Scholar
Alec Koppel
Google Scholar ID: 8ClxyjIAAAAJ
Research Lead, JP Morgan AI Research
Citations & Impact
Citations
2,316
H-index
23
i10-index
43
Publications
20
Co-authors
131
list available
Publications
Resume (English only)
Research Experience
- AI Research Lead, Multiagent Learning and Simulation Group, Artificial Intelligence Research, JP Morgan Chase & Co.
- Former Research Scientist at Amazon, Optimal Sourcing Systems (OSS) within Supply Chain Optimization Technologies (SCOT).
- Research Scientist at the U.S. Army Research Laboratory, Computational and Information Sciences Directorate (Sept. 2017–2021).
- Collaborated with Brian Sadler, Ethan Stump, and Jon Fink at ARL.
- Earlier research experiences at ARL with Alma Wickenden and William Nothwang, and as an undergraduate at WashU.
Education
- PhD in Electrical and Systems Engineering from the University of Pennsylvania in 2017, advised by Alejandro Ribeiro, focusing on statistical signal processing—particularly distributed online and stochastic optimization.
- Concurrently earned a Master’s degree in Statistics from the Wharton School, University of Pennsylvania.
- BS in Mathematics/Systems Science (2011) and MS in Mathematics (2012) from Washington University in St. Louis (WashU).
- Undergraduate research at WashU Mathematics Department under the guidance of Renato Feres.
- Participated in the DoD-sponsored SMART Scholarship program, hosted by the U.S. Army Research Laboratory’s Computational and Information Sciences Directorate.
Background
- Currently AI Research Lead in the Multiagent Learning and Simulation Group at JP Morgan Chase & Co.
- Research focuses on optimization and machine learning methods for autonomous systems and supply chain problems, especially inventory planning and vendor selection.
- Current research interests include: Reinforcement Learning, Scalable online Bayesian and nonparametric methods.
- Previously worked on and remains interested in: Online Learning, Stochastic Optimization, Decentralized Optimization.
- Generally interested in learning theory that bridges theoretical rigor and practical utility.
- Welcomes collaboration inquiries from students or recent graduates working in related areas or their applications in finance/economics, but recommends reading his papers beforehand and coming prepared with specific questions.
