JAMIESON SCHULTE

 jschulte+@cs.cmu.edu
(412) 401-4329
Objective Research and development of machine learning, information extraction, or autonomous robotic systems.
Education Carnegie Mellon University, Pittsburgh, PA
M.S., Knowledge Discovery and Data Mining, December 2001
M.S., Electrical and Computer Engineering, May 1997
B.S., Electrical and Computer Engineering, May 1996
Recent
Employment
and
Research		 Carnegie Mellon University, CS Dept, Pittsburgh, PA (Aug 2002 - Aug 2003)
Senior Research Programmer at the CMU Robot Learning Lab: Software and hardware development for mobile robotics.

WhizBang! Labs, Pittsburgh, PA (August 2001 - May 2002)
Software Engineer: Developed entity extractors, document classifiers, and information aggregation applications for web data.

Carnegie Mellon University, CS Dept, Pittsburgh, PA (Jan 1997 - Aug 2001)
Research Engineer at the CMU Robot Learning Lab: Machine learning research with application to robotics, multi-agent planning, human-robot interaction, and intelligent environments. Researched a fast, probabilistic method for distributed multi-agent planning and developed a multi-agent simulation environment with visualization tools. Past research has involved robot learning of human interaction and learning of building control strategies.

MEMS Lab, CMU ECE Dept, Pittsburgh, PA (September 1996 - April 1997)
M.S. research project in Electrical and Computer Engineering: Development of a computer vision system to estimate submicron (and subpixel) motion of MEMS structures, and of hardware for positioning of microscope probes at the micron scale.

Selected
Publications		 L.V. Lita, J. Schulte, and S. Thrun, 2001. A Multi-Agent System for Agent Coordination in Uncertain Environments. Proceedings of the 5th International Conference on Autonomous Agents.

S. Thrun, J. Schulte, and C. Rosenberg, 2000. Robots With Humanoid Features in Public Places. IEEE Intelligent Systems, pages 7--11, July/August 2000.

J. Schulte, C. Rosenberg, and S. Thrun, 1999. Spontaneous Short-term Interaction with Mobile Robots. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA99).

J. Schulte and S. Thrun, 1998. Reinforcement Learning for Intelligent Building Control. Proceedings of CONALD-98, Workshop on Machine Learning and Reinforcement Learning for Manufacturing.

Skills Experience with statistical methods for machine learning and signal processing, AI and machine learning algorithms, standard research programming languages, electrical engineering, computer science, and robotics concepts.