Arunesh Sinha

Assitant Professor at Singapore Management University

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  • Singapore Management University

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Biography

Singapore Management University

Dr. Arunesh Sinha is an Assistant Professor in the Department of Management Science & Information Systems, Rutgers Business School at Rutgers University. Prior to this, he was an Assistant Professor in the School of Computing and Information Systems at Singapore Management University. Prior to the position in Singapore, Dr. Arunesh Sinha was an Assistant Research Scientist in the Computer Science and Engineering Department at the University of Michigan. Prior to joining University of Michigan, he was a postdoctoral scholar with Prof. Milind Tambe in the Computer Science Department at the University of Southern California. He received his Ph.D. from Carnegie Mellon University, where he was advised by Prof. Anupam Datta. He obtained his undergraduate degree in Electrical Engineering from IIT Kharagpur in India.

Dr. Sinha has conducted research at the intersection of security, machine learning and game theory. His interests lie in the theoretical aspects of multi-agent interaction, machine learning, security and privacy, along with an emphasis on the real-world applicability of the theoretical models. He was awarded the Bertucci fellowship at CMU in appreciation of his novel research. His paper was nominated for the best application paper at AAMAS 2016, he won the best demo award at AAMAS 2021, and an honorable mention at ACSAC 2022.

Dr. Sinha's work has provided novel approaches to solve the optimization problem used in computing Stackelberg equilibrium in security games and explored learning aspects in multi-agent models. This novel Stackelberg game approach was tested in an airport screening application by TSA (called the DARMS project). Dr. Sinha has also provided mathematical foundations for the use of learning in security games, revealing the circumstances in which the composition of learning and optimization leads to sub-optimal outputs. Dr. Sinha has explored how to learn and act robustly in multi-agent settings.

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