Alexander Gray Machine learning, artificial intelligence, data mining Computational mathematics for massive datasets Challenge applications	After completing Bachelor's degrees in Applied Mathematics (concentration in Computational Statistics) and Computer Science from UC Berkeley, spending summers at the Santa Fe Institute and Los Alamos National Laboratory, among other places, I worked in the Machine Learning Systems Group of NASA's Jet Propulsion Laboratory, then completed my PhD in Computer Science and a postdoc at Carnegie Mellon University supervised by Prof. Andrew Moore. Since August 2005 I've been an Assistant Professor in the College of Computing at Georgia Tech, within the new Interactive and Intelligent Computing Division and affiliated with the even newer Computational Science and Engineering Division. I'm a member of the Center for the Study of Systems Biology, Center for Robotics and Intelligent Machines, Center for Experimental Research in Computer Systems, and the Graphics, Visualization and Usability Center, and affiliated with the The Industrial and Technology Statistics Center.
agray @ cc.gatech.edu 240 TSRB 85 5th St. NW Atlanta, GA 30318 (404) 894-6328 fax (404) 894-0673 C.V. [pdf] [ps]		My work focuses on developing the new statistical and computational foundations demanded by next-generation challenges in data analysis. Two challenges which keep increasing in importance and ubiquity are massive datasets and various curses of dimensionality. I have been concerned with computational strategies for dealing with the fundamental summations, integrals, and maximizations at the root of a wide variety of statistics and machine learning methods. A second thread of my research, in progress, concerns statistical theory for some practical open problems in machine learning. A third thread concerns meta-methods, or methods for making new methods, both statistical and algorithmic.	New/recent stuff! Spring 08 graduate course: Computational Data Analysis: Foundations of Machine Learning and Data Mining. Fall 2006 undergraduate course: Constructing Proofs. To appear in UAI 2006, Jul 06: Faster Gaussian Summation: Theory and Experiment [pdf], [ps]. I'm currently on the Program Committees for ICML 06, to be held in Pittsburgh, and KDD 06, to be held in Philadelphia. I'll be hosting a visit by Bill Cleveland, who will give a CSE Distinguished Lecture on 3/15/05. I hosted a visit by Jeff Racine, who gave a talk on Recent Developments in Kernel Smoothing with Both Categorical and Continuous Data on 11/8/05. To appear in NIPS 2005, Dec 05: Dual-tree Fast Gauss Transforms [pdf], [ps]. Talk at Oxford University, PhyStat 2005 Sep 05. Cosmic magnification result featured in Nature, April 27, 2005.

	Generalized N-body Methods Fast kernel density estimation Fast n-point correlation functions Fast all-nearest-neighbors Fast Gaussian process regression Fast nonparametric Bayes classification
						Computational Astrophysics, Cosmology, Astronomy Evidence for dark energy The origin of galaxies Quasar mapping Large-scale structure of the universe Galaxy morphologies and clusters
		Computational Chemistry, Drug Discovery, Biology Molecule ranking for virtual screening Microarray data analysis		Adaptive Monte Carlo Methods Multi-tree Monte Carlo High-dimensional integration without Markov chains
		Proximity Search The Proximity Project Fast nearest-neighbor classification Fast approximate nearest-neighbor Tutorial: Data Structures for Fast Statistics		Advanced Systems Autonomous planetary science by rover teams Efficient computer systems		Derivation of Learning Algorithms Automatic derivation of new EM algorithms
						Other pursuits...