Anantha Chandrakasan

About

Anantha Chandrakasan is the Vannevar Bush Professor of Electrical Engineering and Computer Science at MIT and serves as the Provost. His research focuses on electrical engineering and computer science, particularly in areas related to electronic, magnetic, optical, and quantum materials and devices, as well as communications systems. His work leverages computational, theoretical, and experimental tools to develop groundbreaking sensors, energy transducers, new physical substrates for computation, and systems that address shared challenges facing humanity.

Research topics

• Nanotechnology
• Computer Science
• Materials science
• Chemistry
• Neuroscience
• Biochemistry
• Biology

Selected publications

• Low-thermal-budget synthesis of monolayer molybdenum disulfide for silicon back-end-of-line integration on a 200 mm platform

  Nature Nanotechnology · 2023-04-27 · 250 citations

  articleOpen access
  PublisherOA PDFDOI

• Multifunctional microelectronic fibers enable wireless modulation of gut and brain neural circuits

  Nature Biotechnology · 2023 · 134 citations

  Progress in understanding brain-viscera interoceptive signaling is hindered by a dearth of implantable devices suitable for probing both brain and peripheral organ neurophysiology during behavior. Here we describe multifunctional neural interfaces that combine the scalability and mechanical versatility of thermally drawn polymer-based fibers with the sophistication of microelectronic chips for organs as diverse as the brain and the gut. Our approach uses meters-long continuous fibers that can in…

  PublisherOA PDFDOI

• Sub-1.4 cm3 capsule for detecting labile inflammatory biomarkers in situ

  Nature · 2023 · 115 citations

  PublisherDOI

• FAB: An FPGA-based Accelerator for Bootstrappable Fully Homomorphic Encryption

  2023-02-01 · 114 citations

  article

  Fully Homomorphic Encryption (FHE) offers protection to private data on third-party cloud servers by allowing computations on the data in encrypted form. To support general-purpose encrypted computations, all existing FHE schemes require an expensive operation known as "bootstrapping". Unfortunately, the computation cost and the memory bandwidth required for bootstrapping add significant overhead to FHE-based computations, limiting the practical use of FHE.In this work, we propose FAB, an FPGA-b…

  PublisherDOI

• MEGA.mini: A Universal Generative AI Processor with a New Big/Little Core Architecture for NPU

  2025-02-16 · 10 citations

  articleSenior author

  The global AI market is growing explosively with the rise of generative AI applications, such as image manipulation and text-to-text/image/video creation. AI was primarily expected to automate only simple tasks like classification and data analysis. However, the advent of generative AI has transformed it into a creativity assistant, helping people think more creatively by offering new perspectives through deep neural networks (DNNs). As shown in Fig. 23.6.1, there are various types of DNNs used…

  PublisherDOI

Frequent coauthors

• C.G. Sodini

  Massachusetts Institute of Technology

  122 shared

• Teresa H. Meng

  Stanford University

  116 shared

• D.A. Johns

  University of Toronto

  116 shared

• Takayasu Sakurai

  The University of Tokyo

  116 shared

• Gary Baldwin

  116 shared

• Wanda Gass

  116 shared

• Jan Van Der Speigel

  University of California, Davis

  116 shared

• Lewis M. Terman

  Monash University

  116 shared

Awards & honors

• Vannevar Bush Professor of Electrical Engineering and Comput…

Similar researchers at Massachusetts Institute of Technology

• Manolis Kellish-164
• Antonio Torralbah-133
• Daniela Rush-129