Thomas G. Dietterich

E-mail: tgd@cs.orst.edu Phone: +1-541-737-5559 Office: KEC 2067
PGP Public Key Recent changes to this page. (Last updated November 4, 2009.)

Page Contents: Research Prospective Students Publications CV Software Students and Staff Course Materials Bio Sketch Conferences Comedy

Current Research

"If you invent a breakthrough in artificial intelligence, so machines can learn," Mr. Gates responded, "that is worth 10 Microsofts." (Quoted in NY Times, Monday March 3, 2004)

The focus of my research is machine learning: How can we make computer systems that adapt and learn from their experience? How can we combine machine learning with other advances in AI to build Integrated Intelligent Systems? How can we combine human knowledge with massive data sets to expand scientific knowledge and build more useful computer applications? My laboratory combines research on machine learning and AI fundamentals with applications to problems in science and engineering.

• Scientific Projects
  • Ecosystem Informatics and Computational Sustainability: Oregon State University is a leader in combining computer science and the ecological sciences to build the new discipline of Ecosystem Informatics. Ecosystem Informatics studies methods for collecting, analyzing, and visualizing data on the structure and function of ecosystems. It is an instance of an important new direction in science: Data Exploration Science (see Jim Gray's 2003 KDD talk).

    Oregon State is also part of the NSF Expedition in Computational Sustainability jointly with Cornell University, Bowdoin College, Howard University, and the Conservation Fund. This effort seeks to develop novel computational methods to address problems in ecosystem science and sustainable management of the biosphere.

    My group is involved in many Ecosystem Informatics and Computational Sustainability activities:

    • Arthropod Identification. Our current understanding of complex ecosystems is limited by a lack of data. One particularly useful kind of data is population counts of "bugs" (small arthropods that live in soils, lakes, streams, and the ocean). We seek to develop devices for capturing, imaging, and sorting bugs combined with general image processing/machine learning/pattern recognition tools for counting and classifying them. We hope to transform the ability of scientists to measure the health of forests, streams, and estuaries. More generally, we are interested in developing a wide range of novel instruments for expanding the quality, quantity, and spatio-temporal resolution of ecologically-relevant data. We recently received a grant of $800,000 from the NSF to support our work in this area. See OSU News Service Story. Project Pages. Image Databases. Our research also contributes to computer vision and object recognition more generally.

    • Automated Data Cleaning for Sensor Networks. As large scale sensor networks are deployed to collect ecological data, methods are needed for automated quality assurance ("data cleaning") for this data to detect and remove errors due to sensor failure. Graduate student Ethan Dereszynski has developed Dynamic Bayesian Network (DBN) models of sensor network data from the Andrews Experimental Forest. These models are being applied to automatically detect and flag anomalies in temperature data.

    • Machine Learning for Species Distribution. One of the central goals of ecology is to understand and predict the distribution of species (including the bugs that we are studying in the Insect Identification project). Given a data set that records observations of the presence (or absence) of multiple species at multiple locations, we wish to develop models that can predict their presence/absence elsewhere. We are interested not only in static distribution models, but also in process models that capture the temporal and spatial of species distributions (e.g., bird migration, flight times of moths, spread of invasive species, survival of endangered species, etc.). Our species distribution team includes faculty members (Matt Betts, myself, and Weng-Keen Wong), post-doc Rebecca Hutchinson, and graduate students Arwen Lettkeman and Paul Wilkins.

    • Approximate Optimization for Bio-Economic Models. Many sustainability applications require solving large spatio-temporal optimization problems under uncertainty. We are collaborating with economists Jo Albers and Claire Montgomery on methods for approximate solution of spatio-temporal optimization problems involving land management for wildfire control and counter-measures for controlling invasive species.

    • Summer Institute in Ecosystem Informatics. Junior and Senior undergraduates and first-year graduate students can spend 10 weeks studying and researching at the H. J. Andrews Experimental Forest under funding from this NSF summer program. The goal is to introduce students to the research challenges at the intersection of mathematics, computer science, and the ecosystem sciences.

  • Intelligent Desktop Assistants. We are involved in two large efforts to develop intelligent assistants for the computer desktop.

    • TaskTracer. When you come into work in the morning, you don't want to say to your computer "I want to run Word", but rather, "I want to work on my CS534 homework". In other words, you would like a user interface that was organized around your projects and activities rather than around application programs, files, folders, etc. You would also like all of your information in one place rather than scattered across the local file system, network file systems, web sites, email folders, calendar, contacts, etc. TaskTracer extends the Windows UI to provide exactly this functionality. This research is supported by DARPA through the CALO project and gifts from Intel. OSU News Service story. Project Web Site.

    • CALO. The goal of the CALO project was to develop an AI personal assistant that can help you find relevant documents, prepare for meetings, keep track of what is going on during meetings, and autonomously execute tasks such as arranging travel, scheduling meetings, executing administrative workflows (e.g., purchasing and staffing), and so on. Our work on CALO focused on developing methods for integrating multiple, separately-engineered components into a single learning and reasoning system. We also prototyped a novel system that employs programming-by-demonstration to define new learning tasks for CALO to solve autonomously. We are currently editing a book describing the results of the CALO project.

  • Learning via Interaction. Statistical machine learning has focused primarily on learning from observational data. Human learning often involves learning from demonstrations, explanations, and feedback. I am interested in combining all of these methods to develop intelligent systems that can learn both autonomously and through interaction with human users and coaches. We have three efforts in this direction:
    • AI for Computer Games. The AI components of most computer games are hand-authored rule-based systems. We are studying methods for developing game AI agents via machine learning. One approach is to apply reinforcement learning to automatically learn an opponent for games. Another approach is to teach the game AI by demonstrating how to play the game. A third method is to provide "coaching feedback" to the learning system. A fourth approach is to transfer knowledge learned on one game to rapidly construct an AI agent for a new game. We use the RTS game Wargus as our experimental platform. Wargus is based on the Strategus game engine. This work is funded by ARO under a MURI grant and through the DARPA Bootstrapped Learning program.

    • End-User Debugging of Learned Programs. We are starting to see end-user applications that incorporate machine learning components (e.g., adaptive spam filters, adaptive email management, adaptive user interfaces). How can we empower end users to get these learning systems to behave properly? For example, how can end users define new features, provide advice on feature relevance, and yet also understand that no learning system can be perfect? How can a learning system explain itself to the end user? Funding provided by the National Science Foundation.

    • Bootstrapped Learning. Under the DARPA Bootstrapped Learning program, we are studying how a teacher can create a complex AI system through natural instructional methods that combine direct instruction, demonstration, examples, and feedback. The vision of Bootstrapped Learning is that the teacher constructs a series of lessons where each lesson introduces a new concept or skill that relies on the lessons previously taught. We are part of the Student team, lead by SRI International. Alan Fern leads our effort.

• Fundamental Machine Learning Research

  • Evidence Trees. We have developed a new approach to supervised learning in which ensembles of tree classifiers are applied not to make classification decisions but to select which training data points provide evidence relevant to making a decision or prediction. This evidence can then be input to a second-level decision making process, which could be another classifier or some form of kernel density estimation. We are exploring this in the context of computer vision and the Arthropod Identification project.

  • Sequential and Spatial Supervised Learning (SSSL). Many emerging applications of machine learning involve sequential or spatial data including the scientific applications listed above as well as problems of transaction monitoring, counter-terrorism, and fraud detection. Most present-day applications of machine learning to spatio-temporal require extensive ad hoc tool-building. Can we build a new generation of generic machine learning tools that can be applied "off the shelf" to solve these sequential and spatial supervised learning problems?

  • Machine Reading. In collaboration with researchers at BBN, CMU, University of Washington, ISI, and Cycorp, we are studying methods for extracting knowledge from text to support inference. Our focus is on learning rules (e.g., Horn clauses) from noisy and incomplete training data extracted from reading text. Funding provided by the DARPA Machine Reading program.

• Reviews, tutorials, and books. I have written several review articles and tutorials on machine learning.

There is a weekly AI Colloquium (Tuesdays, 3-4pm, KEC 1007) where we present and discuss research we are doing here at Oregon State and where visitors also make presentations from time to time. Students interested in joining my group are strongly encouraged to attend these meetings. This quarter there are two reading groups: Machine Reading (Tuesdays 4-5pm KEC 3057) and Learning + Search (Wednesdays 4-5pm KEC 2057).

Information for Prospective Students

If you are seeking a research career in machine learning, data mining, artificial intelligence and related areas, and you have a strong background in mathematics and programming, please read my Information for Prospective Students page. To see what courses I expect my Ph.D. students to take, please see Recommended Courses for Ph.D. Students in Machine Learning.

Publications, Curriculum Vita, and Software

• Publications
• Talks
• Curriculum Vita
• Downloadable Collection of Error Correcting Codes for use with the error-correcting output coding technique.
• Release 1.0 of MAXQ Hierarchical Reinforcement Learning code.
• RSW (Recurrent Sliding Window) Package for WEKA for sequential supervised learning.
• Implementation of the PCBR region detector developed as part of the BugID project.
• Implementation of TreeCRF system and supporting materials from our JMLR paper.

Journals and Book Series

• I am a member of the Editorial Board for the Journal of Machine Learning Research, which is an electronic (and hardcopy) journal covering all areas of machine learning.
• I serve on the Board of Advisors of Neural Computing Surveys.
• I edit the MIT Press book series on Adaptive Computation and Machine Learning.
• With Ron Brachman, I co-edit the Morgan-Claypool Synthesis Series on Artificial Intelligence and Machine Learning.
• I serve on the Board of Evaluation of Intelligent Systems, an online resource on methods for statistical evaluation of AI systems.
• I am past President of the International Machine Learning Society.

Entrepreneurial Activities

• I am a co-founder of Strands (formerly MyStrands; formerly MusicStrands), a recommendation company.
• I am a co-founder of Smart Desktop. Smart Desktop is now part of Decho, Inc., which is a "cloud computing" effort of EMC. Decho is commercializing technology developed as part of the TaskTracer system.

Current Students and Staff

• Christian Baumberger, Graduate Student.
• Ethan Dereszynski, Graduate Student.
• Rebecca Hutchinson, Postdoc.
• Jed Irvine, Software Developer.
• Arwen Lettkeman, Graduate Student.
• Junyuan Lin, Graduate Student.
• Jonathan Mark, REU Student.
• Gibby Reynolds, REU Student.
• Michael Slater, Project Manager.
• Shahed Sorower, Graduate Student.

Former Students and Staff

• Hussein Almuallim, Oil and Energy Professional, Calgary, Canada.
• Eric Altendorf, Google.
• Adam Ashenfelter, Strands, Inc., Corvallis, Oregon.
• Ghulum Bakiri, Department of Computer Science, Bahrain University
• Xinlong Bao. Google Pittsburgh.
• Brian Breck.
• Waranun Bunjongsat.
• Giuseppe Cerbone. Independent Information Services Professional, Milan, Italy.
• Martha Chamberlin.
• Hei Chan.
• Richard Charon.
• Eric Chown, Associate Professor, Bowdoin College.
• Dan Corpron
• Diane Damon, Damon Consulting, Portland, OR.
• Phuoc Do, Decho.
• Nicholas Flann Associate Professor, Utah State University
• Greg Foltz.
• Dan Forrest.
• Tony Fountain, Lab Director/Scientist, San Diego Supercomputer Center.
• Ashit Gandhi, Founder and Vice-President, Prism Gem, LLC - The Art of Diamond Coloring.
• Colin Gerety, Fort Collins, CO.
• Brandon Harvey, Project Manager, GarageGames, Eugene, Oregon.
• Guohua Hao, Machine Learning Researcher, Motorola.
• Hermann Hild, President, SMI Cognitive Software GmbH .
• Saket Joshi, Graduate Student, Tufts University.
• Varad Joshi, Senior Software Engineering Manager at Arris, Portland, OR.
• Caroline Koff, Hewlett-Packard Corporation, Fort Collins, CO.
• Victoria Keiser, Research Programmer, CMU. Masters Thesis (PDF).
• Michael Kelm, Research Scientist, Siemens Healthcare.
• Eun Bae Kong, Associate Professor and Department Head, Computer Science, Chungnam National University, South Korea
• Bill Langford, Post-doc at RMIT, Melbourne, Australia.
• Dragos Margineantu, The Boeing Company.
• Gonzalo Martinez, Assistant Professor, Autonomous University of Madrid.
• Prafulla Mishra
• Avis Ng.
• Soumya Ray, Assistant Professor, Case-Western Reserve University.
• Angelo Restificar, ZetaInteractive.
• Ritchey Ruff, Senior SDET, Microsoft.
• Jianqiang Shen. Research Scientist, Pearson Knowledge Technologies. Doctoral Dissertation (PDF).
• Rongkun Shen. Post-doc, Oregon Health and Science University, Portland.
• Shriprakash Sinha
• Simone Stumpf. Lecturer, City University London.
• Dan Vega, Senior Software Engineer at Valley Inception, LLC.
• Mark Vulfson. Microsoft Corporation.
• Xin Wang, Microsoft Corporation.
• Dietrich Wettschereck. Recommind.com.
• Pengcheng Wu.
• Michael Wynkoop, Qualcomm.
• Wei Zhang, The Boeing Company.
• Wei Zhang, Post-doc. HP Labs. Doctoral Dissertation (PDF).
• Valentina Zubek, Aureon Biosciences.

Previous Courses and Courseware

• CS519/GEO599: Principles of Ecosystem Informatics, 2004-2005.
• CS 534, Spring 2005, Machine Learning.
• CS430, Fall 2003, Introduction to Artificial Intelligence
• CS539, Fall 2003, Seminar: Probabilistic Relational Models
• CS 533, Applied Artificial Intelligence for Engineeers.
• CS 539, Winter 2000, Selected Topics in Artificial Intelligence: Probabilistic Agents
• CS 430/530, Fall 1999, Artificial Intelligence Programming Techniques.
• CS 519, Fall 1996. Research Methods in Computer Science.
• CS 450/550, Winter 1996, Introduction to Computer Graphics.

Machine Learning Resources

• How to be a Graduate Student. A great web page with pointers to lots of good resources for graduate students.
• Standard Proofreading Symbols that I use when marking corrections on papers.
• Research Index at Penn State. An invaluable resource for finding online articles, citations, etc.
• Journal of Machine Learning Research (JMLR). (Free electronic and hardcopy journal.)
• Machine Learning. (Expensive hardcopy journal.)
• Journal of Artificial Intelligence Research (JAIR). (Free electronic and hardcopy journal.)
• The Machine Learning Database Repository at UC Irvine.
• The Machine Learning Programs Repository at UC Irvine.
• UC Irvine ML information.
• StatLib containing data, algorithms, and other information relevant to statistics.
• Reinforcement Learning Repository at UMass Amherst.
• Knowledge Discovery Mine containing information about knowledge discovery in databases.
• NIPS Online Proceedings.
• Neuroprose Archive containing recent papers relevant to neural networks.
• Machine Learning Information Services .
• CMU Reinforcement learning group.
• NeuroCOLT home page.
• List of Usenet FAQ's
• Bibliographies on Artificial Intelligence
• Monash University Minimum Message Length group
• Computational Intelligence Bibliography Collection.
• Numerical Recipes Homepage.

Conferences and Workshops

Upcoming Conferences

The 1st Asian Conference on Machine Learning (ACML-2009), Nanjing, China. November 2-4, 2009.

Neural Information Processing Systems Conference (NIPS-2009), Vancouver BC, Canada, December 7-12, 2009.

International Conference on Machine Learning (ICML 2010), Heifa, Israel, 21-24 June 2010.

Recent Conferences

Twenty-first International Joint Conference on Artificial Intelligence (IJCAI-09), Pasadena, CA, July 11-17, 2009.

First International Conference on Computational Sustainability (Comp-Sust 2009), Ithaca, NY, June 8-11, 2009.

International Conference on Machine Learning (ICML 2009), Montreal, Canada June 14-18, 2009.

IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), Miami, Florida, June 20-25, 2009.

Research Comedy

• "Gaussian classifiers and support vector machine with Gaussian kernels) are wildly used in many areas such as speech signal processing and pattern recognition." (Lin, Bilmes, and Crammer, How to Loose Confidence: Probabilistic Linear Machines for Multiclass Classification).

My Family's Musical Activities

• yOya. My son Noah writes songs and plays keyboards for this band.

• The Stack. Former band that my son Noah belonged to. While in college, Noah also arranged lots of songs for Mens' Blue and White (Men's A Capella at the Claremont Colleges); watch them on YouTube.

• Jubilate: The Women's Choir of Corvallis. My wife Carol sings in this choir.

Tom Dietterich, tgd@cs.orst.edu