Jack Dunn

Co-founder of Interpretable AI
PhD, Operations Research Center, Massachusetts Institute of Technology

Contact: jack.dunn.nz at gmail, Github, LinkedIn

About Me

I am a co-founding partner of Interpretable AI, which delivers interpretable methods and solutions for machine learning and artificial intelligence. I obtained my PhD 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, valuing both performance and interpretability.

Alongside my research, I am a contributor to various open-source projects. I was 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 have also contributed 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.

PhD Thesis
  • "Optimal Trees for Prediction and Prescription". (link) (preprint)
Journal Articles
  • D. Bertsimas, J. Dunn. "Optimal Classification Trees". Machine Learning, 2017. (link) (preprint)
    • Winner of the MIT ORC Best Student Paper award, 2016
  • D. Bertsimas, J. Dunn, C. Pawlowski, Y. Zhuo. "Robust Classification". INFORMS Journal on Optimization, 2018. (link)
  • D. Bertsimas, J. Dunn, G. Velmahos, H. Kaafarani. Surgical Risk is Not Linear: Derivation and Validation of a Novel, User-Friendly, and Machine-Learning-based Predictive, OpTimal-Trees-in-Emergency-Surgery, Risk (POTTER) Calculator. Annals of Surgery, 2018. (link)
  • D. Bertsimas, J. Dunn, C. Pawlowski, J. Silberholz, A. Weinstein, Y. Zhuo, E. Chen, A. Elfiky. An Applied Informatics Decision Support Tool for Mortality Predictions in Cancer Patients. JCO Clinical Cancer Informatics, 2018. (link)
  • D. Bertsimas, J. Dunn, N. Mundru. Optimal Prescriptive Trees. INFORMS Journal on Optimization, 2019. (link) (preprint)
  • D. Bertsimas, J. Dunn, E. Gibson, A. Orfanoudaki. Optimal Survival Trees. Under review.
  • D. Bertsimas, J. Dunn, T. Trikalinos, Y. Wang. Improved Triaging of Diagnostic Computed Tomography for Children After Head Trauma. JAMA Pediatrics. 2019. (link)
  • D. Bertsimas, J. Dunn, M. Li, Y. Zhuo, C. Estrada, C. Nelson, H. Wang. Targeted workup after initial febrile Urinary Tract Infection: Using a novel machine learning model to identify children most likely to benefit from VCUG. Journal of Urology. 2019. (link)
  • "Optimal Classification and Regression Trees."INFORMS, Oct 2019