Samuel Madden

Samuel Madden is the MIT College of Computing Distinguished Professor and CS Head in the Electrical Engineering and Computer Science (EECS) Department at Massachusetts Institute of Technology. He leads the Systems that Learn Initiative and co-directs both the Data Systems and AI Lab (DSAIL) and the Data Systems Group (DSG) at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL).

Professor Madden's research focuses on database systems, including main memory databases, data warehousing/analytics, querying video data, and machine learning for data systems. His recent projects include Palimpsest (a declarative system for optimizing AI workloads), BRAD (simplifying cloud data processing), self-organizing data containers, and VaaS (Video Analytics as a Service). His work bridges traditional database systems with emerging AI technologies, exploring how learned models can improve data management efficiency.

His research has resulted in numerous influential systems including C-Store (column-oriented database), CarTel (mobile sensor network system), BlinkDB (approximate query processing), Silo (main memory transaction processing), and Datahub (versioned data platform). These projects have significantly impacted both academic research and industry database technologies.

• ACM Fellow (2020)
• SIGMOD Edgar H. Codd Innovations Award (2024)
• Multiple 'Test of Time' awards from VLDB, SIGMOD, SIGMOBILE, and SenSys
• MIT TR35 Outstanding Researcher Under 35 (2005)
• Sloan Fellowship (2007)
• NSF CAREER Award (2005)

Professor Madden actively advises six current PhD students and has mentored numerous successful researchers who now hold positions at leading universities and tech companies. His lab maintains strong industry connections through the Data Systems and AI Lab, providing students with opportunities to work on real-world data challenges. As Chief Scientist of Cambridge Mobile Telematics and advisor to several database startups, he ensures his research remains grounded in practical applications while pushing the boundaries of database system design.

His recent publications demonstrate a clear trajectory toward tighter integration between AI and database systems, with significant contributions in cloud data infrastructure, video analytics, and learned query optimization techniques that are shaping the next generation of data management systems.