Dr. Ramtin Pedarsani PhD of UCSB - ECE

Abstract: The COVID-19 pandemic has severely affected many aspects of people's daily lives. While many countries are in a reopening stage, some effects of the pandemic on people's behaviors are expected to last much longer, including how they choose between different transport options. Experts predict considerably delayed recovery of the public transport options, as people try to avoid crowded places. In turn, significant increases in traffic congestion are expected, since people are likely to prefer using their own vehicles or taxis as opposed to riskier and more crowded options such as the railway. We propose to use financial incentives to set the tradeoff between risk of infection and congestion to achieve safe and efficient transportation networks. For our framework to be useful in various cities and times of the day without much designer effort, we also propose a data-driven approach to learn human preferences about transport options.

Biography:

Biography: Dr. Pedarsani is an assistant professor at the University of California, Santa Barbara, Department of Electrical and Computer Engineering. His research interests are broadly in the areas of machine learning, optimization, coding and information theory, applied probability, and intelligent transportation systems. Before joining as a faculty at UCSB in Fall 2016, he was a postdoctoral scholar in the EECS Department at UC Berkeley. He obtained his PhD in May 2015 from the EECS Department at UC Berkeley. He received his M.Sc. degree at EPFL in 2011, and his B.Sc. degree at the University of Tehran in 2009. He is the recipient of the Communications Society and Information Theory Society Joint Paper Award in 2020, the best paper award in the IEEE International Conference on Communications (ICC) in 2014, and the NSF CRII award in 2017.