Frank McSherry

Frank McSherry is an American computer scientist known for foundational work in differential privacy, streaming algorithms, and distributed dataflow systems. He has held research and engineering roles linking academic institutions and technology companies, contributing algorithms and systems that influenced data mining, cryptography, machine learning, and database management systems. His work intersects with a wide array of researchers and projects across theoretical computer science, systems engineering, and privacy-preserving computation.

Early life and education

McSherry received advanced training in computer science and related mathematical fields, studying topics that connect to figures and institutions such as Donald Knuth, Leslie Lamport, Edsger W. Dijkstra, University of California, Berkeley, Carnegie Mellon University, Massachusetts Institute of Technology, Stanford University, Harvard University, and Princeton University. His formative education exposed him to streams of research tied to Peter Shor, Whitfield Diffie, Martin Hellman, Ronald Rivest, and techniques related to probability theory, combinatorics, linear algebra, graph theory, and algorithmic game theory. Mentors and collaborators in his early period include scholars associated with SIGMOD, PODS, STOC, FOCS, and ICALP communities.

Career

McSherry's professional trajectory connects research labs, startups, and major technology firms, intersecting with organizations such as Microsoft Research, Google Research, Amazon, Twitter, LinkedIn, Facebook, Meta Platforms, Inc., Yahoo! Research, Bell Labs, IBM Research, and Bell Labs Innovations. He contributed to projects in distributed computation and data processing alongside engineers influenced by architectures like MapReduce, Apache Hadoop, Apache Spark, Flink, and Kubernetes. His career includes collaboration with teams that built systems referenced by Jeff Dean, Sanjay Ghemawat, Matei Zaharia, Michael Franklin, David DeWitt, and Ion Stoica. He has participated in conferences hosted by ACM, IEEE, USENIX, NeurIPS, ICML, KDD, and VLDB.

Research and contributions

McSherry's research advanced privacy and streaming paradigms, producing techniques used in contexts handled by practitioners connected to Alfred Aho, John Hopcroft, Leslie Valiant, Shafi Goldwasser, and Silvio Micali. He developed algorithms that intersect with the work of Cynthia Dwork, Aaron Roth, Kunal Talwar, Ilya Mironov, and Omer Reingold on privacy-preserving computation and cryptographic protocols. His contributions include methods relevant to stream processing influenced by concepts from Eugene W. Myers, Robert Sedgewick, Michael Rabin, and Ron Rivest. Systems-level results relate to dataflow and incremental computing paradigms akin to efforts by Paul Hudak, Robin Milner, Ken Thompson, and Dennis Ritchie in programming-language and systems design. His work on differential privacy and approximate query answering has been applied in settings discussed by Tim Berners-Lee, Vint Cerf, Jacob Nielsen, Doug Cutting, and Sergey Brin. He has influenced practical deployments touching areas engaged by Peter Norvig, Yoshua Bengio, Geoffrey Hinton, Yann LeCun, and Andrew Ng in machine-learning pipelines.

Awards and honors

McSherry's achievements have been recognized by communities and venues associated with honors similar to those awarded by ACM SIGMOD, IEEE Fellow, MacArthur Fellowship, Turing Award, Gödel Prize, Knuth Prize, ACM Fellowship, and named lectures at INRIA, Max Planck Institute, and CNRS-affiliated centers. Peer recognition includes invitations to present at NeurIPS, ICML, STOC, FOCS, PODS, and keynote roles at SIGMOD and VLDB events, alongside associations with award committees involving scholars like Shafi Goldwasser, Silvio Micali, Leslie Valiant, and Richard Karp.

Selected publications and projects

McSherry authored and coauthored influential papers and systems that are staples in literatures alongside publications by Cynthia Dwork, Aaron Roth, Kunal Talwar, Ilya Mironov, and Krzysztof Ostrowski. Notable contributions include work on differential privacy mechanisms comparable to studies published in venues frequented by Jairam Ranganathan, Eugene Wong, Michael Stonebraker, Hector Garcia-Molina, Jeffrey Ullman, and Jennifer Widom. He also led or contributed to projects intersecting with Apache Arrow, Apache Parquet, TensorFlow, PyTorch, NumPy, and Pandas ecosystems, and collaborated with engineers and researchers connected to Jeff Dean, Sanjay Ghemawat, Matei Zaharia, Michael Franklin, and Ion Stoica. His software and algorithmic innovations have been integrated with infrastructure used by Amazon Web Services, Google Cloud Platform, Microsoft Azure, and open-source communities around Linux Foundation projects.

Category:Computer scientists Category:Privacy researchers Category:Algorithms researchers