News

Anantha Chandrakasan named dean of MIT's School of Engineering

Alumnus Anantha Chandrakasan (B.S. '89/M.S. '90/Ph.D. '94), the Vannevar Bush Professor and head of the Department of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Techhnology (MIT), has been named dean of MIT's School of Engineering.  Anantha joined the MIT faculty in 1994 and has produced a significant body of research focused largely on making electronic circuits more energy efficient. His early work on low-power chips for portable computers helped make possible the development of today’s smartphones and other mobile devices.  While Chair, he initiated a number of student programs including Rising Stars, for graduate and postdoc women.

Ana Arias selected to receive the 2017 Research and Development award from FlexTech

Ana Arias has been selected for the 2017 Research and Development Award from the Flexible and Printed Electronics Conference, organized by FlexTech, a consortium that supports the development of flexible electronics. The FLEXIs recognizes outstanding work and achievements of organizations and individuals active in flexible hybrid electronics (FHE). The four categories include Research & Development, Innovation & Commercialization, Industry Leadership, and Education Leadership. Associate Professor Arias is recognized for the development of flexible medical sensors and printed flexible devices. Her breakthrough research has led to the creation of flexible receiving coils for magnetic resonance imaging devices and devices for impedance sensing for the detection of early pressure ulcers in vivo.

Alistair Sinclair named recipient of the 2017 ACM Distinguished Service Award.

Alistair Sinclair has been awarded the 2017 Association for Computing Machinery (ACM) Distinguished Service Award. This award is presented on the basis of value and degree of services to the computing community including activities on behalf of the ACM, other computer organizations, and/or other entities. Prof. Sinclair is recognized for his role in the spectacular success of the Simons Institute for the Theory of Computing in taking collaboration in the field to an entirely new level.

Jose Carmena in IEEE article titled “Timeline: The Evolution of Assistive Technologies”.

Jose Carmena is mentioned in IEEE’s publication The Institute in an article titled “Timeline: The Evolution of Assistive Technologies”  (see timeline at the bottom of the article, year 2017). In celebration of The Institute's 40th anniversary this article highlights topics and technologies  over the past four decades that have applied electronics to significantly help people overcome disabilities. Professor Carmena is recognized for contributions to the neural basis of motor-skill learning and neuroprosthetic systems. His research program in neural engineering and systems neuroscience is aimed at understanding the neural basis of sensorimotor learning and control, and at building the science and engineering base that will allow the creation of reliable neuroprosthetic systems for the severely disabled. He is also co-chair of the IEEE Brain Initiative and Co-Director of the Center for Neural Engineering and Prostheses at UC Berkeley and UCSF.

Aaron Wagner selected winner of the IEEE 2017 James L. Massey Research and Teaching award.

EECS alumni Aaron Wagner has been selected as the winner of the 2017 James L. Massey Research and Teaching award for young scholars of the IEEE Information Theory Society. This award recognizes outstanding achievement in research and teaching by Society members under 40 years of age in the Information Theory community. Currently he is an Associate Professor in the School of Electrical and Computer Engineering at Cornell University. He received his Ph.D. in Electrical Engineering and Computer Sciences in 2005 (advisor was Prof. Venkatachalam Anantharam). His current research interests are at the intersection of information theory and other fields including networking, statistics, queueing theory, security, computational linguistics, and learning. He is particularly interested in network information theory, distributed compression and its application to peer-to-peer networks, secure communication over timing and photonic channels, and communication and classification in learning-limited environments.

Warren Hoburg selected by Nasa for 2017 Astronaut Candidate Class

EECS alumni Warren Hoburg has been selected by NASA to be one of 12 people to join the 2017 Astronaut Candidate Class. Hoburg received his Ph.D. in Electrical Engineering and Computer Sciences in 2015 (advisor Associate Prof. Pieter Abbeel). Currently he is a Boeing Assistant Professor in the Department of Aeronautics and Astronautics Center for Computational Engineering Operations Research Center at the Massachusetts Institute of Technology (MIT). His research focuses on efficient methods for design of engineering systems. His group produced and maintains the open-source software tool GPkit, which is a Python package for geometric programming. His group's tools were used to design a five-day endurance UAV currently under development for the US Air Force.

Wenting Zheng wins the 2017-18 IBM PhD Fellowship

EECS graduate student Wenting Zheng (advised by Ion Stoica and Raluca Ada Popa)  has won the prestigious 2017-18 IBM PhD Fellowship.   Wenting works in the RISELab and her research involves system security and distributed systems. The IBM Ph.D. fellowship is an "intensely competitive worldwide program that honors exceptional Ph.D. students who have an interest in solving problems that are important to IBM and fundamental to innovation in many academic disciplines and areas of study." Only 50 fellowships are awarded worldwide annually.

Sculpted Light in the Brain

In an effort to gather scientists at the interface between neurosciences, optical engineering, and computer science, an all-day conference is being held on Friday, June 9, in Stanley Hall titled Sculpted Light in the Brain.   Participants are united in their mission to develop technologies to enable real time optical communication with the living brain.  The endeavor was initiated with a $2k seed grant to the California Institute for Quantitative Biosciences (QB3) and grew into a $20K showcase for the collaboration between neuroscientists, electrical engineers, and computer scientists, highlighting U.C. Berkeley's position as a preeminent leader in brain research. The conference, which already has a 50 person waitlist, will host 11 fully funded speakers including EE Associate Prof. Laura Waller,  present 25 posters, and is supported by a dozen corporate sponsors .

Armando Solar-Lezama: Academic success despite an inauspicious start

Alumnus and Mexican immigrant Armando Solar-Lezama (CS Ph.D. '08) is the subject of an MIT News article describing some of the academic obstacles he had to overcome on his path to becoming a tenured professor at MIT.  Armando's creative  approaches to his class assignments were discouraged in Mexico and despite self-educating to narrow the gap, he experienced systematic repression in high school when he moved to Texas with his family in 1997.  After he graduated from Texas A&M, he was welcomed into the Berkeley EECS graduate program.  Under the mentorship of Prof. Ras Bodik, Armando discovered the nascent area of "program synethesis," which has since blossomed into a popular field of research.  Read about Armando's challenging and inspiring journey.

Meet the most nimble-fingered robot yet

Many researchers are working on ways for robots to learn to grasp and manipulate things by practicing over and over, but the process is very time-consuming. The research work on robotic deep learning by Prof. Ken Goldberg is featured on the cover of MIT Review in an article titled "Meet the Most Nimble-Fingered Robot Yet".  Instead of practicing in the real world, Prof. Ken Goldberg and colleagues have developed a robot that learns by feeding on a data set of more than a thousand objects that includes their 3-D shape, visual appearance, and the physics of grasping them. This data set was used to train the robot’s deep-learning system. Advances in control algorithms and machine-learning approaches, together with new hardware, are steadily building a foundation on which a new generation of robots will operate.