Hassan Ashtiani is an Assistant Professor in the Department of Computing and Software at McMaster University, and a faculty affiliate at Vector institute. He obtained his Ph.D. 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 attacks. He is one of the recipients of a best paper award at NeurIPS 2018 for introducing distribution compression schemes and resolving the sample complexity of learning Gaussian Mixture Models up to logarithmic factors.