Biostatistics Seminar – Online Multiple Hypothesis Testing

Dr. Ramdas' main theoretical and methodological research interests include selective and simultaneous inference, sequential uncertainty quantification, and distribution-free black-box predictive inference. His areas of applied interest include neuroscience, genetics and... Read more »

Discovering how complex traits are regulated using unsupervised learning

Barbara E. Engelhardt, an associate professor, joined the Princeton Computer Science Department in 2014 from Duke University, where she had been an assistant professor in Biostatistics and Bioinformatics and Statistical Sciences. Her research interests are Machine learning, Bayesian statistics, statistical genetics, computational biology, quantitative genetics and she received an Alfred P. Sloan Research Fellowship, 2016.... Read more »