Short Bio
Pooyan Jamshidi is an assistant professor in the Computer Science and Engineering department at the University of South Carolina. He holds a PhD in Computer Science from Dublin City University and has completed postdoctoral research at Carnegie Mellon University and Imperial College London. Pooyan has also worked in the industry; most recently, he was a visiting researcher at Google in 2021. Dr. Jamshidi, who received the University of South Carolina's 2022 Breakthrough Stars Award, specializes in developing resilient systems for dynamic environments. His work integrates various areas such as distributed systems, statistical and causal learning, and robotics, focusing on areas like autonomous systems, AI accelerators, and software/hardware co-design.
Long Bio
Pooyan Jamshidi is an assistant professor in the Computer Science and Engineering department at the University of South Carolina. Before his current position, Pooyan was a postdoctoral researcher at Carnegie Mellon University (2016 - 2018) and Imperial College London (2014 - 2016). He received a Ph.D. in computer science from Dublin City University in 2014 and an M.S. and B.S. in Systems Engineering and Computer Science and Math from the Amirkabir University of Technology in 2003 and 2006. Pooyan has also worked in the industry; most recently, he was a visiting researcher at Google in 2021. He received the University of South Carolina's 2022 Breakthrough Stars Award. Pooyan's research interests span the areas of Software, Systems, AI/ML, and Robotics. In particular, he is interested in developing algorithms and tools that enable building resilient systems deployed in dynamic environments that can automatically handle goal tradeoffs, incorporate user preferences and constraints, identify causes of failures, and self-adapt to be able to operate in dynamic environments. Pooyan integrates distributed systems, control theory, statistical learning and optimization, causal inference, representation learning, and transfer learning. In addition to the theoretical contributions, he is excited about several directions in ML for Systems and Systems for ML--autonomous systems, robot learning, AI accelerators, system performance optimization, software/hardware co-design, and model robustness.
