Rate My Professor Qin Zhang

Qin Zhang's research interests include algorithms for big data such as communication-efficient algorithms for distributed data, streaming and sketching algorithms, algorithms for massive parallel computation, and lower bounds; theoretical foundations for machine learning and AI including collaborative learning, distributed learning, and learning-augmented algorithms; and quantum information and computing with a focus on quantum data management. She has received the NSF CAREER Award (2019), Best Paper Award at the ACM Symposium on Parallelism in Algorithms and Architectures (SPAA 2017), and Luddy Faculty Fellow (2023/24). Four of her conference papers have been invited to journals: ACM Transactions on Database Systems (PODS 2020), ACM Transactions on Parallel Computing (SPAA 2017), Distributed Computing (DISC 2013), and Journal of the ACM (PODS 2010). She was recognized as Top Reviewer for NeurIPS 2019 and ICML 2020. Key publications include "Quantum Data Sketches" (ICDT 2025), "Robust Statistical Analysis on Streaming Data with Near-Duplicates in General Metric Spaces" (PODS 2025), "MinJoin++: A Fast Algorithm for String Similarity Joins under Edit Distance" (VLDB Journal 2024), "Distributed Partial Clustering" (ACM Transactions on Parallel Computing 2019), "When Distributed Computation is Communication Expensive" (Distributed Computing 2017), and "Continuous Sampling from Distributed Streams" (Journal of the ACM 2012). Qin Zhang has served as Action Editor for Transactions on Machine Learning Research (2022-2023), Guest Editor for ACM Transactions on Algorithms (SODA 2016 Special Issue), and on program committees for ICML (Area Chair 2023-2025), AAAI, ICDE, ICLR, and others. At Indiana University, she chairs the Ph.D. Admissions and Award Committee (2021-2024) and M.S. Admissions and Award Committee (2021-present), and serves on faculty search committees and data science curriculum committees.