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Postdoctoral Research Fellow in Biomedical Informatics, Vanderbilt University

Zhiyu Wan is a postdoctoral research fellow in the Department of Biomedical Informatics at Vanderbilt University Medical Center. He is mentored by Dr. Bradley A. Malin, and is currently working in the Health Information Privacy Laboratory (HIPLAB) and the Center for Genetic Privacy and Identity in Community Settings (GetPreCiSe) at Vanderbilt University.

He holds a PhD in Computer Science from Vanderbilt University. He obtained his bachelor’s degree in Automation from Xi’an Jiaotong University.

His research interests include: optimizing privacy-preserving data sharing, with an emphasis on health and genomic data; leveraging game theory, artificial intelligence, and machine learning techniques to improve social good; and analyzing social networks and network security.


  • 2021–present
    Postdoctoral Research Fellow, Vanderbilt University Medical Center
  • 2013–2020
    Research Assistant, Vanderbilt University
  • 2012–2013
    Teaching Assistant, Vanderbilt University


  • 2021
    Enabling Realistic Health Data Re-identification Risk Assessment Through Adversarial Modeling, Journal of the American Medical Informatics Association
  • 2020
    Game Theory for Privacy-Preserving Sharing of Genomic Data, Responsible Genomic Data Sharing: Challenges and Approaches
  • 2019
    Biomedical Research Cohort Membership Disclosure on Social Media, Proceedings of the 2019 American Medical Informatics Association Annual Fall Symposium
  • 2018
    Detecting the Presence of an Individual in Phenotypic Summary Data, Proceedings of the 2018 American Medical Informatics Association Annual Fall Symposium
  • 2018
    It’s all in the timing: Calibrating temporal penalties for biomedical data sharing, Journal of the American Medical Informatics Association
  • 2017
    Controlling the Signal: Practical Privacy Protection of Genomic Data Sharing In Beacon Services, BMC Medical Genomics
  • 2017
    Expanding Access to Large-Scale Genomic Data While Promoting Privacy: A Game Theoretic Approach, The American Journal of Human Genetics
  • 2017
    An Open Source Tool for Game Theoretic Health Data De-identification, Proceedings of the 2017 American Medical Informatics Association Annual Fall Symposium
  • 2015
    A Game Theoretic Framework for Analyzing Re-Identification Risk, PLoS ONE
  • 2015
    Process-Driven Data Privacy, Proceedings of the 24th ACM International on Conference on Information and Knowledge Management
  • 2010
    Honeynet-based collaborative defense using improved highly predictive blacklisting algorithm, Proceedings of 8th World Congress on Intelligent Control and Automation