Put AI to Work
April 15-18, 2019
New York, NY
Matthew REYES

Matthew REYES
Data Scientist, Technergetics

Website

Matthew Reyes is a consultant and an independent researcher developing a reinforcement learning-based approach to influence the maximization on social networks. Previously, he spent more than four years at MIT’s Lincoln Laboratory. He holds a PhD and MS in electrical engineering systems from the University of Michigan, Ann Arbor, and an MS and BS in math from Wichita State University.

Matthew Reyes is a contractor at Technergetics working on deep learning and FPGAs. He earned his B.S. and M.S. in mathematics at Wichita State University in Wichita, KS. He then earned his M.S. and Ph.D. in EE:Systems at the University of Michigan in Ann Arbor, MI doing a thesis on compression of Markov random fields. He worked for four years at MIT Lincoln Laboratory doing work on sensor calibration. From early 2015 to early 2019, Matt conducted independent research on compression, belief propagation, and interpolation of Markov fields, and is currently developing a model of social decision-making based on random utility.

Sessions

4:55pm–5:35pm Thursday, April 18, 2019
A reinforcement learning approach to optimizing preference on a social network
Models and Methods
 Location: Trianon Ballroom
Secondary topics:  Media, Marketing, Advertising, Models and Methods, Reinforcement Learning
Matthew REYES (Technergetics)
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Matthew Reyes casts consumer decision making within the framework of random utility and outlines a simplified scenario of optimizing preference on a social network to illustrate the steps in a company’s allocation decision, from learning parameters from data to evaluating the consequences of different marketing allocations. Read more.