WELCOME TO THE EXCITING WORLD OF MACHINE LEARNING AND NEURAL NETWORKS AT CMU !

SOME MEMEBERS OF THE MACHINE LEARNING FAMILY

``Ever since computers were invented, it has been natural to wonder whether they might be made to learn. If we could understand how to program them to learn the impact would be dramatic. The practice of computer programming would be revolutionized as many tedious hand-coding tasks would be replaced by automatic learning methods. And a successful understanding of how to make computers learn would most likely yield a better understanding of human learning abilities and disabilities as well.'' (from Tom Mitchell's new book ``Machine Learning'')

• Avrim Blum, interested in machine learning theory, algorithm design and analysis. See his papers.
• Jaime Carbonell, interested in integrated intelligent systems (seamless integration of machine learning, planning, problem-solving, execution monitoring and communication), and in multi-lingual natural language processing.
• Scott Fahlman, interested in artificial neural networks, software development environments (Gwydion/Dylan), and high-performance processing of biomedical images.
• Merrick Furst, interested in internet-based tools for resource discovery and information exchange, complexity theory, and planning theory.
• Tom Mitchell, interested in machine learning, with applications to robotics, information retrieval and database mining.
• Andrew W. Moore, interested in machine learning applications to robots, factories and other complex control systems.
• John Lafferty, interested in speech and natural language processing, statistical learning algorithms, probability and information theory.
• Dean Pomerleau, interested in autonomous diving, development of neural network learning techniques for robotics and computer vision, and human computer interaction.
• Reid Simmons, interested in mobile robot planning and task-level control, probabilistic planning and reasoning intelligent agents.
• Katia Sycara, interested in distributed coordination of intelligent software agents, case-based reasoning and learning, and constraint-directed reasoning.
• Sebastian Thrun, interested in machine learning applications to robotics, learning architectures, reinforcement learning, and the integration of symbolic and neural computation. See his papers.
• Dave Touretzky, interested in representation and processing of information in the brain.
• Manuela Veloso, interested in planning and learning, machine learning applied to signal understanding, experience-based autonomous agents with high-level and low-level task reasoning, collaborative and adversarial planning and learning in dynamic domains.
• Alex Waibel, interested in speech, language, speech translation, multimodal interfaces, neural nets and machine learning.

• Michael Cox, interested in case-based learning, derivational analogy, multistrategy learning, rationale capture and replay in planning, memory (especially forgetting), introspection.
• Jeff Schneider
• Michael Witbrock

• Deb Baker
• Shumeet Baluja
• Douglas Beeferman
• Jim Blythe
• Justin Boyan, interested in novel algorithms for applying reinforcement learning and function approximation to large-scale control and optimization problems (see his papers). He also organizes our weekly ML seminars--contact him to volunteer!
• Rich Caruana
• Lonnie Chrisman
• Scott Davies , interested in machine learning, planning, and function approximation (particularly within the context of reinforcement learning).
• Rujith DeSilva
• Robert Driskill
• Eugene Fink, interested in problem solving, automatic problem reformulation, learning, and theoretical foundations of AI.
• Dayne Freitag
• Matthew Glickman
• Rich Goodwin
• Geoffrey Gordon, interested in reinforcement learning, particularly in combination with function approximators.
• Karen Haigh, interested in learning for high-level tasks in robotics.
• Angela Kennedy
• Sven Koenig, interested in real-time search, probabilistic robot navigation and learning, performance of reinforcement learning methods, risk-sensitive planning, planning and learning with Markov models, exploration, and lunar rovers (see his papers).
• Ken Lang
• Shyjan Mahamud, interested in visual recognition and navigation, human-robot interactions.
• Daniel Nikovski
• David Redish interested in theoretical neuroscience, especially spatial reasoning, including navigation and sensory-motor coordination (see his papers).
• Scott Reilly
• Henry Rowley, interested in computer-vision neural networks.
• Lisa Saksida
• Sean Slattery
• Yury Smirnov
• Peter Stone, interested in machine learning and planning, particularly within the Robotic Soccer domain (see his papers).
• Joseph O'Sullivan, interested in machine learning applications to robotics and vision, and particularly in learning with prior knowledge. (see his papers).
• Astro Teller, interested in machine learning, with a specialization in evolutionary computation
• Belinda Thom , interested in machine learning applications, in particular, applications to improvisational, interactive music tasks. These types of tasks involve time-series prediction, unsupervised pattern classification, and adaption.
• James Thomas
• William Uther
• Santosh Vempala
• Xuemei Wang

• Thorsten Joachims, interested in machine learning, with applications to information retrieval and the World Wide Web.
• Visitors - please send me mail !!!

• Learning noisy linear separators. Contact Avrim Blum.
• Algorithms for focusing on relevant features. Contact Avrim Blum.
• CRAWL. A computational theory of rodent navigation, including neural representations of spatial information and the learning of new environments by exploration. Contact Dave Touretzky or David Redish.
• Skinnerbots. A theory of operant conditioning with application to trainable robots. Contact Dave Touretzky or Lisa Saksida.
• XAVIER and AMELIA. Autonomous mobile robots that learn.
• WebWatcher. A tour guide for the World Wide Web.
• PRODIGY, a high-speed planning and learning architecture.
• Various projects related to speech, language, speech translation, and multimodal interfaces.
• Detection of faces and other objects in images, using neural network-based techniques. See the on-line demo.
• AUTON
• Planning and Learning by Analogical/Case-Based Reasoning
• Planning and Search Algorithms
• Minds for Robots: Agents that Plan, Execute, and Learn
• Collaborative and Adversarial Planning and Learning: RoboSoccer and Bolo
• Machine Learning for Signal Understanding
• Rationale Capture and Reuse in Mixed-Initiative Planning

• Machine Learning
• Machine Learning Theory
• Introduction to Artificial Neural Networks
• Computational Models of Neural Systems.
• Introduction to Artificial Intelligence
• Information Theory

• Center for the Neural Basis of Cognition (a joint center with the University of Pittsburgh)
• The Reinforcement Learning Homepage
• The Learning Lab Homepage
• list of conferences related to machine learning
• The Neural Information Processing Systems (NIPS) Conference
• David Aha's list of machine learning researchers and resources