Alan Yuille named Bloomberg Distinguished Professor at Johns Hopkins
The solutions to the world’s biggest challenges do not lie in any one discipline—they lie in the connections among big thinkers and bridge-builders. The four individuals appointed this week as Bloomberg Distinguished Professors represent this new generation of cross-specialty collaborators who pull insights from across the spectrum of human knowledge.
The Bloomberg Distinguished Professorships are the centerpiece of the university’s focus on strengthening its “capacity for faculty-led interdisciplinary collaboration” as outlined in President Ronald J. Daniels’ Ten by Twenty vision plan. A total of 50 endowed professorships, to be appointed over five years, are supported by a gift to the university by Johns Hopkins alumnus and former New York City Mayor Michael Bloomberg.
Bloomberg Distinguished Professors are affiliated with two or more Johns Hopkins University schools and divisions, conduct multidisciplinary research that furthers the university’s signature initiatives, and teach undergraduate students across the university.
Four Bloomberg Professors were announced in March, and now the addition of Jessica Fanzo, Taekjip Ha, Rong Li, and Alan Yuille brings to 14 the total of world-class faculty members dedicated to interdisciplinary scholarship.
“Our newest Bloomberg Distinguished Professors embody the promise of Mike Bloomberg’s visionary gift,” Daniels says. “These distinguished faculty join a cohort of scholars whose interdisciplinary approach to scholarship and research will strengthen meaningful collaboration across our divisions and advance solutions to fundamental challenges facing our society. We are delighted they will call Johns Hopkins their academic home.”
Krieger School of Arts and Sciences, Department of Cognitive Science
Whiting School of Engineering, Department of Computer Science
Mathematician and computer scientist Alan Yuille teaches robots to see—which also helps us understand the biology of vision.
“People think that seeing is easy because everybody can open their eyes and understand the world directly. When people started trying to create artificial intelligence, they thought vision was so easy that an MIT professor asked his student to solve it over the summer holidays,” Yuille says. Twenty years later, a computer program, Deep Blue, beat world chess champion Garry Kasparov, but computer vision systems were still unable to detect faces in images.
“Getting computers to understand images is really difficult,” says Yuille, whose research aims to develop mathematical models of vision and cognition that allow us to build computers that, when given images or videos, can reconstruct the three-dimensional structure of a scene.
His work will reach across the computer vision, vision science, and neuroscience communities at Johns Hopkins, particularly in the schools of Arts and Sciences and Engineering. “I’m looking forward to working across departmental boundaries to build a strong team of people who complement each other and work on the computer side and on the biology side of what I study,” says Yuille, whose research falls within the Science of Learning Institute signature initiative.
“There has been a lot of progress in vision in the last few years. There are now computer vision systems which, when applied to restricted domains, can perform better than humans. There is still a long way to go until a computer vision system can perform general purpose vision tasks as well as a human, but it will only be a matter of time,” he says.
Yuille comes to Hopkins from a full-time position in the Department of Statistics at UCLA, where he holds courtesy appointments in Psychology, Computer Science, and Psychiatry and serves as the director of the UCLA Center for Cognition, Vision, and Learning.
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