging medical question.
  • What does the future of your research look like?
    I wish my research will provide a comprehensive set of tools, widgets and baselines that will make policy and programs in public health more truthful, transparent and traceable (TTT).
  • What does the future of informatics look like?
    I believe informatics would protect fairness and create happiness for individuals and communities.


  • PhD, Systems Engineering, The George Washington University, Washington, DC, 2022
  • BS, Systems Engineering, The George Washington University, Washington, DC, 2013

Areas of Expertise

  • Public Health Program Evaluation
  • Big Data and Surveillance
  • Systems Architecture and Health Policy
  • Quasi-experimental Design in social media

Staff Support

Blanca Torres | 713-486-0114

Selected Publications

  1. Hu D, Liu CM, Hamdy R, et al. Questioning the Yelp Effect: Mixed Methods Analysis of Web-Based Reviews of Urgent Cares. J Med Internet Res. 2021;23(10):e29406. Published 2021 Oct 8. doi:10.2196/29406
  2. Hu D, Martin C, Dredze M, Broniatowski DA. Chinese social media suggest decreased vaccine acceptance in China: An observational study on Weibo following the 2018 Changchun Changsheng vaccine incident. Vaccine. 2020;38(13):2764-2770. doi:10.1016/j.vaccine.2020.02.027
  3. Lama Y, Hu D, Jamison A, Quinn SC, Broniatowski DA. Characterizing Trends in Human Papillomavirus Vaccine Discourse on Reddit (2007-2015): An Observational Study. JMIR Public Health Surveill. 2019;5(1):e12480. Published 2019 Mar 27. doi:10.2196/12480
  4. Hu D, Broniatowski DA. Measuring Perceived Causal Relationships Between Narrative Events with a Crowdsourcing Application on Mturk. In: Lecture Notes in Computer Science. ; 2017. doi:10.1007/978-3-319-60240-0_43