I am a fourth-year doctoral candidate at the Operations Research Center at MIT advised by Dimitris Bertsimas. My primary research interest is the intersection of modern optimization with problems of statistics and machine learning, with the goal of developing new methods that significantly improve upon the state of the art.
Other interests include:
- robust and data-driven optimization,
- data analytics,
- decision-making under uncertainty.
Alongside my research, I am a contributor to various open-source projects. I am the lead developer of the OpenSolver add-in, which enables open-source optimization within Microsoft Excel (230,000+ downloads) and Google Sheets (13,000 weekly active users). I also contribute to the JuliaOpt suite of packages that enable optimization in the Julia programming language.
I have previously worked as an intern at Google, and I obtained my undergraduate degree (B.E. (Hons)) in Engineering Science from the University of Auckland in New Zealand.
- D. Bertsimas, J. Dunn. "Optimal Classification Trees". Machine Learning, 2017. (DOI) (preprint)
- Winner of the MIT ORC Best Student Paper award, 2016
- D. Bertsimas, J. Dunn, C. Pawlowski, Y. Zhuo. "Robust Classification". Submitted, 2015.
- "Machine Learning in Surgery and Cancer." MIT Sloan: Innovating Health Systems, Digital Health Transformations, Nov 2017
- "Estimating Risk Of Morbity And Mortality After Emergency Surgery With Machine Learning." INFORMS, Nov 2017
- "Optimal Regression Trees." INFORMS, Nov 2017
- "Personalized Medicine for Traumatic Brain Injury." INFORMS Healthcare, July 2017
- "Optimal Classification Trees." University of Auckland, Dec 2016
- "Optimal Classification Trees." INFORMS, Nov 2016
- "Optimal Trees." MIT ORC Seminar Series, Sep 2016
- "Optimal Trees and Robust Classification." University of Auckland, Jan 2016
- "Optimal Trees." INFORMS, Nov 2015