TITLE: Reliable Tools for Data Exploration in Healthcare
Dr. Doshi-Velez will talk about some work she’s doing to understand non-identifiability in the context of non-negative matrix factorization (NMF). NMF is a popular tool to do data exploration, and recently she has been doing work to see how robust it is: that is, can there sometimes be two very different explanations for the same data? And if so, how can researchers expose them so that clinical scientists can use them to generate the next study? She will start with some concrete examples, talk about some underlying math, and touch on what students might want to study if they want to do machine learning/data science in the future.
“Finale Doshi-Velez is excited about methods to turn data into actionable knowledge. Her core research in machine learning, computational statistics, and data science is inspired by—and often applied to—the objective of accelerating scientific progress and practical impact in healthcare and other domains. Specifically, she is interested in questions such as: How can we design robust, principled models to combine complex data sets with other knowledge sources? How can we design models that summarize and generate hypotheses from such data? How can we characterize the uncertainty in large, heterogeneous data to provide better support for decisions? Finale Doshi-Velez is interested in developing the probabilistic methods to address these questions. Prior to joining SEAS, Finale Doshi-Velez was an NSF CI-TRaCS Postdoctoral Fellow at the Center for Biomedical Informatics at Harvard Medical School. She was a Marshall Scholar at Trinity College, Cambridge from 2007-2009, and she was named one of IEEE’s “AI Top 10 to Watch” in 2013.”
https://www.seas.harvard.edu/directory/finale
Finale graduated from GSGIS in 2001 and earned her PhD from MIT in 2012. She is currently an Assistant Professor of Computer Science at Harvard’s School of Engineering and Applied Sciences.
