You are cordially invited to the next IIC Colloquium Wednesday, Feb. 24: ************************ Machine Learning and Statistics on Massive Datasets, with Applications in Astronomy Feb. 24, 2010, 4:00 pm Room G115, Maxwell Dworkin, 33 Oxford Street, Cambridge Alexander Gray Professor of Mathematics and Computer Science Computational Science and Engineering Division College of Computing Georgia Institute of Technology **Note: This colloquium was postponed from Feb. 10. Bruce Boghosian's colloquium has been rescheduled for April 7.** Abstract I’ll describe algorithms and data structures for allowing the most powerful machine learning and multivariate statistical methods, which often scale quadratically or even cubically with the number of data points, to be performed many orders of magnitude faster than naive implementations. Such techniques can make previously impossible statistical analyses tractable on the scale of entire astronomical sky surveys. I will discuss scalable algorithms we have developed for n- point correlations, friends-of-friends, nearest-neighbors, kernel density estimation, nonparametric Bayes classification, principal component analysis, local linear regression, isometric non-negative matrix factorization, hidden Markov models, k-means, support vector machine-like classifiers, Gaussian process regression, and Gaussian graphical model inference, among others. In addition to techniques inspired by computational geometry, fast multipole methods, and Monte Carlo integration, we employ a distributed framework which can be thought of as a higher-order version of Google’s MapReduce. Our algorithms have enabled several first-of-a-kind large-scale cosmological analyses. Bio Alexander Gray received bachelor's degrees in Applied Mathematics and Computer Science from UC Berkeley and a PhD in Computer Science from Carnegie Mellon University, and worked in the Machine Learning Systems Group of NASA's Jet Propulsion Laboratory for 6 years. He currently directs the FASTlab (Fundamental Algorithmic and Statistical Tools Laboratory, http://www.fast-lab.org/) at Georgia Tech, consisting of 25 people including 14 PhD students, which works on the problem of how to perform machine learning/data mining/statistics on massive datasets, and related problems in scientific computing and applied mathematics. Employing a multi-disciplinary array of technical ideas (from machine learning, nonparametric statistics, convex optimization, linear algebra, discrete algorithms and data structures, computational geometry, computational physics, Monte Carlo methods, distributed computing, and automated theorem proving), the lab has developed the current fastest algorithms for several fundamental statistical methods, and also develops new machine learning methods for difficult aspects of real-world data, such as in astrophysics and biology. This work has enabled high-profile scientific results which have been featured in Science and Nature and has earned an NSF CAREER award, two best paper awards, and two best paper award nominations. He has given tutorials for the field and invited talks on efficient algorithms for machine learning at venues including ICML, NIPS and SIAM Data Mining, as well as in applied mathematics and astronomy. --------------- Refreshments will be served at 3:45 pm. Mark your calendar for these upcoming events: Monday, Feb. 22, noon, Maxwell Dworkin 323: SciGPU Seminar, Ron Babich, Boston University, "Unraveling the Mysteries of Quarks with GPUs" Wednesday, March 4, 4 pm, Maxwell Dworkin G115: Distinguished Lecture in Computational Science, Erik Winfree, Caltech For more information about IIC colloquia and other events : http://iic.harvard.edu/events/upcoming ______________________________________________________________________________________________ iic-colloquium mailing list iic-colloquium(a)seas.harvard.edu https://lists.deas.harvard.edu/mailman/listinfo/iic-colloquium_____________… iic-colloquium mailing list iic-colloquium(a)seas.harvard.edu https://lists.deas.harvard.edu/mailman/listinfo/iic-colloquium