In recent years, there has been a renewed research interest in finding efficient algorithms for solving convex optimization problems in a parallel or distributed fashion. This trend has been mainly motivated by the explosion in size and complexity of data-sets used in statistical machine learning and applications in modern cyberphysical systems, communication networks, and other applications in networked systems. This talk presents how automatic control-theoretic tools contribute to developing and analyzing distributed convex optimization algorithms systematically.
Bio: Solmaz S. Kia who an associate professor of Mechanical and Aerospace Engineering at the University of California Irvine (UCI), with a joint appointment at the Computer Science department. She obtained her Ph.D. degree in Mechanical and Aerospace Engineering from UCI, in 2009, and her M.Sc. and B.Sc. in Aerospace Engineering from the Sharif University of Technology, Iran, in 2004 and 2001, respectively. She was a senior research engineer at SySense Inc., El Segundo, CA from Jun. 2009-Sep. 2010. She held postdoctoral positions in the Department of Mechanical and Aerospace Engineering at UC San Diego and UCI. She was the recipient of the prestigious UC President's Postdoctoral Fellowship from 2012-2014. She is also a recipient of the 2017 NSF CAREER award and the 2021 IEEE Control Systems best paper award. Dr. Kia is a senior member of IEEE and serves as an associate editor for Automatica, IEEE Transactions on Control of Network Systems, IEEE Open Journal of Control Systems and IEEE Sensors Letters. Dr. Kia's main research interests, in a broad sense, include nonlinear control theory, distributed optimization/coordination/estimation, and probabilistic robotics.