Hassan Ashtiani is an Assistant Professor in the Department of Computing and Software at McMaster University, and a Faculty Affiliate of the Vector Institute for Artificial Intelligence. He obtained his PhD in Computer Science in 2018 from University of Waterloo where he was advised by Shai Ben-David. Before that, he received his master’s degree in AI and Robotics and his bachelor’s degree in Computer Engineering, both from University of Tehran. Broadly speaking, a major theme in his research is the design and analysis of sample-efficient learning algorithms. In recent years, he has focused on studying sample-efficient learning methods that are robust to (i) model misspecification, (ii) distribution shift, (iii) adversarial attacks, and/or (iv) privacy-related attacks.
- Statistical learning theory
- Machine learning
- Privacy, anonymity, and/or security
- Best paper award at Neurips 2018