Human-guided ML pipelines for data unification and cleaning might be the only way to provide complete and trustworthy data sets for effective analytics.
Ihab Ilyas is a professor in the Cheriton School of Computer Science at the University of Waterloo, where his main research is in the area of data management, with special interest in big data, data quality and integration, managing uncertain data, and information extraction. Ihab is also a co-founder of Tamr, a startup focusing on large-scale data integration and cleaning. He is a recipient of the Ontario Early Researcher Award (2009), a Cheriton Faculty Fellowship (2013), an NSERC Discovery Accelerator Award (2014), and a Google Faculty Award (2014), and he is an ACM Distinguished Scientist. Ihab is an elected member of the VLDB Endowment board of trustees, the elected vice chair of the ACM SIGMOD, and an associate editor of the ACM Transactions on Database Systems (TODS). He is also the recipient of the Thomson Reuters Research Chair in Data Quality at the University of Waterloo. He received his PhD in computer science from Purdue University, West Lafayette.