Computer Science PhD student at NYU. Previously, mathematics and economics student at Pomona College. Experience with a professional sports team, sports betting, a Silicon Valley venture fund and a world class research laboratory. Currently interested in machine learning, particularly for applications in interpretability, visualization and sports.
I'm currently looking for internships and consulting work in sports, tech and finance, with a particular focus on developing and deploying machine learning models.
Experience with a professional sports team, a Silicon Valley venture fund and the world's largest supercomputer
I'm currently a second-year PhD student at NYU, working with Professor Claudio Silva as part of the VIDA group. My main research interests include interpretability of machine learning models, visualization and sports analytics.
Before NYU, I was a double major in mathematics and economics at Pomona College, where I graduated in May 2018. I wrote my mathematics thesis on an original feature selection algorithm and my economics senior exercise on the relationship between drug overdose death rates and economic conditions. My advisor was Professor Pierangelo De Pace. These were some of the courses I took:
I also was part of the student investment club, where I led the healthcare and technology group as a Sophomore; Pomona Ventures, where I helped student run startups expand and pitch to investors, and in particular advised Social Cipher, which won the 2018 Sage Tank competition; Pomona Sports Analytics Club, which I founded my junior year to provide an outlet for sports statistics research at the Claremont Colleges.
At Big League Advance, I was a data scientist intern working on engineering and deploying complex pipelines to support sports betting operations. In particular, I worked on projection systems, data ingestion, data scraping and a full end-to-end modeling process for American football. To date, said American football model has been profitable, providing tremendous value for Big League Advance.
For two summers, I worked as a quantitative analyst associate in the baseball research & development group at the Philadelphia Phillies. I worked on a variety of projects, such as writing reports for front office staff and coaches, to developing and integrating machine learning models into the Phillies' team operations. In particular, I took the initiative to lead over 5 projects that pushed baseball analytics boundaries, using data sources such as text and audio.
For the spring semester of my junior year, I was selected to partake in Claremont McKenna College's Silicon Valley Program. As part of the program, I worked full time, while taking a full course load on the weekend. Specifically, I worked with CrunchFund, now known as Tuesday Capital, where I met with over 30 prospective startups, alongside partners, principals and associates. I wrote over 20 reports that guided the investment of millions of dollars. Additional duties included meeting with founders, reviewing decks, conducting diligence and supporting portfolio company operations.
At Oak Ridge National Laboratory, I was part of the Advanced Data and Workflows Group, where I produced two research papers, as a sophomore, one of which I first authored:
J. Harney, S.H. Lim, S. Sukumar, D. Stansberry, P. Xenopoulos, "On-Demand Data Analytics in HPC Environments at Leadership Computing Facilities: Challenges and Experiences", 2016 IEEE International Conference on Big Data [December 2016]
P. Xenopoulos, J. Daniel, M. Matheson, S. Sukumar, "Big Data Analytics on HPC Architectures: Performance and Cost", 3rd Workshop on Advances in Software and Hardware for Big Data to Knowledge Discovery (ASH), part of 2016 IEEE International Conference on Big Data [December 2016]
At the Company Lab, a non-profit startup accelerator in Chattanooga, TN, I worked closely with participating startups to refine their strategy and investment pitch decks. The startups that I worked with in the Summer 2015 class ultimately raised over a million dollars in local and national funding.
My current research interests revolve around machine learning interpretability, data visualization and sports analytics
On-Demand Data Analytics in HPC Environments at Leadership Computing Facilities: Challenges and Experiences
J. Harney, S.H. Lim, S. Sukumar, D. Stansberry, P. Xenopoulos, 2016 IEEE International Conference on Big Data
Big Data Analytics on HPC Architectures: Performance and Cost
P. Xenopoulos, J. Daniel, M. Matheson, S. Sukumar, 2016 IEEE International Conference on Big Data
Who Will Win? Win Probability Models for Counter-Strike: Global Offensive
N. Latshaw and P. Xenopoulos
What's He Throwing? Deep Neural Networks for Baseball Pitch Classification
P. Xenopoulos and M. Mandic
Data visualization and mining methods for: (1) soccer, using event data, (2) Counter-Strike: Global Offensive, developing an open-source and easy to use game-parser and (3) American football, using tracking data provided by the NFL
Developing TCAV-esque methods for model interpretability for text and audio classification tasks, understanding the effect of class imbalance on interpretability methods
This is a collection of data sets I have amassed and cleaned over the years