Stephan Rabanser

I'm Stephan, a Postdoctoral Research Associate at Princeton University working with Prof. Arvind Narayanan and Prof. Matthew Salganik at the Center for Information Technology Policy (CITP). Previously, I was a PhD student in Computer Science at the University of Toronto and the Vector Institute working with Prof. Nicolas Papernot.

I work on advancing reliable and trustworthy machine learning. Most notably, I work on uncertainty quantification, selective prediction, and out-of-distribution generalization/robustness. In my past research I have worked on out-of-distribution and selective classification methods, reliability of time series representations, time series anomaly detection, distribution shift detection/characterization, robustness in federated learning, examining the intersection of uncertainty quantification and differential privacy, providing tighter bounds on selective classification performance, designing suitability filters to detect malignant distribution shifts, introducing a confidence tuning method for improved cascading/deferral from small to big models, and studying adversarial use of uncertainty.

Over the past years, I have interned a few times at Amazon / AWS AI Labs as well as Google. I was also research visitor at MIT, CMU, and the University of Cambridge.