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Dr. Michael Franklin

Dr. Michael Franklin

Liew Family Chairman of Computer Science, Senior Advisor to the Provost for Computing and Data Science, The University of Chicago, USA  

Dr. Franklin is the inaugural holder of the Liew Family Chair of Computer Science. 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. Dr. Franklin most recently was the Thomas M. Siebel Professor of Computer Science and chair of the Computer Science Division of the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He co-founded and directed Berkeley’s Algorithms, Machines and People Laboratory (AMPLab), a leading academic big data analytics research center, The AMPLab won a National Science Foundation CISE "Expeditions in Computing" award, which was announced as part of the White House Big Data Research initiative in March 2012, and has received support from over 30 industrial sponsors. AMPLab has created industry-changing open source Big Data software including Apache Spark and BDAS, the Berkeley Data Analytics Stack.

At Berkeley he also served as an executive committee member for the Berkeley Institute for Data Science, a campus-wide initiative to advance data science environments. In addition to his academic work, Dr. Franklin founded and became chief technology officer of Truviso, a data analytics company acquired by Cisco Systems. He serves on the technical advisory boards of various data-driven technology companies and organizations.

Research Interests:
  • Big Data
  • Databases
  • Distributed And Streaming Database Technology
  • Systems
  • Doctor of Philosophy (PhD) in Computer Science, University of Wisconsin, Madison, USA
  • Master of Software Engineering, Wang Institute of Graduate Studies, Nashville, USA
  • Bachelors of Science in Computer and Information Science, University of Massachusetts, USA
  • M Zaharia, M Chowdhury, MJ Franklin, S Shenker, I Stoica, Spark: Cluster computing with working sets.HotCloud 10 (10-10), 95
  • M Zaharia, M Chowdhury, T Das, A Dave, J Ma, M McCauley, M Franklin Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing, Proceedings of the 9th USENIX conference on Networked Systems Design and Automation
  • S Madden, MJ Franklin, JM Hellerstein, W Hong, TAG: A tiny aggregation service for ad-hoc sensor networks, ACM SIGOPS Operating Systems Review 36 (SI), 131-146
  • SR Madden, MJ Franklin, JM Hellerstein, W Hong, TinyDB: an acquisitional query processing system for sensor networks, ACM Transactions on database systems (TODS) 30 (1), 122-173
  • S Chandrasekaran, O Cooper, A Deshpande, MJ Franklin, JM Hellerstein, TelegraphCQ: continuous dataflow processing, Proceedings of the 2003 ACM SIGMOD international conference
  • X Meng, J Bradley, B Yavuz, E Sparks, S Venkataraman, D Liu, Mllib: Machine learning in apache spark, The Journal of Machine Learning Research 17 (1), 1235-1241
  • S Acharya, R Alonso, M Franklin, S Zdonik: Broadcast disks: data management for asymmetric communication environments, Proceedings of the 1995 ACM SIGMOD International Conference
  • M Zaharia, RS Xin, P Wendell, T Das, M Armbrust, A Dave, X Meng, Apache spark: a unified engine for big data processing, Communications of the ACM 59 (11), 56-65
  • S Madden, MJ Franklin, JM Hellerstein, W Hong, The design of an acquisitional query processor for sensor networks, Proceedings of the 2003 ACM SIGMOD international conference
  • M Armbrust, RS Xin, C Lian, Y Huai, D Liu, JK Bradley, X Meng, T Kaftan, Spark sql: Relational data processing in spark, Proceedings of the 2015 ACM SIGMOD international conference