Three MIT faculty elected 2020 ACM Fellows

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Three MIT computer science faculty members have been elected as fellows of the Association for Computing Machinery (ACM).

The new fellows are among 95 ACM members recognized as the top 1 percent for their outstanding accomplishments in computing and information technology and/or outstanding service to ACM and the larger computing community. Fellows are nominated by their peers, with nominations reviewed by a distinguished selection committee.

Anantha Chandrakasan is dean of the School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science. He leads the MIT Energy-Efficient Circuits and Systems Group, which works on a variety of projects such as ultra-low-power internet-of-things devices, energy-efficient processors, machine learning processors, hardware security for computing devices, and wireless systems. He was recognized as a 2020 ACM fellow for energy-efficient design methodologies and circuits that enable ultra-low-power wireless sensors and computing devices.

Alan Edelman is an applied mathematics professor for the Department of Mathematics, the Applied Computing Group leader for the Computer Science and Artificial Intelligence Laboratory, and co-founder of the Julia programming language. His research includes high-performance computing, numerical computation, linear algebra, random matrix theory, and scientific machine learning. He was recognized as a 2020 ACM fellow for contributions to algorithms and languages for numerical and scientific computing.

Samuel Madden is the MIT Schwarzman College of Computing Distinguished Professor of Computing. Madden’s research is in the area of database systems, focusing on database analytics and query processing, ranging from clouds to sensors to modern high-performance server architectures. He co-directs the Data Systems for AI Lab initiative and the Data Systems Group, investigating issues related to systems and algorithms for data focusing on applying new methodologies for processing data, including applying machine learning methods to data systems and engineering data systems for applying machine learning at scale. He was recognized as a 2020 ACM fellow for contributions to data management and sensor computing systems.