Michael J. Franklin is the Morton D. Hull Distinguished Service Professor for the Department of Computer Science. He held the inaugural title of Liew Family Chair of Computer Science until September of 2023. An authority on databases, data analytics, data management and distributed systems, he also serves as Senior Advisor to the Provost on Computation and Data Science and is Faculty Co-Director of the Data Science Institute.
Previously, Franklin was on the faculty at the University of California, Berkeley, where he was Thomas M. Siebel Professor of Computer Science and served a term as Chair of the Computer Science Division. He was Director of the Algorithms, Machines and People Laboratory (AMPLab) and was Principal Investigator of the lab’s National Science Foundation CISE “Expeditions in Computing” award. He is one of the original creators of Apache Spark, a leading open source platform for data analytics and machine learning that was developed at the lab.
In addition to his academic work, Franklin founded and was chief technology officer of Truviso, a data analytics company acquired by Cisco Systems. He currently serves as a technical advisor to various data-driven technology companies and organizations, including AMPLab spin-out Databricks and Chicago-based Ocient. Franklin recently completed terms as a member of the NSF CISE Advisory Committee and a Board Member of the Computing Research Association and currently serves on the ACM Fellows Committee. He is a Fellow of the Association for Computing Machinery and a two-time recipient of the ACM SIGMOD (Special Interest Group on Management of Data) “Test of Time” award and numerous Best Paper awards at leading Systems and Database conferences. He received the Ph.D. in Computer Science from the University of Wisconsin in 1993, a Master of Software Engineering from the Wang Institute of Graduate Studies in 1986, and the B.S. in Computer and Information Science from the University of Massachusetts in 1983.
Research
Focus Areas: Big Data, Databases, Distributed and Streaming Database Technology, Systems
