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.
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Guest Editor, IEEE Security and Privacy Magazine: Special Issue on Genomic Privacy
Associate Editor, ACM Transactions on Information and System Security
Editorial Board, Journal of the American Medical Informatics Association
Editorial Board, Learning Health Systems
Editorial Board, Methods of Information in Medicine
Editorial Board, Transactions on Data Privacy
Scientific Program Committee (Upcoming)
Named a Chancellor Faculty Fellow for 2016-2018
... on game theory and identifiability (3/2015)
... on cryptography for genomics in Nature Medicine (6/2014)
... on my work with the Office for Civil Rights at the U.S. Department of Health and Human Services (12/2012)
... at the PCORI Annual Meeting (10/8/2015)
... at Research Triangle International (12/10/2015)
... at Stanford University (3/11/2016)
"Assessing Data Intrusion Threats"
... IEEE Journal of Biomedical and Health Informatics:
Patient Stratification Using Electronic Health Records from a Chronic Disease Management Program
... Journal of Medical Internet Research:
A Scalable Framework to Detect Personal Health Mentions in Social Media