At Princeton, I get to advise the senior/junior theses of some very bright undergraduate students. If interested, you can find these theses at the Princeton Mudd Library.
2018–2019
- Woramanot Yomjinda, “Money laundering route optimization”.
- Arav Arora, “Supervised learning for optimization of salary cap distribution across player positions in the National Football League”.
- Tor Nitayanont, “Predicting public transportation ridership and locating optimal coordinates for new stations”.
2017–2018
- Tin Nguyen (now PhD student at MIT EECS), "Novel results on computational methods for polynomial optimization".
- Winner of the Proctor and Gamble Prize for best thesis in operations research.
- Winner of the Calvin Dodd MacCracken Senior Thesis Award (awarded by Princeton, SEAS to a thesis that is most distinctive for its inventiveness and technical accomplishment).
- Supervised in collaboration with Bachir El Khadir.
- Deniz Cekirge, "Maximizing safety, minimizing cost: a long-term solution to the Syrian refugee crisis through optimization".
- Winner of the Admiral W. Mack Angas Memorial Prize for significant contributions to society.
- Supervised in collaboration with Cemil Dibek.
- Tianay Ziegler, "How to succeed in basketball without really trying: a support vector machine approach to draft picks and trade deals".
- Supervised in collaboration with Jeff Zhang.
- Haley Wan, "Overcoming barriers to 100% renewable energy: network flow optimization of California's energy transmission grid".
- Supervised in collaboration with Bachir El Khadir.
2016–2017
- Mihaela Curmei, "Monotonically constrained polynomial regression: an application of sum of squares techniques and semidefinite programming".
- Winner of the Proctor and Gamble Prize for best thesis in operations research.
- Supervised in collaboration with Georgina Hall.
- Preprint in preparation.
- Ellie McDonald, "Minimizing, through a mixed integer nonlinear programming problem, the cost of reaching Hawaii's one hundred percent renewable energy goal by 2045".
- Winner of the Sigma Xi Book Award for excellence in research.
- Supervised in collaboration with Georgina Hall.
- Naman Jain, "An application of computer vision methods for diamond classification: color, clarity, and cut".
- Winner of the Admiral W. Mack Memorial Prize for significant contributions to society.
- Ryan Miller, "The outdoor action trip assignment problem".
- Winner of the Joseph Clifton Elgin Prize (awarded by SEAS to a thesis that has done the most to advance the interests of the school and the community at large).
- Winner of the Kenneth H Condit '13 Prize (awarded for academic achievement and impact on the community).
- Supervised in collaboration with Jeffrey Zhang.
2015–2016
- Michael Wattendorf, "Systemic risk in the asymmetric case: theory and experiments with epidemiology using semidefinite programming".
- Winner of the Proctor and Gamble Prize for best thesis in operations research.
- Jacob Eisenberg, "Combating uncertainty with context: optimal lineup selection in daily fantasy baseball".
- Winner of the Proctor and Gamble Prize for best thesis in operations research.
- Max Kaplan, "Subway optimization: New York metro and London underground".
- Salena Hess, "Predicting residential real estate prices and gentrification in Washington, DC using machine learning".
- Supervised in collaboration with Georgina Hall.
2014–2015
- Benjamin Quazzo, "Levels of the game: a statistical and mathematical analysis of ATP Grand Slam competitions from 2005 to 2012".
- Supervised in collaboration with Kush Varshney.
- Data set courtesy of IBM Watson.
- Janie Gu, "The minimum vacation cost problem: a novel generalization of TSP with vertex costs and flexible time windows".
- Rishita Patlolla, "Redistribution of unused pharmaceuticals from hospitals to safety-net clinics in New Jersey".
- Michael Wattendorf, "Predicting points in tennis".
- Supervised in collaboration with Kush Varshney.
- Data set courtesy of IBM Watson.
- Preprint in preparation.
- Jacob Eisenberg, "Determining the boundary of the MLB strike zone: a convex optimization approach".
- Preprint in preparation.