I study problems at the intersection of data management and machine learning. My research is driven by the need to build trustworthy and responsible decision-making systems.
Recently, I have been working in the area of responsible data science, developing systems aimed at enabling explainability, fairness, and accountability of data-driven decision-making systems.
In the past, I have worked on problems in data integration, focusing on resolving conflicts in data integrated from disparate data sources and ensuring end-users can trust the quality of the integrated data.
I am always looking for PhD students to collaborate with. If you are interested in data management or responsible data analytics, feel free to contact me with your CV/resume and a couple of sentences describing your research interests, and consider applying to Purdue CIT!
I am teaching a graduate course on Responsible Data Management in Spring 2023-- check out the course webpage for more details.
11/22 Delighted to receive funding from the Center for Advancing Safety of Machine Intelligence (CASMI) for building safe, equitable, and beneficial AI systems 10/22 Position paper with Tianyi Li on human-in-the-loop bias mitigation in data science accepted to the Human-Centered AI Workshop at NeurIPS 2022 6/22 Presented our paper on debugging fairness of machine learning models (Gopher) at SIGMOD 22 6/22 & 5/22 Gave tutorial on Explainable AI and Opportunities for Data Management Research at SIGMOD 22 and ICDE 22 4/22 Honored to receive a Google Research Scholar Award! Thanks, Google! 3/22 Chaired the research talk session on "Data Quality and Curation" at EDBT 22 3/22 Serving on the PC of CIKM 22 (Short paper track) 2/22 Demonstration paper of Gopher accepted to SIGMOD 22 2/22 Tutorial on Explainable AI and Opportunities for Data Management Research accepted to SIGMOD 22 ... more