Background

• Assistant Professor, University of South Carolina, August 2018-Present
• Postdoctoral Associate, Carnegie Mellon University, 2016-2018
• Postdoctoral Associate, Imperial College London, 2014-2016
• Ph. D., Dublin City University (2014)
• M.S., Amirkabir University of Technology (2006)
• B.S., Amirkabir University of Technology (2003)

Research

Pooyan Jamshidi's research involves designing novel artificial intelligence and machine learning algorithms and investigating their theoretical guarantees. He is also interested in applying the AI/ML algorithms in high-impact applications, including robotics, computer systems, healthcare, neuroscience, space explorations, engineering, and sciences. Pooyan has extensive collaborations with industry, including Google and NASA, and he is always open to new collaborations. 

Research Themes:

• Causal AI and Statistical ML: Theory, Structure Learning, Inference, Transfer Learning, Multi-objective Optimization
• Deep Learning: Deep Learning for Symbolic Math, Representation Learning, Deep RL, Neural Architectures
• Trustworthy AI: Security of ML/AI, Robustness, Explainability
• ML for Systems: Computer Architecture, Machine Learning Systems, Highly-Configurable Systems
• Robot Learning: Causal Reinforcement Learning, Autonomous Robots, Autonomous Space Rovers, Self-Adaptive Systems
  

His Application Interests Include:

• Computer Systems: Autonomous systems, Robotics, Big data analytics, Computer architecture, Software engineering
• Healthcare: Cancer research, Functional genomics, Drug discovery
• Chemistry: Learning molecular representations
• Mathematics: Symbolic mathematics
• Neuroscience: Child learning, Neuroplasticity, Meta-learning