Mike Franklin - Databricks

Mike Franklin

Professor of Computer Science, UC Berkeley

Professor Franklin is a Professor of Computer Science at UC Berkeley, specializing in large-scale data management infrastructure and applications (these days called “Big Data”). He works primarily in the Database (DB) and Operating Systems and Networking Technology (OSNT) areas. He is currently Director of the Algorithms, Machines and People Lab (AMPLab) – an industry and government-supported collaboration of students, postdocs, and faculty who specialize in data management, cloud computing, statistical machine learning and other important topics necessary for making sense of vast amounts of varied and unruly data. He is also a founder and was the CTO of Truviso, a high-performance analytics software company in Foster City, CA, which was acquired by Cisco (CSCO) in Spring 2012.
Professor Franklin holds the Thomas M. Siebel Chair in Computer Science at UC Berkeley. He won the IBM Faculty Award and the Google Faculty Research Grant in 2009. He was named ACM Fellow in 2005. He won the ACM Service Award in 2002 and the Okawa Foundation Research Grant, as well as the Siemens Faculty Development Grant in 2000. In 1995, he won the National Science Foundation CAREER Award. He completed the B.S. in Computer and Information Science at the University of Massachusetts, Amherst in 1983; the M.S.E. at the Wang Institute of Graduate Studies in 1986, and his Ph.D. at the University of Wisconsin, Madison in 1993.

UPCOMING SESSIONS

PAST SESSIONS

What’s Next for BDAS?Summit 2014

The AMPLab at Berkeley is the home of the Berkeley Data Analytics Stack (BDAS) and the birthplace of Spark, which serves as a central component of BDAS. Supported by 24 leading IT companies and an Expeditions in Computing award announced by the White House in 2012, the AMPLab is continuing to explore and expand the scope of Big Data research and platforms. In this talk, I’ll layout our future directions for BDAS and discuss some of our on-going research in topics such as Machine Learning and Data and Model Serving.