MIT Chair, MIT-IBM Watson AI Lab; Dean, MIT School of Engineering
Who they work with
Anantha Chandrakasan, PhD is dean of the MIT School of Engineering, the Vannevar Bush Professor of Electrical Engineering and Computer Science, and the MIT chair of the MIT-IBM Watson AI Lab. His research focuses on making electronic circuits more energy efficient. His early work on low-power chips for portable computers contributed to the development of today’s smartphones and other mobile devices. More recently, his research has addressed the challenge of powering even more energy-constrained technologies, such as the “internet of things” that would allow everyday devices to send and receive data via networked servers while using minimal power. Chandrakasan also leads the MIT Energy-Efficient Circuits and Systems Group, which focuses on energy harvesting, security hardware, and wireless charging for the internet of things; energy-efficient circuits and systems for multimedia processing; and platforms for ultra-low-power biomedical electronics.
Chandrakasan has served as conference chair of the IEEE International Solid-State Circuits Conference and has received the Solid-State Circuit Society’s Distinguished Service Award. He has also received a Semiconductor Industry Association University Researcher Award, the IEEE Donald O. Pederson Award in Solid-State Circuits, and a University of California, Berkeley, EE Distinguished Alumni Award. A fellow of IEEE, he was elected to the National Academy of Engineering and the American Academy of Arts and Sciences. He earned a BS, an MS, and a PhD in electrical engineering and computer sciences from UC Berkeley.
- Banerjee U., Ukyab, T. S., Chandrakasan, A. P. (2019). Sapphire: A Configurable Crypto-Processor for Post-Quantum Lattice-based Protocols. IACR Transactions on Cryptographic Hardware and Embedded Systems, vol. 2019, no. 4, pp. 17-61.
- Biswas A., Chandrakasan, A. P. (2018). CONV-SRAM: An Energy-Efficient SRAM With In-Memory Dot-Product Computation for Low-Power Convolutional Neural Networks. IEEE Journal of Solid-State Circuits, vol. 54, no. 1, pp. 217-230.
- Juvekar C., Vaikuntanathan, V., Chandrakasan, A. P. (2018). GAZELLE: A Low Latency Framework for Secure Neural Network Inference. Proceedings of the 27th USENIX Security Symposium, pp. 1651-1669.
- Price M., Glass, J., Chandrakasan, A. P. (2018). A Low-Power Speech Recognizer and Voice Activity Detector Using Deep Neural Networks. IEEE Journal of Solid-State Circuits, vol. 53, no. 1, pp.66-75.
- Feb. 20, 2020: MIT News, Cryptographic ‘tag of everything’ could protect the supply chain.
- January 28, 2020: MIT News, Accelerating the pace of engineering.
- January 6, 2020: MIT News, MIT School of Engineering and Takeda join to advance research in artificial intelligence and health.
- April 17, 2019: MIT News, Four from MIT elected to American Academy of Arts and Sciences for 2019.
- Feb. 20, 2019: IEEE Spectrum, Circuit secures the IoT against attack.