Join Michelle Nixon, Assistant Research Professor with the Silverman Lab in the College of IST at Penn State, for her upcoming research seminar. This lecture is free and open to the Penn State community.

"Statistical Methods for Biomedical Informatics"

Increasingly, researchers are turning to statistics and machine learning methods to help improve clinical outcomes and make sense of complex data. Yet traditional statistical methods are often ill-equipped to handle these new settings. In this talk, I will discuss three common problems in biomedical informatics: (1) data privacy (2) sequence counting (e.g., microbiome sequence or RNA-seq) and (3) clinical decision support tools. With respect to data privacy, I will discuss the current state of the art for statistical data privacy and several methods I developed that aim to balance the competing concern between scientific advancement and safeguarding of personal information. With respect to sequence counting, I will present recent work using Bayesian partially identified models to overcome compositional limitations that are common to many other methods in the literature. With respect to clinical decision support tools, I will discuss recent work using pool-based active learning to create personalized bacteremia risk models using partially-labeled data as bacteremia (bacteria present in the blood) has an imperfect diagnosis process due to possible contaminants.

About the Speaker

Michelle Nixon is an Assistant Research Professor with the Silverman Lab in the College of IST at Penn State. Her research interests span across all of applied statistics, including statistical data privacy and applications to social science and biomedical data. She is a 2019 recipient of a Dissertation Fellowship from the U.S. Census Bureau. She received her Ph.D. in Statistics and Social Data Analytics (2020) and B.S. in Statistics and Economics (2015) at Penn State.