Non-Commutative Harmonic Analysis in Machine Learning

Risi Kondor, Caltech

Visualization tools are essential for deriving meaning from the avalanche of data we are generating today. To facilitate an understanding of the complex relationships embedded in this data, visualization research leverages the power of the human perceptual and cognitive systems, encoding meaning through images and enabling exploration through human-computer interactions. In my research I design visualization systems that support exploratory, complex data analysis tasks by biologists who are analyzing large amounts of heterogeneous data. These systems allow users to validate their computational models, to understand their underlying data in detail, and to develop new hypotheses and insights. My research process includes five distinct stages, from targeting a specific group of domain experts and their scientific goals through validating the efficacy of the visualization system. In this talk I’ll describe a user-centered, methodological approach to designing and developing visualization tools and present several successful visualization projects in the areas of genomics and systems biology. I will also discuss the long term implications

Speaker Biography

Risi Kondor obtained his B.A. in Mathematics from the University of Cambridge. After some further studies in Physics and Computational Fluid Dynamics, he changed direction to Machine Learning, getting his M.Sc. from CALD (the precursor to the Machine Learning Department) at Carnegie Mellon University in 2002, and his Ph.D from Tony Jebara’s group at Columbia University in 2007. His first post-doc took him to London, where he worked at the Gatsby Computational Neuroscience at UCL, and he is now completing his second post-doc at the Center for the Mathematics of Information at Caltech.