Dr. Fei Chiang – Faculty of Engineering
Fei Chiang

Dr. Fei Chiang


Database systems, data quality, data privacy, data cleaning, mining temporal graphs, data profiling

Areas of Specialization

Research Clusters

  • Associate Professor

    Computing and Software


Fei Chiang is an Associate Professor in the Department of Computing and Software, and the Director of the Data Science Research Lab at McMaster University. She is an international expert in rule-based data cleaning systems, and her research interest is broadly in data management, spanning data quality, data profiling, temporal graphs, and database systems. She holds four patents for her work in self-managing database systems. She is a recipient of an Ontario Early Researcher Award (2018). She was a Faculty Fellow at the IBM Centre for Advanced Studies. Her industry experience includes positions at Microsoft Research (Redmond), IBM Global Services, and the IBM Toronto Software Lab (Markham). She served as an inaugural Associate Director of the McMaster MacData Institute, is an Associate Editor for the ACM Journal of Data and Information Quality, and was Co-Treasurer of the 2023 International Conference on Very Large Databases (VLDB). She received her M. Math from the University of Waterloo, and B.Sc and PhD degrees from the University of Toronto, all in Computer Science.

Block Heading

Fei Chiang is an Associate Professor in the Department of Computing and Software, where her data management experience spans academic and industry roles, including serving as an inaugural Associate Director of McMaster’s MacData Institute. She leads the Data Science Research Group, focused on developing tools to facilitate data cleaning, improved data quality and fostering knowledge discovery. Her research in discovering data quality rules, and developing data cleaning algorithms, has been published in top-tier venues. She holds 4 patents for her work in self-managing databases, and is a Faculty Fellow at the IBM Centre for Advanced Studies, where she is the PI to develop data quality metrics for IBM Watson Analytics. She has been invited as a featured speaker and panellist, and her work has been featured in McMaster Research News, and the SOSCIP 2017 Impact Report. She is the recipient of a 2018 Ontario Early Researcher Award.

Hons. B.Sc. in Computer Science, Major in Mathematics, University of Toronto

M. Math in Computer Science, University of Waterloo

Certificate in Teaching in Higher Education, University of Waterloo

PhD in Computer Science, University of Toronto

(DBLP List of Publications)

Z. Zheng, T. Quach, Z. Jin, M. Milani, F. Chiang. “CurrentClean: Interactive Change Exploration and Cleaning of Stale Data”. CIKM 2019, pp. 2917-2920.
H. Ma, M. Alipour Langouri, Y. Wu, F. Chiang, J. Pi. “Ontology-based Entity Matching in Attributed Graphs”. VLDB 2019, pp. 1195 – 1207.
M. Milani, Z. Zheng, F. Chiang “CurrentClean: Spatio-temporal Cleaning of Stale Data.” ICDE 2019, pp. 172-183.
Y. Huang, M. Milani, F. Chiang. “PACAS: Privacy-Aware, Data Cleaning-as-a-Service”. IEEE International Conference on Big Data, pp. 1023-1030, 2018.
M. Langouri, F. Chiang. “KeyMiner: Discovering Keys for Graphs”. In VLDB workshop on Advances in Mining Large-Scale Time Dependent Graphs, 2018.
M. Langouri, Z. Zheng, F. Chiang, L. Golab, J. Szlichta. “Contextual Data Cleaning”. In ICDE workshop on Context in Analytics, 2018.
F. Chiang, D. Gairola. “InfoClean: Protecting Sensitive Information in Data Cleaning”. In ACM Journal of Data and Information Quality. Vol. 9(4), 2018, pp. 1-26
Z. Zheng, M. Alipour Langouri, Z. Qu, I. Currie, F. Chiang, L. Golab, J. Szlichta. “FastOFD: Contextual Data Cleaning with Ontology Functional Dependencies”. In EDBT pp. 694-697, 2018 (demo track).
S. Baskaran, A. Keller, F. Chiang, J. Szlichta, L. Golab. “Efficient Discovery of Ontology Functional Dependencies”. In CIKM, pp. 1847-1856, 2017.


I. Elghandour, A. Aboulnaga, D. Zilio, F. Chiang, A. Balmin, K. Beyer, C. Zuzarte.
XML Index Recommendation with Tight Optimizer Coupling. Filed Sept. 2007.
F. Chiang, B. Schiefer, S. Lightstone.
Method and Model for Calculating the Default Database Memory Allocation, published Jan. 2006.
S. Lightstone, F. Chiang, I. Lew, I. Popivanov, M. Emmerton.
Approximate Time Constrained Database Activation, published Jan. 2006.
F. Chiang, S. Lightstone, L. Cranston, D. Zilio.
Method and Apparatus for Automatic Recommendation and Selection of Clustering Indexes, US Patent 7548903. Issued Jan. 2005.