Bradley Malin, Ph.D.
I have broad research interests in data science, management, and trustworthy computing. I believe that the analysis of large quantities of health and molecular information has the potential to refine phenotype definitions into significantly more nuanced models, as well as novel clinical concepts, which associate with differential response to interventions. My research has demonstrated that we can make rapid progress in this direction by integrating novel computing infrastructures with statistically-driven methods to learn patterns and test predictive models. However, to maximize the potential for data science in clinical investigations, we must make data available on a broad scale without violating the rights of the people to whom it corresponds. As such, a great deal of my research focuses on the development of multidisciplinary approaches to privacy preservation that draw upon methods from computer science, biomedical knowledge modeling, policy analysis, and economics.
Check out the people on the privacy lab's online home.
Conference Program Commitees (Upcoming)
... Appointed to the Technical Anonymisation Group of the European Medicines Agency (7/12/2017)
... Named a Chancellor Faculty Fellow for 2016-2018
... on our new Center of Excellence in Ethics Research (on Genomics and Data Privacy) (5/2016)
... on the new Big Biomedical Data Science Ph.D. Program (4/2016)
... on our involvment in the Precision Medicine Initiative pilot (2/2016)
... for the U.S. Commission on Evidence-based Policymaking (2/24/2017)
... at University of Cambridge (12/7/2016)
"Assessing Data Intrusion Threats"
... American Journal of Human Genetics:
Expanding Access to Large-Scale Genomic Data While Promoting Privacy: A Game Theoretic Approach
... AAAI Conference on Weblogs and Social Media:
The Power of the Patient Voice: Learning Indicators of Hormonal Therapy Adherence From an Online Breast Cancer Forum
... Journal of the American Medical Informatics Association:
Identifying Collaborative Care Teams through Electronic Medical Record Utilization Patterns