Aravindan Vijayaraghavan receives NSF honor for young faculty
Computer scientist develops new algorithmic techniques for machine learning and discrete optimization
EVANSTON - Northwestern University’s Aravindan Vijayaraghavan, a theoretical computer scientist, has received a Faculty Early Career Development Program (CAREER) award from the National Science Foundation (NSF).
The foundation’s most prestigious honor for junior faculty members supports early career development of individuals who exemplify the role of teacher-scholar through outstanding research, excellent education and the integration of education and research.
Vijayaraghavan will receive $505,251 over five years from NSF’s Division of Computing and Communication Foundations. His interests are in designing efficient algorithms with provable guarantees for common computational problems that arise when extracting structure from large amounts of data. Typical problems in this domain are inherently hard from a computational standpoint. These include combinatorial problems like partitioning and clustering (grouping a set of items by relatedness) and fitting statistical models for classifying and representing datasets.
“I am very honored and grateful to the NSF for supporting basic science research on both theory and practice of algorithms in different areas of computer science,” said Vijayaraghavan, assistant professor of electrical engineering and computer science in the McCormick School of Engineering.
A large disconnect between theoretical and practical understanding of many computational problems exists in machine learning, operations research and data analysis. With his CAREER support, Vijayaraghavan will focus on bridging the fundamental gap between theory and practice by developing paradigms and machinery that will allow researchers to understand and reason about the performance of algorithms on real-world instances.
Vijayaraghavan’s project will lead to more general models for real-world instances and new algorithmic techniques for problems such as clustering, graph partitioning and learning popular latent variable models.
The work also will integrate aspects of average-case analysis in both graduate and undergraduate courses and will include outreach activities in high schools in Evanston and the broader Chicago area on algorithmic thinking.