Tony Duan

I am currently a resident at Microsoft Research AI, interested in probabilistic machine learning. Previously, I completed my MS in CS at Stanford and BS in EECS at UC Berkeley.


Machine Learning

Randomized Smoothing of all Shapes and Sizes
Greg Yang*, Tony Duan*, J. Edward Hu, Hadi Salman, Ilya Razenshteyn, Jerry Li.
Preprint, 2020 [Paper] [Code]

NGBoost: Natural Gradient Boosting for Probabilistic Regression
Tony Duan*, Anand Avati*, Daisy Yi Ding, Khanh K. Thai, Sanjay Basu, Andrew Y. Ng, Alejandro Schuler.
Preprint, 2019 [Paper] [Code]

Countdown Regression: Sharp and Calibrated Survival Predictions
Anand Avati, Tony Duan, Sharon Zhou, Kenneth Jung, Nigam Shah, Andrew Y. Ng.
Uncertainty in Artificial Intelligence (UAI), 2019 [Paper] [Code]

Counterfactual Reasoning for Fair Clinical Risk Prediction
Stephen Pfohl, Tony Duan, Daisy Yi Ding, Nigam Shah.
Machine Learning in Healthcare (MLHC), 2019 [Paper]


Clinical value of predicting individual treatment effects for intensive blood pressure therapy
Tony Duan, Pranav Rajpurkar, Dillon Laird, Andrew Y. Ng, Sanjay Basu.
Circulation: CQO, 2019 [Paper] [Code]

Deep learning for chest radiograph diagnosis
Pranav Rajpurkar*, Jeremy Irvin*, Robyn L. Ball, Kaylie Zhu, Brandon Yang, Hershel Mehta, Tony Duan, Daisy Ding, Aarti Bagul, Curtis P. Langlotz, Bhavik N. Patel, Kristen W. Yeom, Katie Shpanskaya, Francis G. Blankenberg, Jayne Seekins, Timothy J. Amrhein, David A. Mong, Safwan S. Halabi, Evan J. Zucker, Andrew Y. Ng, Matthew P. Lungren.
PLOS Medicine, 2018 [Paper] [Press]


I have had the opportunities to TA the following courses:

CS 229 (Machine Learning) at Stanford: Fall 2018.
CS 221 (Artificial Intelligence) at Stanford: Fall 2017.
CS 170 (Algorithms) at UC Berkeley: Fall 2016 and Spring 2017.

* Equal contribution.