I will be discussing my recent work on personalization in federated learning. Federated learning is a powerful distributed optimization framework in which multiple clients collaboratively…
In observational cohort studies with complex sampling schemes, truncation arises when the time to event of interest is observed only when it falls below or…
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Abstract: Linear bandits and contextual bandits are online learning problems that have a wide range of applications, including recommendation systems, healthcare and many others. For…
Multilayer networks continue to gain significant attention in many areas of study, particularly, due to their high utility in modeling interdependent systems such as critical…
Personal tracking through digital technologies like pedometers, mood monitoring apps, and food journaling apps has great potential to help people begin to change their behaviors,…
Abstract Models for clinical risk prediction are important tools for clinicians to understand the risks of adverse event decision-making, stratify patient populations, and enable shared…
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Many of us foresee of a future in which AR eyeglasses are worn all day, replacing, and enhancing of our current prescription eyewear. That future…
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Estimating large covariance/precision matrices are fundamental problems in modern multivariate statistics. Virtually all of the existing methods in this literature assume independent samples. In the…
When conducting analysis of electronic health records (EHR), oftentimes the data utilized is patient level data which readily allows for statistical analyses that properly adjust…