Grant: Discovery Grants Program - Individual

In recent years, there has been a massive increase in computational power and the amount of data available from the web, video cameras, high-throughput genomic sequencing technologies, and various laboratory measurements. Building intelligent systems that can automatically discovery meaningful representations from such data, should lead to new scientific discoveries. For example, they can help neuroscientists analyze high-dimensional fMRI brain imaging data, or improve product recommendation systems of companies like Amazon.My main scientific interest is to understand the computational principles required for discovering structure in large amounts of data. My proposed research concentrates on developing a novel framework for representing and learning large-scale deep generative models that support inferences at multiple levels. This new class of hierarchical probabilistic models provides a powerful tool for defining flexible probability distributions over high-dimensional data, and allows us to build rich probabilistic models that can automatically discover semantic regularities, structured relations, or invariances from large volumes of high-dimensional data.Many existing machine learning systems today, such as support vector machines, are fundamentally limited in their ability to learn complex structural relations from high-dimensional data. Learning systems cannot cope with novel tasks for which they have not been specifically trained. My research aims to develop probabilistic models that are multi-functional, capable of extracting higher-order knowledge from data, and successfully transfer acquired knowledge to learning new tasks. These models hold great promise for making a big impact on many research areas, including computational biology, neuroscience, medical diagnosis, data mining, and robotics.