I recently obtained my Ph.D. in Electrical Engineering from Stanford University, advised by Nicholas Bambos.   

My research interests are game theory, distributed control, and multiagent learning. My goal is to study how agents can make efficient decisions in networked environments where the decisions of the agents affect each other. Applications include autonomous vehicles, multi-robot systems, on-device learning, cloud computing, wireless networks, the smart grid, and more. In these networks, the cooperation of the agents is limited by their local information, so they have to learn how to behave optimally based on what they can observe, which may be as little as bandit feedback that is a function of the decisions of all agents. Typical objectives are to learn how to share resources efficiently under uncertainty, to coordinate towards a common goal, and to collaboratively learn and model the environment.  I'm astonished by how useful probabilistic tools can be to the analysis of the interactions between the agents, so I keep trying to use them. I'll let you know how it goes here. If you liked this paragraph, I have a couple more, and if you didn't like this paragraph, I have others.