Registered user since Tue 19 Sep 2017
Pooyan Jamshidi is an Assistant Professor in the Computer Science and Engineering Department at the University of South Carolina and a Visiting Researcher at Google. He directs the AISys Lab, where he—with his students, postdocs, and collaborators—designs theoretically principled AI algorithms and applies the algorithms to solve problems in computer systems, software engineering, robotics, self-adaptive and autonomous systems, on-device machine learning, cloud-based and serverless systems, big data analytics pipelines, and healthcare. He is, in particular, interested in causality, transfer learning, optimization, causal/deep representation learning, machine learning security, reinforcement learning, as well as several areas in computer systems research, including performance, configuration, computer architecture. Prior to his current position, he was a research associate at Carnegie Mellon University and Imperial College London. He received a Ph.D. in Computer Science at Dublin City University in 2014 and M.S. and B.S. degrees in Systems Engineering, Mathematics, and Computer Science from Amirkabir University in 2003 and 2006n respectively.
Contributions
2024
ACSOS
2023
International Conference on Software Engineering for Adaptive and Self-Managing Systems
2022
ICSE
- Author of On Debugging the Performance of Configurable Software Systems: Developer Needs and Tailored Tool Support within the Technical Track-track
- Session Chair of BoF 3: Causal AI for Software (part of Birds of a Feather)
- Committee Member in Program Committee within the NIER - New Ideas and Emerging Results-track
International Conference on Software Engineering for Adaptive and Self-Managing Systems
- Committee Member in Steering Committee within the SEAMS 2022-track
- Committee Member in Program Committee within the SEAMS 2022-track
- Social Media Chair in Organizing Committee within the SEAMS 2022-track
- Session Chair of Learning (part of SEAMS 2022)
- Ordinary PC member in Doctoral Symposium Committee within the SEAMS 2022-track