From Single Cells to Exploding Stars: Machine Learning on All Scales
Speaker: Thomas J. Fuchs, California Institute of Technology and Jet Propulsion Laboratory
Series: CEE Departmental Seminars
Location: Engineering Quad E219
Date/Time: Wednesday, October 2, 2013, 12:00 p.m. - 1:30 p.m.
We are in the midst of a data-driven revolution. The current explosion of data in nearly all sciences rivals such historic events like the invention of the printing press or Galileo's first telescope. Unlike ever before in the history of science these vast amounts of data exceed the capabilities of even the best domain experts. Thus gaining new insight into nature is fundamentally a collaborative effort and it is machine learning which gives us the capabilities to gain knowledge from big data and it hands us the tools to tackle a multitude of novel and exciting research questions.
In this talk I will focus on projects in cancer research and astronomy and I will argue for a joint discriminative bottom-up and generative top-down approach for parameter estimation and classification. First, fast and simple features based on low-level visual cues are used to train discriminative ensemble classifiers. Then, the output of these models is utilized to restrict the hypothesis space and facilitate parameter estimation for complex generative models. Adopting a Bayesian point of view allows us to perform parameter inference and uncertainty quantification based on the estimated posterior distributions. In this context a challenge arises from the fact that a large number of interesting statistical models have no tractable likelihood. To this end, I will describe an adaptive population Monte Carlo framework based on Approximate Bayesian Computation (ABC) which makes likelihood-free inference feasible. The utility and performance of the proposed approach is demonstrated for applications on microscopic scale in computational pathology and up to macroscopic scale in space exploration.
I will conclude the talk with an update on how these recent breakthroughs in large scale machine learning at JPL facilitate land coverage classification in a joint project with the Ecohydrology Lab at Princeton.
Dr. Thomas Fuchs is a research technologist at NASA's Jet Propulsion Laboratory in Pasadena and visiting scientist at the California Institute of Technology where he occasionally teaches and organizes the machine learning seminar. His research focuses on the development of new ensemble methods and Bayesian sampling techniques for large scale machine learning. Thomas is PI and CoI for several big data related research efforts at JPL. Thomas' postdoctoral research at the computational vision lab of Pietro Perona at Caltech and Larry Matthies' computer vision group at JPL was focused on approximate Bayesian computation (ABC) and its application on likelihood-free parameter estimation for computer vision and medical imaging. In 2010 Thomas received his PhD (Dr.Sc.) from ETH Zurich for his work in the machine learning laboratory of Joachim Buhmann. During this time he also completed the Ph.D. program on System Biology and Medicine from ETH's CC-SPMD. Thomas received his MSc degree (Dipl.-Ing.) from TU Graz where he majored in technical mathematics with a minor in computer science. Most of the work for his master thesis on Bayesian networks was conducted at SCR in Princeton.Most of the work for his master thesis on Bayesian networks was conducted at SCR in Princeton.