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Studer receives NSF CAREER Award

Christoph Studer, Cornell ECE Assistant Professor recently received a U.S. NSF CAREER Award from the Division of Computing and Communication Foundations (CFF).

Christoph Studer, Assistant Professor at the School of Electrical and Computer Engineering, recently received a U.S. NSF CAREER Award from the Division of Computing and Communication Foundations (CFF). The award supports his research proposal on “Hardware Accelerated Bayesian Inference via Approximate Message Passing: A Bottom-Up Approach” for a five-year period from 2017 to 2022 with a total amount of $606,661.

The planned research focuses on Bayesian inference, a powerful method for extracting statistical information from noisy, corrupted, or non-linear measurements. A growing number of applications rely on real-time Bayesian inference, mainly in the fields of wireless communications, imaging, and radar. While state-of-the-art algorithms for Bayesian inference have typically been designed for time-insensitive tasks, real-time applications commonly rely on simplistic (mostly linear) methods that prevent the use of accurate signal and measurement models. This disparity between recent theoretical advances, cutting-edge algorithms, and practical circuit realizations is mainly caused by the fast progress on the theory and algorithm sides and by the limited theoretical expertise of hardware designers.

Studer proposes to resolve the dichotomy between recent advances on the theory side and real-world hardware constraints by pursuing a bottom-up research approach in which hardware limitations drive efforts on the algorithm and theory levels. This unconventional research paradigm requires a joint consideration of the major challenges on all levels, which is the core expertise of Studer’s research group [link to: vip.ece.cornell.edu]. More specifically, the project will evaluate hardware approximations that are key for the design of efficient digital very-large scale integration (VLSI) circuits, investigate new algorithm transforms that enable more efficient hardware architectures, and analyze the impacts of the proposed circuit-level and algorithm-level optimizations on the inference complexity and quality.

The interdisciplinary nature of this project is also the unifying theme across planned educational outreach activities. Studer will lead hands-on design sessions for underrepresented minority high-school students via Cornell’s CATALYST Academy and will supervise undergraduates from South America with the goal of increasing student participation in interdisciplinary research.

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