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Pietro Perona Neuromorphic systems engineer

Pietro Perona studies vision: how images are processed by brains and computers in order to obtain information about the environment. His research currently focuses on visual recognition, and how individuals detect and recognize objects and scenes in images. He is also interested in how machines could measure, represent and analyze animal and human behavior. Perona is known for his work on partial differential equations for image processing (the Perona-Malik equation), as well as his work on unsupervised learning of visual categories and the psychophysics and modeling of human scene perception.


Longuet-Higgins Prize for Fundamental Contributions to Computer Vision, 2013.

Outstanding Student Paper Award, Honorable Mention, NIPS 2010 (student author: P. Welinder), 2010.

Koenderink Prize for Fundamental Contributions in Computer Vision, European Conference on Computer Vision, 2010.

Best Paper Award, IEEE Computer Vision and Pattern Recognition Conference, 2003.

NSF National Young Investigator Award, 1994.

Relevant Publications

D. J. Anderson and P. Perona, “Toward a science of computational ethology.” Neuron, vol. 84, no. 1, pp. 18-31, 2014.

K. Chalupka et al, “Visual Causal Feature Learning,” arXiv preprint arXiv:1412.2309, 2014.

P. Perona, “Far and yet close: multiple viewpoints for the perfect portrait,” Art & Perception, vol. 1, no. 1-2, pp. 105-120, 2013.

P. Welinder et al, “The multidimensional wisdom of crowds,” Adv. Neur. In., vol. 23, pp. 2424-2432, 2010.

R. Fergus et al, “Object class recognition by unsupervised scale-invariant learning,” Computer Vision and Pattern Recognition, Proc. IEEE Comp. Soc., vol. 2, pp. II-264, June 2003.



Advisor Learning in Machines & Brains


California Institute of TechnologyDivision of Engineering and Applied Science


PhD University of California at Berkeley

DEng University of Padua


United States

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