Interests


Topics: Machine Learning, Causal Inference, Fairness and Interpretability

Methods: Discrete Optimization, Stochastic Optimization, Monte Carlo Methods

Application Areas: Healthcare, Criminal Justice, Credit Scoring, Revenue Management


Recent Papers


Alexander Spangher, Berk Ustun

Workshop on Fairness, Accountability and Transparency (FAT/ML), 2018.

Hao Wang, Berk Ustun, and Flavio du Pin Calmon

IEEE International Symposium on Information Theory (ISIT), 2018.


Machine Learning Methods


Berk Ustun and Cynthia Rudin

Proceedings of Knowledge Discovery and Data Mining (KDD), 2017.

Berk Ustun and Cynthia Rudin

Machine Learning, 2015.

Berk Ustun and Cynthia Rudin

Technical Report, 2015.

Berk Ustun, Stefano Tracà and Cynthia Rudin

Proceedings of AAAI Late Breaking Track, 2013.

Panos Parpas, Berk Ustun, Mort Webster and Quang Kha Tran

INFORMS Journal of Computing, 2015.


Machine Learning Applications


Berk Ustun, Lenard Adler, Cynthia Rudin, Stephen Faraone, Thomas Spencer, Patricia Berglund, Michael Gruber, Ronald Kessler

JAMA Psychiatry, 2017.

Aaron Struck, Berk Ustun, Andres Rodriguez Ruiz, Jong Woo Lee, Suzette LaRoche, Lawrence J. Hirsch, Emily J. Gilmore, Jan Vlachy,
Hiba Arif Haider, Cynthia Rudin, and M. Brandon Westover

JAMA Neurology, 2017.

Jiaming Zeng, Berk Ustun and Cynthia Rudin

Journal of the Royal Statistical Society Series A, 2016.

Berk Ustun, M. Brandon Westover, Cynthia Rudin and Matt Bianchi

Journal of Clinical Sleep Medicine, 2015.


Theses


Berk Ustun

PhD thesis. Massachusetts Institute of Technology, 2017.

Berk Ustun

Master’s thesis. Massachusetts Institute of Technology, 2012.