Research results published in top-tier venues such as SIGMOD, VLDB, ICDE, KDD, and TKDE; serves as Associate/Area Editor for TKDE, TKDD, and Information Systems; served as General Co-Chair of ACM SIGKDD 2025; paper 'Selective Cloud Offloading for Accurate and Efficient Object Detection' accepted to ICDM 2025; paper 'Approximating Gradient-Based Influence for Scalable Instruction Data Selection' accepted to CIKM 2025; paper 'Visualization-Oriented Progressive Time Series Transformation' accepted to SIGMOD 2026; Davood Dehghani successfully defended his Master's thesis in July 2025; received NSERC Alliance funding with IBM for the project 'Optimized data compression and scalable verification for efficient data migration' in May 2025.
Research Experience
Professor and Director at the School of Information Technology, York University; research focuses on designing ML-driven database components, developing algorithms and infrastructures for robust, adaptive, and efficient large-scale ML, and creating data preparation, valuation, and governance frameworks.
Education
BSc from Nanjing University, China; MPhil from the Chinese University of Hong Kong; PhD from the University of Toronto.
Background
Research interests: data-centric artificial intelligence, particularly how data quality, management, and representation shape the effectiveness and reliability of machine learning systems. Professional fields include Information Systems & Technology, Electrical Engineering & Computer Science, and Mathematics & Statistics.
Miscellany
Looking for self-motivated students to join the research group as PhD/Master's students or Post-doctoral Fellows.