Carnegie Mellon University
February 23, 2016

Carnegie Mellon Professors Win Sloan Fellowships

Mathematical Sciences Assistant Professor Wesley Pegden is One of Two Recipients from CMU

By Jocelyn Duffy

Carnegie Mellon Professors Win Sloan Fellowships

Mathematical Sciences Professor Wesley PegdenMathematical Sciences Professor Wesley Pegden

Two Carnegie Mellon University faculty members have been awarded 2016 Sloan Research Fellowships. Mathematician Wesley Pegden and computer scientist Abhinav Gupta are among 126 early-career scientists and scholars from 52 colleges and universities in the U.S. and Canada who will receive $55,000 to further their research.

“Getting early-career support can be a make-or-break moment for a young scholar,” said Paul L. Joskow, president of the Alfred P. Sloan Foundation. “In an increasingly competitive academic environment, it can be difficult to stand out, even when your work is first rate. The Sloan Research Fellowships have become an unmistakable marker of quality among researchers. Fellows represent the best-of-the-best among young scientists.”

Pegden, an assistant professor in the Department of Mathematical Sciences, studies problems in combinatorics, including discrete geometry, probabilistic combinatorics and graph theory. Among other things, Pegden and colleagues have established a new paradigm for understanding the Abelian Sandpile Process from statistical mechanics, and have developed new perspective on the hardness of geometric cases of the Traveling Salesman Problem.

Gupta, an assistant professor of robotics, specializes in computer vision and large-scale visual learning. His research interests include developing methods for computers to gain a deep understanding of visual scenes, including how elements of the scene relate to each other physically and functionally. He also studies the role of language in visual learning and how people interact with their environments. Among the research projects he leads is the Never Ending Image Learner (NEIL), in which a computer program constantly searches the Web for images, doing its best to identify objects and characterize scenes on its own.