The research in the Poggio Lab is guided by the belief that learning is at the core of the problem of intelligence, both biological and artificial. Learning is thus the gateway to understanding how the human brain works and for making intelligent machines. Thus, Poggio Lab studies the problem of learning within a multidisciplinary approach. Current research in the Poggio Lab is relevant not only for understanding higher brain function, but also for the mathematical and computer applications of statistical learning. Three basic directions of research in his group are: mathematics of statistical learning theory, engineering applications (in computer vision, computer graphics, bioinformatics and intelligent search engines) and neuroscience of visual learning. (1) In the theory domain, he has focused on the foundations of learning theory and on a formal characterization of necessary and sufficient conditions for predictivity of learning. (2) The engineering applications include bioinformatics projects, computer vision for scene recognition and trainable, man-machines interfaces. (3) In the computational neuroscience area, his research is centered on object recognition and, in particular, on a quantitative theory of the ventral stream in the visual cortex underlying object recognition and object categorization. The theory and its computer implementation has become a tool for analyzing, interpreting and planning experiments in extensive collaborations with experimental neuro-scientists. This should lead to a better and more coherent understanding of the neural mechanisms of visual recognition and of the normal and abnormal functions of the cortex.