Expertise
Database systems, data quality, data privacy, data analytics, information extraction, text mining.
Areas of Specialization
Research Clusters
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Associate Professor
Computing and Software
Overview
Fei Chiang is an Associate Professor in the Department of Computing and Software, Faculty of Engineering. Her research interests are broadly in the area of data management, with a focus on data quality, data analytics, data privacy, text mining, and information extraction. She has worked at IBM Global Services, in the Autonomic Computing Group at the IBM Toronto Lab, and in the Data Management, Exploration and Mining Group at Microsoft Research. 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. She is an IBM CAS Faculty Fellow, and a recipient of a 2018 Ontario Early Researcher Award.
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
Recent
(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.
Patents
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.