I study the problem of object recognition in complex visual scenes, focusing on biologically realistic computational models and on the problem of invariance, particularly scale invariance. My goals are to improve the performance of computational models on complex visual tasks and, from the parameters of models which work well, be able to predict computational characteristics of cells in the primate ventral visual pathway.
I am a Ph.D. student in the department of Brain and Cognitive Sciences at MIT, where I work in the Center for Biological and Computational Learning.
I have a masters degree from the University of British Columbia in Vancouver, Canada, where I was part of the Laboratory for Computational Intelligence in the department of Computer Science. Most of my masters research, supervised by David Lowe, was based on a biologically-inspired model of visual object recognition.
T. Poggio, J. Mutch, F. Anselmi, L. Rosasco, J.Z. Leibo, and A. Tacchetti The computational magic of the ventral stream: sketch of a theory (and why some deep architectures work). MIT-CSAIL-TR-2012-035, Massachusetts Institute of Technology, Cambridge, MA, December 29, 2012. [pdf]
H. Jhuang, E. Garrote, J. Mutch, X. Yu, V. Khilnani, T. Poggio, A.D. Steele, and T. Serre Automated home-cage behavioural phenotyping of mice. Nature Communications, 1, Article 68, [doi: 10.1038/ncomms1064], September 7, 2010. [pdf]
(Note: while CNS is designed for use with a GPU, it can also run -- much more slowly -- without one.)