Mahnoosh Alizadeh is an Assistant Professor of Electrical and Computer Engineering at the University of California, Santa Barbara.
Dr. Alizadeh’s research is focused on designing scalable control and data analytic frameworks and market mechanisms for enabling sustainability and resiliency in societal infrastructure systems (e.g., power systems, transportation systems, the cloud) and smart cities. She is the director of the Smart Infrastructure Systems laboratory.
Prior to joining UCSB, Dr. Alizadeh spent two years at Stanford University as a postdoctoral scholar. She received her PhD degree in Electrical and Computer Engineering from the University of California Davis in 2014 and her B.Sc. degree in Electrical Engineering from Sharif University of Technology in 2009.
What do I do?
I seek to design smarter algorithms for operating our societal-scale infrastructure systems through the use of information technology. My objective is to improve sustainability, reliability, efficiency, and address the challenges of rapid urbanization.
Modern societal-scale infrastructures can be thought of as large-scale Cyber-Human-Physical Systems (CHPS). CHPS are networked systems of physical assets with embedded intelligence and human-machine interfaces. Ubiquitous intelligence and information exchange and the active involvement of humans in the control loop distinguish them from their predecessors and gives them the potential to be more adaptive, resilient, and safe. However, these new characteristics also make our future infrastructure systems and cities vulnerable because no single engineering or science discipline has all the right tools to design simple and scalable monitoring and control solutions to manage their operations reliably and efficiently, requiring interdisciplinary efforts to lead the transition. My research interests lie in addressing this interdisciplinary challenge through systematic reduced-order modeling of physical components, large-scale data analytics for real-time cyber monitoring and intelligent network control, as well as incentive design and market analysis to manage human involvement in real-time control loops.
In general, my research is modeling-heavy. I use a blend of several disciplines including but now limited to stochastic optimization, control, power systems engineering, transportation engineering, statistical learning and signal processing, game theory, and economics.
See some highlighted projects here.
Note to prospective students
As you can see from the above description, my research is quite interdisciplinary. Our research group has openings for graduate students with a strong mathematical background. If are interested in working with me, please apply to the Electrical and Computer Engineering graduate program. If you would like to send me an email about your application, please include your CV and transcripts, as well as a brief description of any past research experience.