CAREER: Optimal Information Extraction in Intelligent Systems

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This is a Faculty Early Career Development (CAREER) award. The research will explore how an organism extracts information from its environment for learning and perception, both to understand human learning and to create better machine learning algorithms. The first objective is to develop and apply new algorithms to better understand the mapping in the sensory pathways. An important goal is to understand how the visual pathway computes the invariant responses observed in inferotemporal cortex. The second objective is to study the extraction of information from cross-sensory interaction and its role in the development of perceptual invariance. This work will involve integrated computer simulations, mathematical modeling, and psychological experiments. As part of this goal, the researcher will study input feature selection, output feature selection, and the general problem of how dimensions should best interact in machine learning algorithms. The final research goal is to bring together the new knowledge in constructing a better autonomous learning machine that can learn to recognize objects. The algorithm will be more modular than current algorithms and will collect its own training data autonomously through a camera, microphone, and other sensors.The educational goal is to train students in the lab as well as in the classes to think about problems from a variety of approaches. They will be educated in the advantages and limitations of computational modeling, computational analysis, psychophysics and electrophysiology.This CAREER award recognizes and supports the early career-development activities of a teacher-scholar who is likely to become an academic leader of the twenty-first century. The research will improve our understanding of optimal integration between sensory modalities. This will lead to improvement in computer sensing algorithms, including computer vision, speech recognition, and any other application where other sources of information may be available. The work is also expected to give insight to the general problem of how to optimally combine different sources of information for machine learning. The educational aspects of this project are designed to give students a multidisciplinary perspective along with specific skills allowing them to use and appreciate a variety of approaches and techniques.