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Auton Lab
Heads: Andrew Moore, Jeff Schneider, and Artur W Dubrawski
Contact: Artur W Dubrawski (awd@cs.cmu.edu)

Mailing address:
Carnegie Mellon University
Robotics Institute
5000 Forbes Avenue
Pittsburgh, PA 15213

For more information, see this lab's homepage.

This page last updated - January 1999.

Lab Description

The Auton Lab, part of Carnegie Mellon University's School of Computer Science, researches new approaches to Statistical Data Mining. It is directed by Artur Dubrawski, Andrew Moore and Jeff Schneider. We are very interested in the underlying computer science, mathematics, statistics and AI of detection and exploitation of patterns in data.

We build practical large-scale deployments of very highly autonomous self-improving systems. We gratefully acknowledge funding support from NSF, DARPA, NASA, the State of Pennsylvania, other agencies, and over a dozen Fortune 500 companies with whom we have collaborated.

Please see our publications page for a complete list of publications.

Personnel [Past members]

	Name	Title	Email Address
	James (Drew) Bagnell	Assistant Research Professor	bagnell2@andrew.cmu.edu
	Michael J Baysek	Systems Analyst	mjbaysek@cs.cmu.edu
	Lujie Chen	Research Analyst	lujiec@andrew.cmu.edu
	Artur W Dubrawski	Systems Scientist/Adj Professor Mism Prg	awd@cs.cmu.edu
	Geoffrey Gordon	Assoc. Res. Professor/Adjunct Faculty RI, MLD (Adjunct)	ggordon@cs.cmu.edu
	Michael Jon Knight	Senior Research Programmer	mikek@andrew.cmu.edu
	Maxim Makatchev	PhD Student, RI	mmakatch@andrew.cmu.edu
	Andrew Moore	Professor, RI/CS	awm@cs.cmu.edu (Currently On Leave)
	John K Ostlund	Senior Research Programmer	jostlund+@cs.cmu.edu
	Saswati Ray	Research Programmer	sray@cs.cmu.edu
	Maheshkumar Sabhnani	Graduate Assistant, MLD	sabhnani+@andrew
	Jeff Schneider	Assoc. Research Professor	schneide@cs.cmu.edu
	Sajid Siddiqi	PhD Student, RI	siddiqi at cs.cmu.edu

Current Projects [Past Projects]

Intelligent Diabetes Assistant - We are working to create an intelligent assistant to help patients and clinicians work together to manage diabetes at a personal and social level. This project uses machine learning to predict the effect that patient specific behaviors have on blood glucose.

Recent publications [View all 70 publications]

• Learning Outbreak Regions in Bayesian Spatial Scan Statistics
  M. Makatchev and D.B. Neill
  ICML 2008 Workshop on Machine Learning for Health Care Applications, Helsinki, Finland, July, 2008. [Abstract]
  Download: pdf [276 KB] copyrighted

• Combining Bayesian Networks and Formal Reasoning for Semantic Classification of Student Utterances
  M. Makatchev and K. VanLehn
  Proc. Int. Conf. on AI in Education, AIED2007, IOS Press, July, 2007. [Abstract]
  Download: pdf [104 KB] copyrighted

• Efficient Discovery of Spatial Associations and Structure with Application to Asteroid Tracking
  J.M. Kubica
  doctoral dissertation, tech. report CMU-RI-TR-06-01, Robotics Institute, Carnegie Mellon University, December, 2005. [Abstract]
  Download: pdf [6730 KB] copyrighted

• Scalable and robust group discovery on large transactional data
  P. Choi, A. Moore, and J.M. Kubica
  tech. report CMU-RI-TR-05-60, Robotics Institute, Carnegie Mellon University, December, 2005. [Abstract]
  Download: pdf [753 KB] copyrighted

• Variable KD-Tree Algorithms for Spatial Pattern Search and Discovery
  J.M. Kubica, J. Masiero, A. Moore, R. Jedicke, and A.J. Connolly
  Neural Information Processing Systems, December, 2005. [Abstract]
  Download: pdf [204 KB] copyrighted

• Making Logistic Regression A Core Data Mining Tool: A Practical Investigation of Accuracy, Speed, and Simplicity
  P. Komarek and A. Moore
  tech. report CMU-RI-TR-05-27, Robotics Institute, Carnegie Mellon University, May, 2005. [Abstract]
  Download: pdf [214 KB], ps.gz [70 KB] copyrighted

• Efficient Algorithms for the Identification of Potential Track/Observation Associations in Continuous Time Data
  J.M. Kubica, A. Moore, A.J. Connolly, and R. Jedicke
  tech. report CMU-RI-TR-05-10, Robotics Institute, Carnegie Mellon University, February, 2005. [Abstract]
  Download: pdf [168 KB], ps.gz [375 KB] copyrighted

• Fast and Robust Track Initiation Using Multiple Trees
  J.M. Kubica, A. Moore, A.J. Connolly, and R. Jedicke
  tech. report CMU-RI-TR-04-62, Robotics Institute, Carnegie Mellon University, November, 2004. [Abstract]
  Download: pdf [1196 KB], ps.gz [803 KB] copyrighted

• Spatial Data Structures for Efficient Trajectory-Based Queries
  J.M. Kubica, A. Moore, A.J. Connolly, and R. Jedicke
  tech. report CMU-RI-TR-04-61, Robotics Institute, Carnegie Mellon University, November, 2004. [Abstract]
  Download: pdf [510 KB], ps.gz [278 KB] copyrighted

• Fast Nonlinear Regression via Eigenimages Applied to Galactic Morphology
  B. Anderson, A. Moore, A.J. Connolly, and R. Nichol
  International Conference on Knowledge Discovery and Data Mining, ACM Press, New York, NY, August, 2004.