New Faculty Profiles

Scott Aaronson

Scott Aaronson

Scott Aaronson joins the EECS Department as an Assistant Professor and member of the Computer Science and Artificial Intelligence Laboratory. A dropout from Council Rock High School in Newtown,
Pennsylvania, Scott went on to receive a bachelor’s degree in computer science from Cornell University and a PhD in computer science from UC Berkeley in 2004. Most recently he held postdoctoral fellowships at the Institute for Advanced Study in Princeton and the Institute for Quantum Computing in Waterloo, Canada.

Scott’s research centers on the limits of quantum computers—and more generally, on what can and can’t be efficiently computed in the physical world. For example, he’s studied the possibility of quantum computers breaking cryptographic hash functions in polynomial time, the computational complexity of hidden variable theories, and the power of quantum computers with closed timelike curves. His work was recognized by a Best Student Paper Award at the ACM Symposium on Theory of Computing (STOC), and by two Best Student Paper Awards at the IEEE Conference on Computational Complexity.

Outside of research, Scott is widely known for his blog “Shtetl-Optimized,” as well as for the Complexity Zoo, an online reference for over 460 computational complexity classes. He also enjoys reading, international travel, ping-pong, and salsa dancing (though he’s terrible at the last two).

Antonio Torralba

Antonio Torralba

Antonio Torralba joined the EECS Department in August 2007 as an Assistant Professor and a member of the Computer Science and Artificial Intelligence Laboratory. After high school on the warm island of Mallorca, Spain, he moved to Barcelona, where he earned a degree in telecommunications engineering from the Technical University of Catalonia (Spain) in 1994. He received his PhD in image and signal processing from the Institut National Polytechnique, Grenoble, France, in 2000.

Then, following a negative gradient of temperatures, he moved to Boston as a postdoctoral fellow at MIT. In 2004, he became a research scientist at the Computer Science and Artificial Intelligence Laboratory. His research combines innovation from computer vision, machine learning and human perception to work towards the goal of developing robust artificial systems that can see and interpret the visual world. He is interested in developing computer vision systems and understanding human visual capabilities that are useful to real-world applications.

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