Alexander GRAY Machine learning, statistics, and data mining Computational mathematics for massive datasets Applications: computational science and engineering	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 for 6 years in the Machine Learning Systems Group of NASA's Jet Propulsion Laboratory. I completed my PhD on 4/29/03 in Computer Science after 3.6 years at Carnegie Mellon University, advised by Prof. Andrew Moore and I'm currently a Postdoctoral Fellow in the Robotics Institute, but starting in the fall I'll be an Assistant Professor at Georgia Tech.
agray @ cs.cmu.edu 3128 Newell-Simon Hall (412) 268-8014 C.V. [pdf] [ps]		My work focuses on developing the new statistical and computational principles demanded by next-generation challenges in data analysis and autonomy. I develop new methods directly driven by, informed by, and validated by hard real problems, often by bridging technical intuitions or mathematical concepts from distant fields and perspectives. Two of the main challenges of modern computational science which keep me awake at night 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. My most recent research, in progress, is about alternative statistical theory for certain old problems. A long-term target is to nail down enough powerful and general statistical and computational foundational primitives to achieve automatic data analysis and data-driven control, a bottom-up path to fully autonomous systems.

	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...