Newly tenured faculty are: (clockwise from top left) Dirk Englund, Yury Polyanskiy, and Adam Chlipala (all EECS), Kenneth Kamrin (MechE), Qiqi Wang (AeroAstro), and David Sontag, and Vinod Vaikuntanathan (both EECS).
School of Engineering / EECS Staff
Five EECS faculty members are among seven from the School of Engineering who have received tenure from MIT. The five – Adam Chlipala, Dirk Englund, Yury Polyanskiy, David Sontag, and Vinod Vaikuntanathan – are joined by Ken Kamrin in the Department of Mechanical Engineering and Qiqi Wang in the Department of Aeronautics and Astronautics.
"I am proud to announce this year’s cohort of newly tenured faculty in the School of Engineering,” said Anantha Chandrakasan, dean of the School of Engineering. “Their work as scholars and educators is inspiring to our entire community. We will benefit immensely from their work.”
Following are profiles of the newly tenured EECS faculty members:
Adam Chlipala is a leader in the emerging area of integrated software design and verification. His contributions include building general computational infrastructure (based on the Coq proof-management system) to support programming, formal verification, and automatic code generation, as well as applications to verification of many types of software and hardware systems.
Specific contributions include the Bedrock system for specifying and verifying software designs, the Fiat framework for automatic code generation, the FSCQ project for verifying file systems, the Kami system for verifying hardware designs, and the Fiat elliptic curve cryptography library. Google recently adopted Chlipala’s Fiat cryptographic library for its Chrome browser, and his formal specification for the RISC-V processor was adopted as the official specification for the RISC-V instruction set architecture. He is a principal investigator in the Computer Science and Artificial Intelligence Laboratory (CSAIL).
Chlipala helped develop the new undergraduate Fundamentals of Programming course (6.009), developed a new graduate course on Foundations of Program Analysis (6.820), and actively participates in EECS graduate admissions. He has received a National Science Foundation (NSF) CAREER Award, a Symposium on Operating System Principles (SOSP) best-paper award, and two Communications of the Association for Computing Machinery (CACM) research highlights
In his research, Dirk Englund focuses on developing of solid-state photonic and quantum devices and systems and their use in quantum computation, communications, and sensing. His work emphasizes leveraging deep insight in quantum information theory and optics to develop engineered systems that dramatically advance the field. His stated vision is ambitious: to create the quantum internet, where entanglement is distributed worldwide. Significant contributions range from achieving record performance with a practical high-dimensional quantum key distribution scheme to performing quantum transport simulations using photonic integrated circuits. He leads the Quantum Photonics Laboratory in the Research Laboratory of Electronics (RLE).
Englund has taught a variety of EECS classes, including Introduction to EECS (6.01), Oral Communication (6.UAT), and Seminar in Undergraduate Advanced Research (6.UAR, the SuperUROP course). He also helped create the EECS Communications Lab, which provides resources for graduate students for oral and written communications, and, with Vinod Vaikuntanathan, has co-directed Masterworks, the department’s annual celebration of research leading to the SM and MEng degrees. His awards include a Sloan Research Fellowship in Physics, the Presidential Early Career Award for Scientists and Engineers, and the Optical Society of America Adolph Lomb Medal, the top award for a young researcher in optics.
Yury Polyanskiy is a well-known theorist who works on information processing systems that arise in communication, control, and learning. He is widely known for his pioneering work on finite blocklength information theory. His work developed fundamental results in non-asymptotic information theory, providing tight lower and upper bounds for the capacity of a given blocklength. He also has important contributions in a broad set of areas including properties of information measures, discrete geometry and combinatorics, and statistical learning theory. The tools and relations he developed for information measures enabled him to settle long-standing conjectures in network information theory and address fundamental questions in control and high-dimensional statistics.
Polyanskiy is a member of the Laboratory for Information and Decision Sciences (LIDS), the Institute for Data, Systems, and Society (IDSS), and the Statistics and Data Science Center (SDSC). He has taught the undergraduate courses Introduction to EECS (6.02) and the SuperUROP course (6.UAR). He is currently co-developing a new foundation-level class, Introduction to Data Science (6.S077). He also teaches the graduate classes Information Theory (6.441) and Fundamentals of Probability (6.436), and he co-developed a new graduate class, Tools of Discrete Probability (6.265).
Awards include an NSF CAREER Award, the IEEE Information Theory best-paper award, IEEE International Symposium on Information Theory (ISIT) best student-paper awards (twice), and the EECS Jerome H. Saltzer teaching award.
David Sontag focuses on research in machine learning and applying machine learning to health care. In machine learning, he focuses on graphical models, which provide a mathematically rigorous and computationally efficient way to represent dependencies between a hidden (latent) structure and observations. His contributions include new highly efficient algorithms for learning, inference, and prediction with graphical models from real world data, and theoretical results in a form of error bounds and correctness proofs that establish a new framework for theoretical analysis of approximate and exact learning and inference in graphical models.
Sontag is a pioneer in applying machine learning expertise to health care, where he has significantly advanced the state of the art in building predictive models from electronic medical records. His expertise in clinical decision making has led him to build novel formulations of machine-learning problems. The analytical tools and algorithms he develops to solve those problems are advancing machine-learning fundamentals and having an impact beyond health care.
Sontag is also the Hermann L. F. von Helmholtz Career Development Assistant Professor in the Institute for Medical Engineering and Science (IMES) and principal investigator in CSAIL. His honors include an NSF CAREER Award, faculty awards from Google, Facebook, and Adobe, several best-paper awards, and the EECS George M. Sprowls Award for Best PhD Thesis.
Vinod Vaikuntanathan, a leader in theoretical security, focuses on techniques for storing, accessing, and computing with encrypted data. His best-known work is on Fully Homomorphic Encryption, Functional Encryption, and Program Obfuscation, among others. His work involves establishing clear mathematical definitions of security properties, identifying key hardness assumptions on which to base security claims, devising new algorithms, and proving their security properties. Most of his work uses Lattice-Based Cryptography, which is based on hardness assumptions for problems involving integer lattices; such assumptions might be justified even in a future with powerful quantum computers. He is a principal investigator in CSAIL.
Vaikuntanathan regularly teaches Introduction to Algorithms (6.006) and Analysis of Algorithms (6.046), and also teaches graduate cryptography courses. He has served on the EECS graduate admissions committee and, with Dirk Englund, co-directed the department’s annual EECS Masterworks poster session.
His awards include an NSF CAREER Award, a Sloan Research Fellowship, a Microsoft Research faculty fellowship, and an EECS Ruth and Joel Spira Award for teaching. Most recently, in April 2018, he received MIT’s annual Harold E. Edgerton Award for Faculty Achievement, presented to junior faculty for outstanding research, teaching, and service.
For additional details, visit the MIT News website.