Kimberly Tecce and Jane Halpern I Department of Mechanical Engineering and Department of Electrical Engineering and Computer Science (original article)
Original posting: January 31, 2022
Among the newly selected Fellows of the Institute of Electrical and Electronics Engineers (IEEE) are three members of the MIT community: Harry Asada, Ford Professor of Engineering in the Department of Mechanical Engineering; Luca Daniel, Professor of Electrical Engineering and Computer Science; and Devavrat Shah, the Andrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science. The IEEE, the world’s largest technical professional organization, annually confers the rank of Fellow on senior members whose body of work has advanced innovation in their respective fields and whose involvement in the engineering community has furthered the IEEE mission of promoting technology to benefit society.
Harry Asada is the Ford Professor of Engineering in the Department of Mechanical Engineering; and the Director of the Brit and Alex d’Arbeloff Laboratory for Information Systems and Technology, which focuses on the science and technology of robotic systems motivated by practical applications and scientific interests. Asada and his group seek effective solutions to socially and economically challenging problems, such as eldercare and workforce development. They’re also exploring the possibility of using living cells and organelles as components of robots. A recent project titled “Robot-on-the-Human: Supernumerary Robotic Limbs,” is a wearable robot perceived to be part of the human body. On the theoretical side, Asada has been exploring the use of Lifting Linearization for modeling complex nonlinear hybrid systems as a unified linear system in a high dimensional space. With over 180 journal publications, Asada has published extensively in peer-reviewed journals, refereed conference proceeding papers, 3 books, and holds 54 US patents for his work which have received broad recognition. Asada has also received several awards such as the ASME Rufus Oldenburger Medal, the Spira Award for Distinguished Teaching, the Henry Paynter Outstanding Researcher Award, and Fellow of the American Society of Mechanical Engineers acknowledging his teaching and research efforts. In addition to those honors, Asada earned 12 best paper awards, including the O. Hugo Schuck Best Paper Award from the American Control Council in 1985, Best Paper Awards at the IEEE International Conference on Robotics and Automation in 1993, 1997, 1999, and 2010, and Best Journal Paper Awards from the Society of Instrument and Control Engineers in 1979, 1984, and 1990. He was elevated to IEEE Fellow for “the design, modeling, and control of direct-drive robotic arms.”
Luca Daniel is a Professor of Electrical Engineering and Computer Science, a principal investigator in the Research Laboratory of Electronics (RLE), and a core faculty member in the Microsystems Technology Laboratories (MTL). His research interests include development of numerical techniques related to integral equation solvers, parameterized model order reduction, uncertainty quantification, inverse problems and robust optimization. His current areas of research interest include radio frequency (RF) circuits, silicon photonics, microelectromechanical devices, magnetic resonance imaging scanners, and robustness of deep neural networks architectures. He received his PhD in electrical engineering and computer science from UC Berkeley. Daniel has received best-paper awards from several Institute of Electrical and Electronics Engineers (IEEE) journals, including Transactions on Power Electronics, Transactions on Power Electronics, Transactions on Computer Aided Design, and Transactions on Components and Manufacturing. His work has also received 13 conference best-paper awards. Other honors include the IBM Corporation Faculty Award, the IEEE Early Career Award in Electronic Design Automation, and the Ruth and Joel Spira Award for Excellence in Teaching from the MIT School of Engineering. He also received best PhD thesis awards from both the Department of Electrical Engineering and Computer Sciences and the Department of Applied Mathematics at the University of California at Berkeley, as well as the Outstanding PhD Dissertation Award in Electronic Design Automation from the Association for Computing Machinery (ACM). He was elevated to IEEE Fellow “for contributions to modeling and simulation of electronic systems”.
Devavrat Shah is the Andrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science, and a member of both the Laboratory for Information and Decision Sciences (LIDS) and the Institute for Data, Systems and Society (IDSS). He was the founding director of the Statistics and Data Science Center (SDSC) at MIT from 2016 to 2020. Shah’s research focuses on statistical inference and stochastic networks, including resource allocation in communications networks; inference and learning on graphical models; algorithms for social data processing including ranking, recommendations, and crowdsourcing; and more recently causal inference. Shah received his bachelor’s degree in computer science and engineering from the Indian Institute of Technology in Bombay and his PhD in computer science from Stanford University. In 2013, he founded the machine learning start-up Celect, Inc., which helps retailers with optimizing inventory by accurate demand forecasting. In August 2019, Celect, Inc. was acquired by Nike, Inc. In 2019, Shah founded Ikigai Labs, with the mission of building self-driving organizations by empowering data business operators to make data-driven decisions with ease of spreadsheets. Shah’s work has received broad recognition, including the Rising Star Award from the Association for Computing Machinery (ACM) Special Interest Group for the computer systems performance evaluation community (SIGMETRICS); the Erlang Prize from the Applied Probability Society of INFORMS; the Best Publication Award from the Applied Probability Society of INFORMS; Best Paper Award from Manufacturing and Service Operations Management Society of INFORMS; INFOCOM Best Paper Award, N(eur)IPS Best Paper Award; and the ACM SIGMETRICS Best Paper Award. He has received the 2019 and 2020 ACM SIGMETRICS Test-of-Time Paper award, as well as the NSF CAREER Award, and he is a distinguished alumni of his alma mater IIT Bombay. He was elevated to IEEE Fellow “for contributions to network and information science, inference and machine learning”.