MIT Electrical Engineering and Computer Science Department Head Anantha Chandrakasan and Associate Department Heads Nancy Lynch, Asu Ozdaglar, and David Perreault announced in February 2017, the promotions of four faculty members in the department. Professors Thomas Heldt, Aleksander Madry, Daniel Sanchez, and Vivienne Sze are promoted to the rank of Associate Professor without Tenure, effective July 1, 2017.
Prof. Heldt explores the collection and use of high-resolution clinical data and mechanistic modeling of human physiology to improve the diagnosis and treatment of critically ill patients. His research at MIT is forging new and promising directions that will enable personalized health care. An example of Thomas’ ingenuity can be found in his recent work on the non-invasive measurement of intracranial pressure (ICP). Thomas developed a surprisingly robust physiological model and measurement approach that enables ICP to be very accurately estimated using only measurements that can be made non-invasively in the ICU. This new approach, which is already under clinical testing in three Boston hospitals, promises to have worldwide impact in reducing the substantial risk and trauma associated with existing ICP measurement methods. In addition to being an innovative scholar, Thomas is an exceptional educator, and has contributed to courses including Quantitative Systems Physiology (6.022J/HST 542), Cellular Biophysics (6.021J/HST 541) and Introduction to Communication, Control, and Signal Processing (6.011). In recognition of his tremendous strength as an educator, Thomas was awarded the Louis Smullin Prize for Teaching Excellence in 2015.
Prof. Madry works on designing efficient algorithms for fundamental optimization problems. His particular focus is on core graph problems, such as finding maximum flows and minimum cuts, computing matchings, and finding optimal routes. In many cases, he has improved upon decades-old bounds. A common theme behind much of his work is combining continuous optimization techniques, such as gradient descent, interior point methods, and linear system solving, with purely combinatorial techniques. Aleksander's work was recognized with four best paper awards at top conferences in theoretical computer science, honorable mention in the ACM Doctoral Dissertation competition, an NSF CAREER award, and a Sloan fellowship. He has developed a new graduate course on Graph Algorithms and Optimization, and has co-taught the Advanced Algorithms (6.854) and the Introduction to Algorithms (6.006) courses. Aleksander takes active role in organizing activities in the Theory of Computation group, and has served on several important departmental committees. He has also performed extensive service for the broader research community, including co-chairing a semestral program on continuous and discrete optimization at the Simons Institute for Theory of Computing, and co-founding the Interest Group on Algorithmic Foundations of Information Technology (IGAFIT) and Highlights of Algorithms (HALG) conference.
Prof. Sanchez designs efficient, sophisticated architectures for multicore computers, using analytical approaches. His work has addressed many issues involved in designing efficient parallel architectures, most importantly, design of highly efficient caching systems for multiprocessors. He has supervised many other projects containing innovative ideas, such as his COUP project, which exploits commutativity properties of operations to speed up processing in cache-coherent systems. His recent Swarm project develops a new model of multiprocessor computation, based on identifying dependencies between operations and exploiting this information to parallelize program execution. Daniel has won an NSF CAREER award, a Best Paper award for his commutativity work, and two “Micro Top Picks” awards for his work on caching and on Swarm. In addition, one of his PhD students, Nathan Beckmann, finished in 2015 and won the Sprowls award for outstanding PhD thesis in Computer Science. Daniel has taught our major courses in Computation Structures and Computer System Architecture, and earned high teaching ratings in both. He has also developed an Advanced Topics course for computer architecture. He has performed extensive service for the research community and for EECS.
Prof. Sze’s efforts focus on algorithms, circuits and systems for video coding, computer vision and machine learning – applications which are becoming extremely important to our information infrastructure. Vivienne develops new techniques that bridge algorithms and hardware design for these applications, enabling designs optimized across multiple layers of abstraction for greatly improved performance. Examples of her groundbreaking work include customized accelerators that provide substantial power reduction in computer vision and deep learning applications and new video coding algorithms that provide order-of-magnitude throughput improvements. She is widely recognized for her leading work in these areas, and has received many awards, including the DARPA Young Faculty Award, the 3M Corporation Non-Tenured Faculty Award and the AFOSR Young Investigator Award. Vivienne co-edited the book High Efficiency Video Coding (HEVC): Algorithms and Architectures. Her curricular contributions include the introduction of valuable new material into classes including 6.344, Digital Image Processing, and 6.374, Analysis and Design of Digital Integrated Circuits.