Cheriton School of Computer Science Professor Ihab Ilyas has been named a Fellow of the Institute of Electrical and Electronics Engineers for his contributions to data integration, data cleaning and rank-aware query processing.
IEEE Fellowships are a prestigious professional recognition and an important career achievement. A Fellow is the highest grade of IEEE membership and it is conferred to those with an outstanding record of accomplishments. Each year, the total number of IEEE members recognized as Fellows does not exceed one-tenth of one per cent of the Institute’s total voting membership.
Over his career, Professor Ihab Ilyas has received many awards and has had multiple honours conferred by peers, professional organizations and associations, most recently his being named a Fellow by the Institute of Electrical and Electronics Engineers.
In 2020, Professor Ilyas was also named a Fellow of the Association for Computing Machinery for his contributions to data cleaning and data integration. Since 2018, he has held the Thomson Reuters–NSERC Industrial Research Chair in Data Cleaning. He was named an ACM Distinguished Scientist in 2014, the same year in which he received a Google Faculty Award. He also held a Cheriton Faculty Fellowship at the University of Waterloo from 2013 to 2016 and a Fellowship at the IBM Centre for Advanced Studies from 2006 to 2010.
Professor Ilyas’s many accomplishments include successful entrepreneurial pursuits. He co-founded two companies based on his research — Inductiv, a Waterloo-based start-up, now part of Apple Inc., that uses AI for structured data cleaning, and Tamr, a start-up focusing on large-scale data integration and cleaning.
Professor Ilyas’s contributions also include serving as an elected member of the Board of Trustees of the Very Large Data Bases Endowment in 2016 and as the Vice Chair of the ACM Special Interest Group on Data Management (SIGMOD) in 2017.
“Congratulations to Ihab on being named an IEEE Fellow,” said Raouf Boutaba, Professor and Director of the Cheriton School of Computer Science. “Ihab is well known to computer scientists in both academia and industry because of his contributions to scalable automatic error detection, to data cleaning, and to imputation of dirty structured data. He has pioneered scalable automatic data cleaning through the systems he, his students and collaborators have developed that use state-of-the-art machine learning models.”
Professor Ilyas is the seventh faculty member at the Cheriton School of Computer Science to receive the prestigious recognition of IEEE Fellow, following Professors N. Asokan, Raouf Boutaba, J. Alan George, Ming Li, M. Tamer Özsu, and Srinivasan Keshav who is an adjunct at the Cheriton School of Computer Science and is now at Cambridge.
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More on Professor Ilyas’s contributions
Scalable automatic error detection, cleaning and imputation of dirty structured data
Professor Ilyas has addressed multiple technical challenges in scalable automatic error detection, cleaning and imputation of dirty structured data. His research presents data errors as a noisy channel with a probabilistic model to generate original clean data, and a probabilistic realization model that pollutes that data. Several key results on mining the constraints and on the learnability of these machine-learning models’ parameters using only the observed dirty data have helped to create pragmatic and scalable solutions. These machine-learning solutions incorporated modern techniques such as self-supervision, data augmentation, embedding, and schema-level attention mechanisms to build learnable complex error detection and repair models. Key insights include how violations of business rules and integrity constraints can be incorporated into these machine-learning models, which has allowed decades of logical cleaning research to be incorporated in modern and scalable techniques.
Professor Ilyas’s research in data cleaning has been recognized by academia and industry alike. He and his former PhD student Xu Chu, now faculty at Georgia Tech, coauthored Data Cleaning, among the most downloaded books in the ACM Books series. He has given many invited keynote addresses and presentations at top institutions and venues. His start-up Tamr, among the top companies in data integration and preparation, has raised more than $70 million and serves dozens of Fortune 500 companies. And Inductiv, Professor Ilyas’s start-up that uses machine learning to automate the task of identifying and correcting errors in data, was acquired by Apple Inc. in 2020. Inductiv’s technology is based on HoloClean, a next generation of machine-learning techniques to clean data that began in 2017 as a collaborative academic project led by Professor Ilyas and his colleagues Professors Theodoros Rekatsinas at the University of Wisconsin-Madison and Christopher Ré at Stanford University.
Rank-aware query processing
Professor Ilyas has also integrated rank-aware querying into database technologies to allow effective retrieval in large data sets such as those in multimedia and video databases. He developed algorithms and techniques that markedly changed how database systems handle ranking and user-preferences in processing queries.
His rank-join algorithm has been the state-of-the-art physical query operator to produce query answers ranked on user preference in a way that avoids computation of the entire answer sets. He introduced RankSQL, the first end-to-end rank-aware query engine based on novel ranked relational algebra semantics and built on top of PostgreSQL. In addition, he first introduced the problem of ranking uncertain data and provided the first meaningful semantics for the interplay between uncertainty and score-based ranking. He and his research group were the first to define the problem of ranking where either the record membership or the score values — or both — are uncertain. Professor Ilyas has also addressed uncertainty in the ranking function itself. His many papers in this area have defined a new line of research in the database community and have provided valuable insight and several practical semantics of how to produce the most probable top-k records with respect to user preferences.
