My research bridges the gap between prediction and decision-making in machine learning by developing principled statistical methods. The goal is to ensure that as black-box models become more widespread, they remain reliable and trustworthy in real-world applications.
Snigdha Panigrahi
Associate Professor of Statistics, College of Literature, Science, and the Arts and Associate Professor of Biostatistics, School of Public Health
Turning predictive models into interpretable, trustworthy decision tools
COntact
Location
Ann Arbor
Methodologies
Data Integration / Data Mining / Deep Learning / Generative AI / Machine Learning / Mathematical and Statistical Modeling / Optimization / Statistics
Applications
Biological Sciences / Healthcare Research / Policy and Law
Community Affiliation