معرفی
Dr. Hassan Ashtiani is an Associate Professor in the Department of Computing and Software at McMaster University and a faculty affiliate at the Vector Institute. He holds a PhD in Computer Science from the University of Waterloo (2018), a master’s in AI and Robotics, and a bachelor’s in computer engineering from the University of Tehran.
His research focuses on machine learning, statistical learning theory, and theoretical computer science, with emphasis on adversarial robustness, privacy-preserving algorithms, and sample-efficient learning. Current projects include differentially private machine learning, robustness against adversarial perturbations, and distribution shifts.
Recent work highlights include NeurIPS 2018 best paper award for pioneering distribution compression schemes in Gaussian mixtures. He routinely serves as an area chair for NeurIPS and other ML conferences. His CAS 775 course explores modern distribution learning theory, covering topics like PAC learning, computational complexity, and differential privacy.
Awards:
- NeurIPS Best Paper Award (2018)
Advising: Open PhD/MSc positions are listed on his homepage. His research group collaborates with the Vector Institute, focusing on advancing theoretical foundations of machine learning with practical applications.
