User:Karatekid2013/sandbox

Alexander G. Gray currently directs the Fundamental Algorithmic and Statistical Tools Laboratory (FASTlab) at the Georgia Institute of Technology, 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. Gray is the Chief Technology Officer and Co-founder of Skytree, a San Jose, California-based machine learning company that uses analytics to process massive datasets.

Education and Work
Gray received Bachelor’s degrees in Applied Mathematics and Computer Science from UC Berkeley and a Ph.D. in Computer Science from Carnegie Mellon University, and worked in the Machine Learning Systems Groups of NASA’s Jet Propulsion Laboratory for 6 years.

His work with FASTlab employs a multi-disciplinary array of technical ideas (from discrete algorithms and data structures, computational geometry, computational physics, Monte Carlo methods, convex optimization, linear algebra, distributed computing) to develop fast algorithms for several fundamental statistical methods and new statistical machine learning methods for difficult aspects of real-world data, such as in astrophysics and biology. This work has enabled scientific results which have been featured in Science and Nature, and has received a National Science Foundation CAREER award, three best paper awards, and three best paper award nominations. He has given tutorials and invited talks on efficient algorithms for machine learning at venues including ICML, NIPS, SIAM Data Mining, and is a member of the National Academies Committee on the Analysis Massive Data.

'need citation''

He currently serves as Co-founder and CTO to Skytree, Inc., a machine learning company that develops machine-learning software for enterprise use. The company recently raised $18 million in Series A funding round led by U.S. Venture Partners.

Research
His research focuses on developing new statistical and computational foundations demanded by next-generation challenges in data analysis:

Algorithmic and statistical foundations of machine learning and scientific computing. Two challenges which keep increasing in importance and ubiquity are challenges of scale: massive datasets and various curses of dimensionality. He works toward new general algorithmic strategies for dealing with the fundamental "inner-loop" computations at the root of large classes of statistics and machine learning methods, both classical and modern. The work is general enough that it impacts other areas of scientific computing, such as physical simulation and linear algebra.

Astrostatistics and other challenge applications in science and engineering. He develop statistical and computational solutions driven by and validated by problems in domains of modern importance -- mainly, in astrophysics and other areas including biochemistry and medicine, particle physics, and internet applications. 'is the link right?'//''

Activities

 * Founder and Chair of the Machine Learning Area in the College of Computing, Georgia Tech ; Organizer of the Georgia Tech Machine Learning and Data Mining Seminar; see Machine Learning @ Georgia Tech.


 * Co-Principal Investigator of the FODAVA (Foundations of Data Analysis and Visual Analytics) Lead Center at Georgia Tech.


 * Co-Organizer of the Workshop on Large-Scale Machine Learning: Parallelism and Massive Datasets at NIPS 2009.


 * Co-Organizer of the Workshop on Machine Learning and AI Applications in Astrophysics and Cosmologyat IJCAI 2009.


 * Co-Organizer of the first annual CoC Research Day, a showcase of the research of the College of Computing in Georgia Tech, presented by and for students.


 * CEO of Analytics 1305, which produces commercial data analytics software based on the research of the FASTlab. dead link


 * Industrial Liaison for data analytics for CSE, Georgia Tech.

Publications
'Which ones?'


 * A Distributed Kernel Summation Framework for General-Dimension Machine Learning. Lee, D., Vuduc, R., and Gray, A. G., in SIAM International Conference on Data Mining, 2012. Winner of Best Paper Prize.


 * Fast kernel conditional density estimation: A dual-tree Monte Carlo approach. Holmes, M. P., Gray, A. G., and Isbell Jr., C. L., in Computational Statistics & Data Analysis Volume 54 Issue 7, 2010.
 * Fast Stochastic Frank-Wolfe Algorithms for Nonlinear SVMs. Ouyang, Hua., and Gray, A. G. in SIAM International Conference on Data Mining, 2010.
 * FuncICA for Time Series Pattern Discovery. Mehta, N., and Gray, A.G. in SIAM International Conference on Data Mining, 2009.
 * Linear-time algorithms for pairwise statistical problems. Ram, P., Lee, D., March, W. B., and Gray, A. G. in Advances in Neural Information Processing Systems, 2009.


 * Rank-approximate nearest neighbor search: Retaining meaning and speed in high dimensions. Ram, P., Lee, D., Ouyang, H., and Gray, A. G. in Advances in Neural Information Processing Systems, 2009.
 * Non-Negative Matrix Factorization, Convexity and Isometry. Vasiloglou, N., Gray, A.G., and Anderson, D.V. in SIAM International Conference on Data Mining, 2008.


 * Massive-Scale Kernel Discriminant Analysis: Mining for Quasars. Riegel, R., Gray, A.G., Richards, G., in SIAM International Conference on Data Mining, 2008.


 * An Investigation of Practical Approximate Nearest Neighbor Algorithms. Liu, T., Moore, A. W., Gray, A. G., and Yang, K., in Advances in Neural Information Processing Systems 17, 2004 (proceedings will appear in 2005).


 * Nonparametric Density Estimation: Toward Computational Tractability. Gray, A.G., Fischer, B., Schumann, J., and Buntine, W. In SIAM International Conference on Data Mining, 2003. Winner of Best Algorithm Paper Prize.


 * ‘N-Body’ Problems in Statistical Learning. Gray, A. G. and Moore, A. W. In Advances in Neural Information Processing Systems 13, 2000 (proceedings appeared in 2001).

Awards
'Which awards?'

Research Awards
 * SIAM International Conference on Data Mining Best Paper, winner 2012
 * SIAM International Conference on Data Mining Best Algorithm Paper Prize, winner 2003 (1 selected from 106 submissions) (see publication below…nonparametric paper)
 * ASA Computational Statistics Student Paper Prize, co-winner 2003
 * JPL NOVA Team Award for Multi-Rover Integrated Science Understanding System, co-winner 1999
 * JPL NOVA Individual Award for Excellence in Research & Development, winner 1997
 * NASA Group Achievement Award for Multimission VICAR Planner, co-winner 1995

Academic Awards
 * NASA Graduate Research Fellowship
 * L. Wrasse Scholarship
 * Fankhauser Scholarship
 * J. Grossmith Scholarship
 * Levi Strauss Scholarship
 * Bank of America Mathematics Scholarship
 * President’s Undergraduate Fellowship
 * University Scholar
 * Regents’/Chancellor’s Scholarship (full scholarship to UC Berkeley)