معرفی
Hassan Z. Ashtiani is an Associate Professor in the Department of Computing and Software within the Faculty of Engineering at McMaster University. His academic profile shows consistent engagement in both teaching and research activities, with evidence of active participation in major machine learning conferences and journals through 2025.
Dr. Ashtiani's research focuses on the theoretical foundations of machine learning, with particular expertise in privacy-preserving algorithms, Gaussian mixture models, and adversarial robustness. His work bridges statistical learning theory with practical algorithm design, often addressing fundamental questions about sample complexity and computational efficiency in learning systems. A significant portion of his recent work explores the intersection of differential privacy with statistical learning, developing methods for private density estimation and distribution learning.
Analysis of his publication record reveals a strong trend toward increasingly sophisticated theoretical frameworks for private and robust learning. His work consistently appears in top-tier venues including NeurIPS, ICML, COLT, and ALT, with recent contributions extending into agnostic private density estimation and robust learning with tolerance. The research demonstrates progression from foundational work on nearest neighbor search and clustering algorithms toward more complex problems in private learning of high-dimensional distributions.
Dr. Ashtiani teaches across multiple levels of computer science education, including undergraduate courses in Automata and Computability (COMPSCI 2AC3) and Principles of Programming (COMPSCI 2S03), as well as graduate-level courses such as Fundamentals of Machine Learning (COMPSCI 4ML3) and Theoretical Foundations of Unsupervised Learning (CAS 775). His teaching portfolio shows consistent involvement in machine learning education since at least 2019, with evidence of teaching multiple sections each academic year.
His scholarly impact is reflected in mentions across 3 news outlets, reference in 1 policy source, engagement from 7 X users, and 90 readers on Mendeley, suggesting growing recognition of his contributions to theoretical machine learning.
