About

Daniel Kifer is a Professor in the Department of Computer Science and Engineering at Pennsylvania State University, where he serves as the Director of the Center for Machine Learning and Applications (CMLA). His research is focused on the intersection of Statistical Privacy, Machine Learning, and Computational Social Science. Dr. Kifer is widely recognized for his foundational work on Differential Privacy, particularly for his role as a key collaborator with the U.S. Census Bureau in developing the disclosure avoidance systems used for the 2020 Decennial Census. His research addresses the "no free lunch" reality of data privacy, creating rigorous frameworks like Pufferfish and L-diversity to protect individual identities in massive datasets. Beyond privacy, he applies deep learning to the physical sciences, including pioneering work in Computational Hydrology and predicting "labquakes" using physics-informed AI. A recipient of the ACM SIGMOD Test of Time Award and the IEEE ICDE Influential Paper Award, his work bridges the gap between abstract mathematical privacy definitions and the engineering required to protect national-scale data products.

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