•   When: Monday, February 21, 2022 from 11:00 AM to 12:00 PM
•   Speakers: Sourav Medya
•   Location: ZOOM only
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Abstract:

Networks (or graphs) are a powerful tool to model complex systems such as social networks, transportation networks, and the Web. The accurate modeling of such systems enables us to improve infrastructure, reduce conflicts in social media, and make better decisions in high-stakes settings. However, as graphs are highly combinatorial structures, these optimization and learning tasks require the design of efficient algorithms. 

 In this talk, I will describe three research directions in the context of network data. First, I will overview several combinatorial problems for graph optimization that I have addressed using classical approaches such as approximate and randomized algorithms. The second part will focus on a different and a more recent approach to solving combinatorial problems by leveraging the power of machine learning. More specifically, I will show how combining neural architectures on graphs with reinforcement learning solves popular data ming problems such as the influence maximization problem.  In the last one, I will demonstrate how to deploy these methods on problems in computational social science with applications in decision-making for patent review systems and the stock market. 

 Biography: 

Sourav Medya is a research assistant professor in the Kellogg School of Management at Northwestern University. He is also affiliated with the Northwestern Institute of Complex Systems. He has received his Ph.D. in Computer Science at the University of California, Santa Barbara. His research has been published at several venues including VLDB, NeurIPS, WebConf (WWW), AAMAS, IJCAI, WSDM, SDM, ICDM, SIGKDD Explorations, and TKDE. He has also been a PC member for WSDM, WebConf, AAAI, SDM, AAMAS, ICLR, and IJCAI. 

Sourav's research is focused on the problems at the intersection of graphs and machine learning. More specifically he designs data science tools that optimize graph-based processes and improve quality as well as scalability of traditional graph combinatorial and mining problems. He also deploys these tools to solve problems in the interdisciplinary area of computational social science especially to improve innovation.