Thomas G. Dietterich
Distinguished Professor (Emeritus) and Director of Intelligent Systems
Institute for Collaborative Robotics and Intelligence Systems (CoRIS)
School of Electrical Engineering and Computer Science
1148 Kelley Engineering Center
Oregon State University
Office: KEC 2067
PGP Public Key
(Last updated December 13, 2021)
Students and Staff
"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). Deep
learning is providing that breakthrough, and machine learning is now
transforming computer science and many other disciplines as well. It
is an exciting time to be working in machine learning!
The focus of my research is artificial intelligence and 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 make those integrated AI
systems robust to errors, both known and unknown? 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
- Robust Artificial Intelligence. AI methods are
widely deployed in many parts of the economy including search
engines, speech-enabled systems, computer vision, and natural
language translation. Of particular concern are AI applications that involve life-and-death
decision making such as self-driving cars, AI control of the power
grid, and autonomous weapons systems. Before we deploy AI in such
applications, we need to be confident that it will behave
With my colleagues and students, I'm pursuing research in safe
artificial intelligence including open category supervised
learning, safe reinforcement learning,
artificial intelligence. I've reviewed the research challenges in this area
in my AAAI Presidential Address, Steps Toward Robust
Intelligence. Slides, Video, Paper. More
recently, I've set forth a research agenda based on studies of
reliable human organizations, Toward High-Reliability
Artificial Intelligence PDF slides.
- 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 part of the larger field
of Computational Sustainability.
Oregon State is also part of the Institute for
Computational Sustainability led by Cornell University.
This effort seeks to develop novel computational methods to address
problems in ecosystem science and sustainable management of the
My group is involved in many Ecosystem Informatics and
Computational Sustainability activities:
- Project TAHMO: Deployment, Cleaning, and Analysis of Sensor
Network Data. We are part of
the Project TAHMO that seeks to
construct and deploy a network of 20,000 hydro-meteorological
stations in Africa. We are developing algorithms for sensor
placement, data cleaning, recovery from damaged sensors, and
analysis of the resulting data. We are building on our previous
work with Ethan Dereszynski on dynamic Bayesian network models for
sensor data cleaning. Here is a recent presentation on our work: Toward automated quality control for hydro-meteorological
weather station data PDF slides.
- Approximate Optimization for Bio-Economic Models. Many
sustainability applications require solving large spatio-temporal
optimization problems under uncertainty. We are collaborating
with natural resource economists on methods for approximate solution of
spatio-temporal optimization problems involving land management
for wildfire control and counter-measures for controlling invasive
- NIPS 2012
Posner Lecture: Challenges for Machine Learning in
- ICML 2011 Tutorial
on Machine Learning in Ecology and Ecosystem
- Intelligent Desktop Assistants. We have been 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, dropbox, web sites, email folders, calendar,
contacts, etc. TaskTracer extends the Windows UI to provide
exactly this functionality. This research has been supported by
gifts and grants from Keysight, Inc., Google, Intel, and
the DARPA CALO and PPAML projects.
- Fundamental Machine Learning and Artificial Intelligence Research
- Anomaly Detection. An important capability for AI
systems is to be able to detect when an input situation is
unusual. For example, anomaly detection can allow machine
learning systems to detect when an input case is very different
from the training data and hence could lead to extrapolation and
poor performance. Anomaly detection methods are also important
for detecting novel failures in sensor networks and novel attacks
on computer systems. We are developing a range of algorithms for
anomaly detection under grants from the DARPA, National Science
Foundation, the Future of Life Institute, and a gift from Huawei,
Here is a presentation that summarizes our work on anomaly
detection for machine learning applications involving
hand-engineered features. Advances in Anomaly Detection.
slides. It covers work on benchmarking anomaly detection
algorithms, incorporating analyst feedback, feature-based
explanations of anomalies, and a PAC theory of anomaly detection.
I'm currently studying anomaly detection in deep learning,
with a focus on anomaly detection in object classification for
open category/open set learning. Here is a presentation
summarizing my current hypotheses about how anomaly detection in
deep learning is different from anomaly detection in feature
vector data: Anomaly Detection for OOD and Novel Category Detection
My students and I have also developed a new benchmarking methodology for
comparing anomaly detection methods. Benchmark data sets and
scripts are available for download.
- Exogenous State MDPs
We are studying MDPs where some of the state variables cannot
be controlled by the policy. These exogenous state variables can
confuse reinforcement learning algorithms, so we seek methods for
identifying and ignoring these variables. This is a collaboration
with George Trimponias at Amazon. We published our initial results
at ICML 2018: Discovering and Removing Exogenous State Variables and Rewards
for Reinforcement Learning. PDF slides.
- Reviews, tutorials, and
books. I have written several review articles and tutorials on
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
If you are interested in robotics, I encourage you to visit
the Robotics Team
Pages to learn more about our excellent robotics program.
Professional Service, Journals, and Book Series
- I was a co-founder of Strands
(formerly MyStrands; formerly MusicStrands), a recommendation company
that was acquired by CRIP SVP in 2020.
- 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 was a spinout of the TaskTracer project.
- I am a co-founder and Chief Scientist
of BigML. BigML provides large scale cloud-based machine
learning services. It has a free tier that is a great place to play
around with various machine learning techniques.
Former Students and Staff
- Hussein Almuallim,
Oil and Energy Professional, Calgary, Canada.
- Eric Altendorf, Google.
Ashenfelter, Tignis, Inc. Seattle, Washington.
- Ghulum Bakiri, President at MicroCenter, Bahrain.
- Christian Baumberger. Software Engineer at Zuehlke Group
- Xinlong Bao.
Principal Software Engineer, Google Mountain View.
- Brian Breck.
- Waranun Bunjongsat.
- Giuseppe Cerbone. Independent Information Services Professional, Milan, Italy.
- Martha Chamberlin.
- Hei Chan. Assistant Professor / Project Researcher at the
Transdisciplinary Research Integration Center, Japan.
- Richard Charon.
- Eric Chown, Full Professor, Bowdoin College.
- Selina Chu, JPL, Pasadena, CA.
- Dan Corpron
- Mark Crowley, Assistant Professor, Department of Electrical and
Computer Engineering, University of Waterloo.
- Diane Damon, Damon Consulting, Portland, OR.
Dereszynski, Research Scientist, WebTrends, Portland, OR.
- Phuoc Do, Vida Lab.
Emmott. Software Engineer, Lacework.
- Nicholas Flann Associate Professor, Utah State University
- Greg Foltz.
- Dan Forrest.
- Tony Fountain, Director of the Cyberinfrastructure Lab for Environmental Observing Systems (CLEOS), UC San Diego.
- Ashit Gandhi, Founder and Vice-President, Prism Gem, LLC - The Art of Diamond Coloring.
Garrepalli Senior Deep Learning Research Engineer at Qualcomm
- Colin Gerety, Fort Collins, CO.
- Arwen Griffioen.
- Guohua Hao, Senior Data Scientist at iHeartRadio.
- Brandon Harvey, Symantec and Linn-Benton Community College.
- Hermann Hild, President, SMI Cognitive Software GmbH .
- Jesse Hostetler
Hutchinson, Assistant Professor of Computer Science and
Fishers and Wildlife, Oregon State University.
- Saket Joshi, Member of Technical Staff at Cycorp.
- Varad Joshi, Director of Engineering at Elemental Technologies.
- Caroline Koff, Hewlett-Packard Corporation, Fort Collins, CO.
Keiser, Research Programmer, CMU. Masters Thesis (PDF).
- Michael Kelm, Research Scientist, Siemens Healthcare.
- Eun Bae Kong, Professor, Computer Science, Chungnam National University, South Korea
- Bill Langford, Research Associate at RMIT, Melbourne, Australia.
Lin, VMWare, Seattle.
- Liping Liu,
Assistant Professor, Tufts University.
- Si Liu,
Postdoc, Fred Hutchinson Cancer Institute, Seattle.
- Dragos Margineantu, The Boeing Company.
- Gonzalo Martinez, Assistant Professor, Autonomous University of Madrid.
- Sean McGregor. XPRIZE.
- Prafulla Mishra, Software Development Manager at eBay.
- Avis Ng.
- Soumya Ray, Assistant Professor, Case-Western Reserve University.
- Angelo Restificar, Principal Machine Learning Engineer, EBay, Seattle.
- Ritchey Ruff, Senior SDET, Microsoft.
- Dan Sheldon, Assistant Professor, University of Massachusetts, Amherst.
- Jianqiang Shen. Research Scientist, PARC. Doctoral dissertation.
- Rongkun Shen.
Post-doc, Oregon Health and Science University, Portland.
Shindler, Lecturer at the University of Southern California
- Shriprakash Sinha. Ph.D. student TU Delft.
Sorower, Scientist at Philips Research North America
Stumpf. Senior Lecturer, City University London.
- Amelia Snyder, Intern at World Resources Institute
- Tao Sun, Graduate Student at UMass Amherst.
- Majid Alkaee
Taleghan. Senior Machine Learning Scientist at eSentire.
- Dan Vega, Senior Software Engineer at Valley Inception, LLC.
- Mark Vulfson. Microsoft Corporation.
Wang, Senior Scientist at Inome (Intelius).
Wettschereck. tarent solutions GmbH, Bonn, Germany.
- Pengcheng Wu.
- Michael Wynkoop, Qualcomm.
- Qing Yao, College of Informatics and Electronics. Zhejiang Sci-Tech University. Hangzhou, China.
Zemicheal, Applied Scientist, AI Infrastructure, nVidia, Austin, TX.
- Wei Zhang, The Boeing Company.
Zhang. Senior Software Engineer, Google. Doctoral Dissertation (PDF).
Zubek, Principal Statistician, Boehringer Ingelheim.
- Tsinghua Short Course
on Trustable Machine Learning, Fall 2018
- CS519/GEO599: Principles of
Ecosystem Informatics, 2004-2005.
- CS 534, Spring 2005, Machine
- CS430, Fall 2003, Introduction to
- CS539, Fall 2003, Seminar: Probabilistic
- 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
- CS 519, Fall 1996. Research Methods
in Computer Science.
- CS 450/550, Winter 1996, Introduction to Computer Graphics.
Machine Learning Resources
My Family's Musical Activities
Tom Dietterich, email@example.com