EAPS

Speaker: Professor David Lary, Hanson Center for Space Sciences, University of Texas at Dallas

Machine learning has already proved immensely useful in a wide variety of applications in science, business, health care and engineering. Machine learning allows us to learn by example, and to give our data a voice. It is particularly useful for those applications for which we do not have a complete theory, yet which are of significance. Machine learning is an automated implementation of the scientific method, following the same process of generating, testing and discarding or refining hypotheses. Machine learning has found many applications in remote sensing. These applications range from retrieval algorithms to bias correction, from code acceleration to detection of disease in crops, from classification of pelagic habitats, to rock type classification.As a broad subfield of artificial intelligence, machine learning is concerned with algorithms and techniques that allow computers to 'learn' by example. The major focus of machine learning is to extract information from data automatically by computational and statistical methods. Over the last decade there has been considerable progress in developing a machine learning methodology for a variety of Earth Science applications involving trace gases, retrievals, aerosol products, land surface products, vegetation indices, and most recently, ocean applications. We will review some examples of how machine learning has already been useful for remote sensing and some likely future applications with the hope that this will promote active collaboration.

Registration (Free): HERE

About the Speaker

David received a First-Class Double Honors B.Sc. in Physics and Chemistry from King's College London with the Sambrooke Exhibition Prize in Natural Science, and a Ph.D. in Photochemical Computer Modeling of Atmospheric Chemistry from the University of Cambridge. In 2010, David joined the Hanson Center for Space Science, where he is about to deploy a network of airborne allergen sensors across Chattanooga, TN, for a smart city asthma and allergy early warning system.

This seminar is co-sponsored by the MIT-CSAIL Alliances together with the IEEE Boston 1. Geoscience and Remote Sensing, 2. Computer, 3. Aerospace and Electronic Systems, 4. Microwave Theory and Techniques, 5. Robotics and Automation Societies.