Aaditya Ramdas

 

Aaditya Ramdas
Tenure-track Assistant Professor
Department of Statistics and Data Science
(affl.) Machine Learning Department
Carnegie Mellon University

Contact

132H Baker Hall
Carnegie Mellon University
Pittsburgh, PA 15232, USA

aramdas AT cmu FULLSTOP edu
http://www.stat.cmu.edu/~aramdas

Research

My research spans theory and algorithms in machine learning and statistical inference, with applications to science and technology. One line of recent work focuses on the theme of reproducibility in science and technology (multiple hypothesis testing, adaptive data analysis, selective inference), by designing new algorithms for controlling false discoveries in novel static and dynamic settings. Another line of work involves active sequential experimentation (interactive learning, multi-armed bandits, martingale concentration, confidence sequences), by designing new algorithms that work in online or streaming data settings. I have also worked on problems in convex optimization and nonparametric statistics.

Keywords: scientific reproducibility, false discovery rate, adaptive data analysis, selective inference, hypothesis testing, online experimentation, interactive learning, convex optimization, multi-armed bandits, sequential analysis, nonparametric inference.

Biography

Aaditya Ramdas is an assistant professor in the Department of Statistics and Data Science and the Machine Learning Department at Carnegie Mellon University. Previously, he was a postdoctoral researcher in Statistics and EECS at UC Berkeley from 2015-18, mentored by Michael Jordan and Martin Wainwright. He finished his PhD at CMU in Statistics and Machine Learning, advised by Larry Wasserman and Aarti Singh, winning the Best Thesis Award. His undergraduate degree was in Computer Science from IIT Bombay. A lot of his research focuses on modern aspects of reproducibility in science and technology, involving statistical testing and false discovery rate control in static and dynamic settings. He also works on some problems in sequential decision-making and online uncertainty quantification.

Curriculum Vitae