Abstract: Rapid “urbanization” (more than 50% of worlds’ population now resides in cities) coupled with the natural lack of coordination in usage of common resources (ex: bikes, ambulances, taxis, traffic personnel, attractions) has a detrimental effect on a wide variety of response (ex: waiting times, response time for emergency needs) and coverage metrics (ex: predictability of traffic/security patrols) in cities of today. 

Motivated by the need to improve response and coverage metrics in urban environments, we have focussed on building intelligent agent systems that exploit past data in making sequential decisions to continuously match available supply of resources to an uncertain demand for resources.  To address the main challenges of societal scale, uncertainty and dynamism, we exploit key properties of urban environments namely homogeneity and anonymity, decomposability and abstraction of supply/demand components. In this talk, I will describe our contributions and also provide results based on real data sets (and in some cases on real systems) in transportation (taxis and bike sharing), emergency response, energy, theme parks and security. 

Pradeep Varakantham is a Lee Kong Chian Fellow and an Associate Professor in the School of Information Systems at Singapore Management University.  Prior to his current position, Pradeep received his PhD from University of Southern California and was a post-doctoral fellow for two years at Carnegie Mellon University. His research is at the intersection of Artificial Intelligence, Operations Research and Machine Learning with specific focus on solving sequential matching problems in urban environments.  Pradeep has published research papers in top tier conferences (AAAI, IJCAI, AAMAS, ICAPS, UAI, NIPS) and journals (JAIR, JAAMAS) in Artificial Intelligence and Machine Learning.