Newswise — Spend just one day in New York City, and you might be transferring subway trains, hailing taxis, calling Ubers, sitting in traffic, or scrolling the streets on Google Maps for hours. Each one of these interactions constitutes a decision and data point, produced by just one person. Multiply that by a population of 8 million more, on top of tourists, and the busiest city in the U.S. generates millions upon millions of decisions and data points every single day. New artificial intelligence models that parse through that vast sea of human-generated data, however, could further expedite and smooth the decision-making process, from commute times to traffic safety. This is the crux of Binghamton University, State University of New York Assistant Professor Yingxue Zhang’s recent National Science Foundation CAREER Award, for which she won a five-year grant of $584,649. “In urban life, people make decisions every day. Taxi drivers want to pick up passengers, and if people want to go to work, they need to switch to different public transits,” said Zhang, a faculty member at the Thomas J. Watson College of Engineering and Applied Science’s School of Computing. “They make decisions every day, every hour, every second. We want to model this kind of process to make the whole decision-making process more efficient and effective.” The CAREER Award is the NSF’s most prestigious distinction, given to early-career faculty who are poised to become the future leaders in research and education in their respective fields. Zhang’s project will be the first to use a particular type of deep learning technique, called offline reinforcement learning, to wrangle the ever-changing dynamics of urban life and spaces. “This technique has never been applied in the urban domain, which is a spatial-temporal domain. The data is spatial-temporal data. We have spatial correlations, and