The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester.

AI/ML are transforming societal systems: Opportunities abound for the transformation of multi agent social systems using new technologies and business models to address some of the most pressing problems in diverse sectors such as energy, transportation, health care, manufacturing and financial systems. Indeed as a consequence the term “digital transformation of societal scale systems” has become a favorite board room buzzword. The issues of transforming societal systems is accompanied by issues of economic models for trans formation, privacy, cyber security and fairness considerations. Indeed, the area of “mechanism and incentive design” for societal scale systems is a key feature in transitioning the newest technologies and providing new services. Crucially, human beings interact with automation and change their behavior in response to incentives offered to them. Training, Learning and Adaptation in Human AI Teams (HAT) is one of the most engaging problem in Human-AI/ML systems today. In this course, we will present a few vignettes: how to align societal objectives with Nash equilibria using suitable incentive design, proofs of stability of decentralized decision making while learning preferences. The application of the techniques to multi-agent problems in real time tolling, Advanced Air Traffic Management for Air Taxis and High Value Package Delivery, Multi-Car Racing and Pursuit Evasion Games, and other societal scale systems will be presented. This is a graduate level class. We will need to cover materials from an array of disciplines and analytical techniques and a fair amount of self study will be needed. We will have four homework assignments (30 % of the grade), one mid term literature presentation (20 % of the grade)and one substantial final project (50 % of the grade)

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