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Faculty promotions announced in EECS

Five faculty are promoted to full professor; three faculty promoted to associate professor.
Five EECS faculty have been promoted to full professor: Elfar Adalsteinsson, Luca Daniel, Polina Golland (top row, l-r), Jing Kong, and Antonio Torralba (second row, l-r). Three EECS faculty have been promoted to associate professor: Adam Chlipala, Yury Polyanskiy, and Vinod Vaikuntanathan (bottom row, l-r).
Five EECS faculty have been promoted to full professor: Elfar Adalsteinsson, Luca Daniel, Polina Golland (top row, l-r), Jing Kong, and Antonio Torralba (second row, l-r). Three EECS faculty have been promoted to associate professor: Adam Chlipala, Yury Polyanskiy, and Vinod Vaikuntanathan (bottom row, l-r).
Images courtesy of EECS.

Department of Electrical Engineering and Computer Science (EECS) head Anantha Chandrakasan and associate department heads Bill Freeman, Silvio Micali, and David Perreault announced in February the promotions of eight faculty members in the department. Professors Adalsteinsson, Daniel, Golland, and Torralba are promoted to full professor, while professors Chilpala, Polyanskiy, and Vaikuntanathan are promoted to associate professor. The promotions are effective July 1.

Elfar Adalsteinsson is a world leader in the development and application of magnetic resonance imaging (MRI). The primary goal of his research is to advance the physics and engineering of MRI to develop new methods and instruments that can be deployed in the clinic. A remarkable feature of his work is that it spans the spectrum from studies of the basic physics of MRI and the development of enabling technologies to testing and application of new imaging modalities in the clinic. For example, he has been a leader in both the development of parallel RF transmission to realize improved MRI scanning and in development of methods for measuring parameters associated with oxygenation in the brain. He is also an excellent teacher, and has been responsible for both developing an MRI course for graduate students and senior undergraduates, 6.556/HST.580, and — along with his colleagues — co-leading development of the new undergraduate course 6.S02 (Introduction to EECS from a Medical Perspective).

Luca Daniel works on computational techniques for modeling and design of complex systems, including for microsystems (for example, integrated circuit modeling and design) and biomedical applications (electromagnetic analysis for magnetic resonance imaging, for example). His research encompasses the development of computationally-efficient integral equations solvers (e.g., “field solvers”), parameterized model-order reduction techniques and methods for uncertainty quantification, and their embodiment in useful software tools. His work has been widely recognized, most recently with a 2014 best-paper award from the IEEE Transactions on Computer Aided Design, and he is having tremendous practical impact in applications such as rapid electromagnetic field prediction for MRI scanners. He has also made key educational contributions, including heavily updating and expanding the content of MIT’s flagship class on numerical simulation, 6.336J (Introduction to Numerical Simulation) and working with colleagues to revamp the department's undergraduate header course 6.013 (Electromagnetics and Applications).

Polina Golland studies the shapes and functions of biological structures through the statistical analysis of biomedical images. She builds computational models of the anatomical and functional variability within populations, and develops methods to detect and characterize changes in those distributions under the influence of development or disease. Her models give insight into the functional organization of the brain and into the causes of its variability.  Her group releases open-source software packages for wide impact and dissemination. Golland has played a major role in developing three important classes in the EECS curriculum: two very popular graduate classes on inference and information — 6.437 (Inference and Information) and 6.438 (Algorithms for Inference) — and the department’s new undergraduate class 6.008 (Introduction to Inference).

Jing Kong is an expert on the synthesis of low-dimensionality (1-D and 2-D) materials using chemical vapor deposition (CVD). For example, her widely-cited work at MIT on CVD growth of single- and few-layer graphene films is considered foundational, and has led to the ability to grow large-area high-quality graphene films and transfer them onto arbitrary substrates, assisting the explosive growth in the field. She is making similarly important contributions to CVD synthesis of few-layer hexagonal boron nitride and transition-metal dichalcogenides such as molybdenum disulfide. Moreover, through extensive collaborations, she is having substantial impact on the engineering application of these new materials in many kinds of systems. Kong is also a highly dedicated teacher who is liked by students and colleagues alike, and has contributed to the new undergraduate / graduate class 6.096 / 6.975 (Introduction to Nanoelectronics), and created a new graduate-level class, 6.976 (Science, Technology and Applications of Carbon Nanoelectronics).

Antonio Torralba has received wide recognition for pointing out the importance of context for object recognition — that the objects near another object help us to recognize the object itself — and developing techniques to exploit context. In collaboration with Aude Oliva, he introduced “scene recognition” as an area of study within computer vision, and developed a representation designed to capture such contextual information. Torralba developed several widely-used datasets that help to advance the field. Since tenure, he has continued with very strong research contributions, deepening his work on scene understanding, analyzing image features, studying database bias, and exploring computational photography. His teaching is strong, and he has contributed new courses within his research area, and to the department’s large intro-level class, 6.01 (Introduction to EECS).

Adam Chlipala's research addresses the software development process, applying formal logic to prove programs are correct using a computer proof assistant. He aims to reduce the human cost of program verification so that it may one day become a standard part of software development. This research could ultimately increase the reliability and security of software. He received an National Science Foundation CAREER Award, and he has written a book that is highly regarded and widely used within the program verification community. He has also developed a new class on interactive theorem proving.

Yury Polyanskiy works in the area of information theory, including topics such as finite blocklength coding, strong data-processing inequalities, combinatorial and geometrical aspects of Hamming spaces. His pioneering work on channel dispersion, which captures the variation of the realized data rate over a channel, has been particularly impactful, and has opened up a new field of research in information theory. He is the winner of several awards including the prestigious Information Theory best paper award and the National Science Foundation CAREER Award. In addition to excellent undergraduate teaching, Polyanskiy has made contributions to both 6.441 (Information Transmission) and 6.436 (Fundamentals of Probability).

Vinod Vaikuntanathan studies cryptography, a topic of ever-increasing importance in modern society. He has made breakthroughs that bring us much closer to being able to compute on encrypted data, important for secure cloud computing, as well as in functional cryptography, the ability to share only some parts of an encrypted system. He has won numerous awards, including a Sloan Research Fellowship, a National Science Foundation CAREER award, and a Microsoft Faculty Fellowship. He is a gifted teacher, and he has a strong record of service within his research community.

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