Research scientist.
Software engineer.
Educator.

Oktie Hassanzadeh is a Senior Research Scientist at IBM T.J. Watson Research Center and an Adjunct Associate Professor of Computer Science at Columbia University. He specializes in building and evaluating data systems, with expertise in query processing, data integration, data discovery, and AI. His work combines hands-on software engineering with the leadership of research programs that produce enterprise systems, open-source tools, and evaluation benchmarks.

At IBM, he leads the design and implementation of a Semantic Query Processing Engine for enterprise data lakes, including extensions to Velox, Presto, and Spark. His research examines how large language models can be incorporated into query execution and how the reliability and reuse of their results can be evaluated. He was also lead developer and co-founder of Helix, a system for discovering and exploring heterogeneous data sources—early work on what would become known as data lakes. He later led the Engine for Forecasting Significant Societal Events research program, which contributed to IBM’s Scenario Planning Advisor, deployed for the IBM Chief Risk Office.

Evaluation is a central theme of his research, spanning clustering and entity resolution, data discovery, Text-to-SQL, and LLM-powered query systems. He led IBM’s work on enterprise Text-to-SQL evaluation and released the IBM Text-to-SQL Evaluation Toolkit. He also co-founded and co-organized the SemTab challenge from 2019 to 2025, developing shared benchmarks for semantic table interpretation. This experience connects the design of evaluation methods with the practical demands of building and deploying data systems.

His work has received best paper awards at ESWC, the ICAPS 2020 Best System Demonstration Award, and IBM research and technical accomplishment awards. He earned his Ph.D. in Computer Science at the University of Toronto and teaches Introduction to Databases at Columbia University.