Pooyan Jamshidi

Registered user since Tue 19 Sep 2017

Name: Pooyan Jamshidi

Bio: Pooyan Jamshidi is an Assistant Professor at the University of South Carolina. He directs the AISys Lab, where he investigates the development of novel algorithmic and theoretically principled methods for machine learning systems. Prior to his current position, he was a research associate at Carnegie Mellon University and Imperial College London, where he primarily worked on transfer learning for performance understanding of highly-configurable systems including robotics and big data systems. Pooyan’s general research interests are at the intersection of systems/software and machine learning. He received his Ph.D. in Computer Science at Dublin City University in 2014, and M.S. and B.S. degrees in Computer Science and Math from the Amirkabir University of Technology in 2003 and 2006 respectively.

Affiliation: University of South Carolina

Personal website: http://pooyanjamshidi.github.io

Research interests: Software Engineering, Systems, Machine Learning


ECSA 2021 Ordinary PC member in Program Committee within the Research Papers-track
SEAMS 2021 Session Chair of Session 6: Uncertainty and Fairness (part of SEAMS 2021)
Committee Member in Program Committee within the SEAMS 2021-track
ASE 2020 Speaker of Machine Learning meets Software Performance: Optimization, Transfer Learning, and Counterfactual Causal Inference within the Tutorials-track
ECSA 2020 Author of VisArch: Visualization of Performance-based Architectural Refactorings within the Research Papers-track
Committee Member in Program Committee within the Research Papers-track
ICGSE 2020 Committee Member in Program Committee
SEAMS 2020 Committee Member in Program Committee within the SEAMS 2020-track
Session Chair of Session 1: AI, Machine Learning and Statistics (part of SEAMS 2020)
ICSE 2021 Author of Whence to Learn? Transferring Knowledge in Configurable Systems using BEETLE within the Journal-First Papers-track
Author of White-Box Analysis over Machine Learning: Modeling Performance of Configurable Systems within the Technical Track-track
ICGSE 2019 Author of Understanding Similarities and Differences in Software Development Practices Across Domains within the ICGSE 2019 Research Papers-track
SEAMS 2019 Program Committee in Program Committee within the SEAMS 2019-track
Author of Machine Learning Meets Quantitative Planning: Enabling Self-Adaptation in Autonomous Robots within the SEAMS 2019-track
ICSE 2020 Session Chair of A11-Performance and Analysis (part of Paper Presentations)
Programme Committee in Program Committee within the Technical Papers-track
Session Chair of I5-Deep Learning Testing and Debugging (part of Paper Presentations)
TechDebt 2018 Author of Evaluating Domain-Specific Metric Thresholds: An Empirical Study within the TechDebt 2018-track
SEAMS 2018 Committee Member in Program Committee within the SEAMS 2018-track
ESEC/FSE 2018 Author of Learning to Sample: Exploiting Similarities Across Environments to Learn Performance Models for Configurable Systems within the Research Papers-track