Bradley Malin, Ph.D.
I have broad research interests in data mining, 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)
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)
... on game theory and identifiability (3/2015)
... on cryptography for genomics in Nature Medicine (6/2014)
... at University of Cambridge (12/7/2016)
... at the Experiences and Methods in Clinical Trials Data Sharing Symposium (10/24/2016)
... at University of Texas at Dallas (9/30/2016)
"Assessing Data Intrusion Threats"
... IEEE Journal of Biomedical and Health Informatics:
Patient Stratification Using Electronic Health Records from a Chronic Disease Management Program
... IEEE Transactions on Knowledge and Data Engineering:
Scalable Iterative Classification for Sanitizing Large-Scale Datasets
... Journal of the American Medical Informatics Association:
Identifying Collaborative Care Teams through Electronic Medical Record Utilization Patterns