Aaron is a Research Scientist at Google NYC, where he leads the Large-Scale Optimization research team, which is part of the broader NYC Algorithms and Optimization team. He came to Google in 2013 after 9+ years in the Algorithms and Optimization department at the AT&T Shannon Research Laboratory. Aaron earned degrees in Operations Research (M.S. 2002, Ph.D. 2004) from Cornell University, where he was advised by Eva Tardos and supported by a Hertz Fellowship. Prior to that, he studied Mathematics (B.S. 1998) at Harvey Mudd College, where he graduated first in his class. Aaron has pursued theoretical, experimental and applied research in diverse areas including mathematical programming (e.g., linear, integer, and convex programming), approximation algorithms, graph algorithms, algorithmic game theory, online algorithms, network design, load balancing, and unsupervised machine learning (e.g., clustering). He particularly enjoys finding effective ways to model complex real-world optimization problems. His primary mission at Google has been to apply these and other techniques to improve the efficiency of Google's computational infrastructure, such as the backend for websearch. Aaron has also made small forays into graphics, machine vision, combinatorics, graph theory, computational ecology, and mathematical rap.