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 »

Biostatistics Seminar: Reframing Proportional-Hazards Modeling for Large Time-to-Event Datasets with Applications to Deep Learning

We are welcoming Dr. Simon, who will talk about "Reframing proportional-hazards modeling for large time-to-event datasets with applications to deep learning." Dr. Simon works on machine learning (including penalized regression and classification), efficient algorithms in high dimensional spaces, shrinkage estimation and clinical trial design. The techniques he develops are directed at problems in biology and... Read more »