Learning to Price Demand in Safety-Critical Infrastructure Systems
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ABSTRACT: This talk is motivated by the problem of learning to design dynamic prices for demand management in safety-critical infrastructure systems such as the power grid. Faced with uncertainty regarding how customers will respond to posted prices, we highlight the need for safety-aware bandit optimization algorithms for designing prices that control the probability of violation of system constraints in spite of uncertainty. Then, in the first part of the talk, we discuss how we can manage safety constraints in the admittedly simpler problem of linear stochastic bandits with affine constraints, and provide formal regret guarantees. In the second part of the talk, we discuss how our insights can be generalized for safe and efficient price design in our motivating example of dynamic pricing in power systems.
BIO: Mahnoosh Alizadeh is an assistant professor of Electrical and Computer Engineering at the University of California Santa Barbara (since January 2017). She received the B.Sc. degree in Electrical Engineering from Sharif University of Technology in 2009 and the M.Sc. and Ph.D. degrees from the University of California Davis in 2013 and 2014 respectively, both in Electrical and Computer Engineering. From 2014 to 2016, she was a postdoctoral scholar at Stanford University. Her research is focused on the design of network control and optimization algorithms for societal-scale cyber-physical systems, with a particular focus on renewable energy integration in the power grid and electric transportation systems. She is a recipient of the NSF CAREER award.
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