

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 NewellSimon Hall
(412) 2688014
C.V. [pdf]
[ps]


My work focuses on developing the new statistical and computational
principles demanded by nextgeneration 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 longterm target is to nail down enough powerful and general statistical and
computational foundational primitives to achieve automatic data analysis and
datadriven control, a bottomup path to fully autonomous systems.


