Three MIT computer science faculty members have been elected as companions of the Association for Computing Machinery (ACM).
The new companions are among 95 ACM members recognized as the top 1 percent for their unappropriated accomplishments in computing and information technology and/or unappropriated labor to ACM and the larger computing aggregation. Fellows are nominated by their peers with nominations reviewed by a illustrious choice 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 difference 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 companion for energy-efficient design methodologies and circuits that empower ultra-low-power wireless sensors and computing devices.
Alan Edelman is an applied mathematics professor for the Department of Mathematics the Applied Computing Group chief for the Computer Science and Artificial Intelligence Laboratory and co-founder of the Julia programming speech. His investigation includes high-performance computing numerical computation direct algebra haphazard matrix speculation and philosophical machine learning. He was recognized as a 2020 ACM companion for contributions to algorithms and speechs for numerical and philosophical computing.
Samuel Madden is the MIT Schwarzman College of Computing Distinguished Professor of Computing. Maddens investigation is in the area of database systems focusing on database analytics and question processing ranging from clouds to sensors to present high-performance server architectures. He co-directs the Data Systems for AI Lab start and the Data Systems Group investigating issues kindred 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 layer. He was recognized as a 2020 ACM companion for contributions to data treatment and sensor computing systems.