Murali Mani

Murali Mani is a Professor in the Department of Computer Science, Engineering, and Physics at the College of Innovation and Technology, University of Michigan-Flint. He is actively involved in teaching courses such as Database Design (CSC 384, CSC 584) and Independent Graduate Study in Computer Science (CSC 591), and serves as Principal Investigator on multiple research grants focused on computing education and data science.

His research interests span database systems, data provenance, generative AI for data augmentation, computing education, and the societal impact of technology. He has developed educational tools including epidemiology calculators and market basket analysis modules to support interdisciplinary learning. His work emphasizes integrating computing skills across disciplines such as health sciences and management.

The 15 most recent scholarly contributions reflect a strong focus on data management, AI-augmented data curation, educational technology, and the cognitive aspects of learning programming. These publications appear in venues such as VLDB, IEEE FIE, and ACM conferences, with several under review or in preparation for top-tier journals like Communications of the ACM and the VLDB Journal.

Scientific Awards:

• No awards explicitly mentioned in the provided text.

Advising and Grants: Murali Mani actively mentors students through independent graduate studies and collaborative research projects. He has secured funding from the National Science Foundation (SGER grant on provenance metadata) and internal university sources, including the CIT/CHS Joint Grant and the Office of Research at UM-Flint, supporting projects on civic literacy, computational skills integration, and AI for social science data archiving.

Labs and Teams: While no formal lab name is mentioned, Murali Mani leads a research group focused on data systems and computing education, collaborating with colleagues across departments and institutions. He contributes to initiatives such as the Michigan Institute for Data & AI in Society (MIDAS) and the Academic Data Science Alliance (ADSA), and has presented at conferences including IASSIST, FIE, and ICCTAC.