Babak Salimi

Babak Salimi is an Assistant Professor at the Halıcıoğlu Data Science Institute (HDSI) at the University of California, San Diego (UCSD), with an affiliation to the Department of Computer Science and Engineering. His research focuses on trustworthy data analysis, algorithmic fairness, and causal inference, aiming to enhance transparency, fairness, and reliability in data-driven decision-making. Salimi completed his Ph.D. in 2016 at Carleton University under Prof. Leopoldo Bertossi and was a postdoctoral researcher at the University of Washington under Prof. Dan Suciu.

Salimi has been honored with prestigious awards including the NSF CAREER Award (2024), the SIGMOD Best Paper Award (2019), and the VLDB Best Demonstration Paper Award (2018). His work emphasizes causal modeling for data quality and bias mitigation, addressing systemic biases through data-centric approaches. He actively engages in academic service roles, including General Chair of Guide-AI 2025 and Area Chair for FAccT 2023/2024.

His research group develops tools to bridge the gap between complex data and human understanding, with a focus on data management for machine learning and responsible AI practices. Salimi teaches courses like DSC 261 (Responsible Data Science) and DSC 100 (Introduction to Data Management), emphasizing education in ethical data science principles.

Key research thrusts include causal modeling for bias mitigation, data cleaning techniques, and holistic debiasing frameworks. His NSF CAREER project redefines algorithmic bias as a data quality issue, integrating causal modeling with traditional data management strategies to enhance decision-making trustworthiness.

Salimi’s work impacts diverse sectors, including healthcare and criminal justice, by improving data quality and fairness. His educational initiatives aim to broaden participation in data science through enhanced data literacy programs.