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2011

• Papers
  • Surface Sulfur Detection via Remote Sensing and Onboard Classification (in press).
    Lukas Mandrake, Umaa Rebbapragada, Kiri L. Wagstaff, David Thompson, Steve Chien, Daniel Tran, Robert T. Pappalardo, Damhnait Gleeson, and Rebecca Castano.
    ACM Transactions on Intelligent Systems and Technology, 2012.
    • A report on the use of SVMs and careful data analysis to develop a reliable surface sulfur detector from orbital data, with implications for the search for biosignatures on Europa.
  • Semi-Supervised Novelty Detection with Adaptive Eigenbases, and Application to Radio Transients (pdf, 11 pages, 625K). [Slides (pdf, 12 pages, 4M)] Received the Best Paper Award.
    David R. Thompson, Walid A. Majid, Colorado J. Reed, and Kiri L. Wagstaff.
    Proceedings of the Conference on Intelligent Data Understanding, 2011.
    • How to do novelty detection while ignoring anomalies you already know about and don't think are interesting.
  • Using Ensemble Decisions and Active Selection to Improve Low-Cost Labeling for Multi-View Data (pdf, 6 pages 275K).
    Umaa Rebbapragada and Kiri L. Wagstaff.
    Proceedings of the ICML Workshop on Combining Learning Strategies to Reduce Label Cost, 2011.
    • How sensors in a network can collaborate to improve their classification performance.
  • Machine Learning in Space: Extending Our Reach.
    Amy McGovern and Kiri L. Wagstaff.
    Machine Learning, 84(3):335-340, doi:10.1007/s10994-011-5249-4, 2011.
    • Why space missions need machine learning, and where we ML folk should be devoting our efforts.
  • Dynamic Landmarking for Surface Feature Identification and Change Detection (in press).
    Kiri L. Wagstaff, Julian Panetta, Adnan Ansar, Ronald Greeley, Mary Pendleton Hoffer, Melissa Bunte, and Norbert Schorghofer.
    ACM Transactions on Intelligent Systems and Technology, 2011.
    • Salience-based methods for finding interesting and unusual surface features on Mars, and a discussion of several such features we discovered, including new dark slope streaks and seasonally exposed bedforms.
  • V-FASTR: The VLBA Fast Radio Transients Experiment.
    Randall B. Wayth, Walter F. Brisken, Adam T. Deller, Walid A. Majid, David R. Thompson, Steven J. Tingay, and Kiri L. Wagstaff.
    The Astrophysical Journal, 735(2), doi: 10.1088/0004-637X/735/2/97, 2011.
  • Detection of fast radio transients with multiple stations: A case study using the Very Long Baseline Array.
    David R. Thompson, Kiri L. Wagstaff, Walter Brisken, Adam T. Deller, Walid A. Majid, Steven J. Tingay, and Randall B. Wayth.
    The Astrophysical Journal, 735(2), doi: 10.1088/0004-637X/735/2/98, 2011.
  • Data Clustering (in press).
    Kiri L. Wagstaff.
    Chapter in Advances in Machine Learning and Data Mining for Astronomy, Chapman & Hall/CRC Press, 2011.
    • Basic survey of clustering methods and how they can be useful in the context of astronomical data analysis.
• Abstracts and Posters
  • Transient Aeolian Features Detected Autonomously in HiRISE Images of El Dorado, Columbia Hills, Gusev Crater, Mars (pdf, 2 pages, 436K).
    Mary Pendleton Hoffer, Ronald Greeley, Kiri L. Wagstaff, and Adnan Ansar.
    42nd Lunar and Planetary Science Conference, March 2011.
    • Using automated change detection and landmark analysis, we identified several transient surface features in the El Dorado dune field. The features were likely caused by the activity of dust devils, both removing dust from the surface and later depositing it. The changes are subtle enough that detecting them manually can be very difficult.

2010

• Papers
  • Constrained Clustering.
    Kiri L. Wagstaff.
    Encyclopedia of Machine Learning, p. 220-221, Springer, 2010.
  • The Commensal Real-Time ASKAP Fast-Transients (CRAFT) Survey.
    Jean-Pierre Macquart, M. Bailes, N. D. R. Bhat, G.C. Bower, J.D. Bunton, S. Chatterjee, T. Colegate, J.M. Cordes, L. D'Addario, A. Deller, R. Dodson, R. Fender, K. Haines, P. Hall, C. Harris, A. Hotan, S. Johnston, D.L. Jones, M. Keith, J.Y. Koay, T.J.W. Lazio, W. Majid, T. Murphy, R. Navarro, C. Phillips, P. Quinn, R. A. Preston, B. Stansby, I. Stairs, B. Stappers, L. Staveley-Smith, S. Tingay, D. Thompson, W. van Straten, K. Wagstaff, M. Warren, R. Wayth, and L. Wen (the CRAFT Collaboration).
    Publications of the Astronomical Society of Australia, 27(3), p. 272-282, doi:10.1071/AS09082, June 2010.
    • This paper describes the goals of the CRAFT team, an international collaboration developing new ways to detect transient radio events in real-time with large radio array telescopes. It serves as a precursor to develop technology needed for the upcoming Square Kilometer Array (SKA).
  • Detecting Structural Microbial Biosignatures in Digital Images.
    Kiri L. Wagstaff and Frank A. Corsetti.
    Astrobiology, 10(4), p. 363-379, doi:10.1089/ast.2008.0301, May 2010.
    • Given an image of a rock that contains layered structures, is it possible to determine whether the layers were created by life (biogenic)? We evaluated several quantitative measures that capture the degree of complexity in visible structures, in terms of compressibility (to detect order) and the entropy (spread) of their intensity distributions. None of the techniques provided a consistent, statistically significant distinction between all biogenic and abiogenic samples, but the PNG compression ratio provided the strongest distinction and could inform future techniques.
  • Confidence-Based Feature Acquisition to Minimize Training and Test Costs (pdf, 11 pages, 624K).
    Marie desJardins, James MacGlashan, and Kiri L. Wagstaff.
    Proceedings of the SIAM Conference on Data Mining, p. 514-524, April 2010.
    • How can the best classifier be learned, when data features may be missing both while training and when classifying new items? Which missing features should be acquired, and in what order? This paper presents a greedy approach to achieving good performance with low feature acquisition costs.
  • Progressive Refinement for Support Vector Machines (pdf, 17 pages, 750K).
    Kiri L. Wagstaff, Michael Kocurek, Dominic Mazzoni, and Benyang Tang.
    Data Mining and Knowledge Discovery, 20(1), p. 53-69, doi:10.1007/s10618-009-0149-y, January 2010.
    • Progressive refinement starts with a rough classification result for all items and then iteratively refines the least-confidently-classified ones. This approach is ideal for applications that require "anytime" solutions that are always complete, such as onboard processing of camera images that informs spacecraft decisions.
  • Modeling and Learning Preferences over Sets (author's version pdf, 32 pages, 1.4M; official journal page).
    Kiri L. Wagstaff, Marie desJardins, and Eric Eaton.
    Journal of Experimental and Theoretical Artificial Intelligence, doi:10.1080/09528130903119336, vol. 22, issue 3, p. 237-268, 2010 (available online November 2009).
    • This work permits explicit control over the diversity and the depth of an automatically generated collection (e.g., music playlist, rover image download batch) through user preferences. The preferences can also be learned from examples of highly preferred sets assembled by the user.
• Abstracts and Posters
  • Machine Learning for Transient Detection with Radio Arrays (poster, pdf, 3.2M).
    David R. Thompson, Kiri L. Wagstaff, Randall Wayth, Adam Deller, and Steven Tingay.
    Astroinformatics 2010, June 2010.
  • Change Detection in Mars Orbital Images using Dynamic Landmarking (pdf, 2 pages, 596K).
    Kiri L. Wagstaff, Julian Panetta, Adnan Ansar, Melissa Bunte, Ronald Greeley, Mary Pendleton Hoffer, and Norbert Schorghofer.
    41st Lunar and Planetary Science Conference, March 2010.
    • We developed a content-based analysis of Mars orbital images to automatically identify and classify landmarks (impact craters, dust devil tracks, slope streaks, etc.) and then detect changes in the landmarks (both new and vanished).
  • Martian Slope Streaks form Sporadically throughout the Year (pdf, 2 pages, 237K).
    Christina M. King, Norbert Schorghofer, and Kiri L. Wagstaff.
    41st Lunar and Planetary Science Conference, March 2010.

2009

• Papers
  • Smart Robots on Mars: Deciding Where to Go and What to See (pdf, 9 pages, 6.1M).
    Kiri L. Wagstaff.
    Juniata Voices, vol. 9, 2009.
  • Mars Coordinated Science Networking Demonstration (pdf, 9 pages, 1.1M).
    Ross Jones, Andre Girerd, Kiri Wagstaff, Charles Baker, Rashied Amini, and Debarati Chattopadhyay.
    JPL Technical Report, 2009.
    • This report describes a concept for how existing Mars assets (orbiters and rovers) could use Delay-Tolerant Networking to support automated, coordinated science investigations.
  • Simulating and Detecting Radiation-Induced Errors for Onboard Machine Learning (pdf, 7 pages, 243K).
    Robert Granat, Kiri L. Wagstaff, Benjamin Bornstein, Benyang Tang, and Michael Turmon.
    Proceedings of the Third IEEE International Conference on Space Mission Challenges for Information Technology (SMC-IT), p. 125-131, July 2009.
  • How much Memory Radiation Protection do Onboard Machine Learning Algorithms Require? (pdf, 7 pages, 645K).
    Kiri L. Wagstaff and Benjamin Bornstein.
    Proceedings of the IJCAI-09/SMC-IT-09/IWPSS-09 Workshop on Artificial Intelligence in Space, July 2009.
    • An update to the ICML paper below, this work extends the analysis of ML algorithm radiation sensitivity to SVM classifiers as well. It also includes updated quantitative results for clustering, finding that radiation levels would have to increase by 9 orders of magnitude above the level observed in low-Earth orbit before any impact to performance was observed.
  • K-means in Space: A Radiation Sensitivity Evaluation (pdf, 8 pages, 283K). (Or watch the video)
    Kiri L. Wagstaff and Benjamin Bornstein.
    Proceedings of the Twenty-Sixth International Conference on Machine Learning (ICML), p. 1097-1104, June 2009.
    
    • The work described in this paper assesses how sensitive various k-means clustering algorithms are when exposed to destructive radiation during the clustering process. One key finding was that, for the data sets tested, k-means can operate in low Earth orbit radiation levels without requiring rad-hardened memory. Also, subsampling k-means was more radiation resistant than kd-kmeans. [Note: An error in units conversion caused the experiments in this paper to over-estimate the impact of radiation. See "How much Memory Radiation Protection do Onboard Machine Learning Algorithms Require?" above for updated results.]
  • Improving Onboard Analysis of Hyperion Images by Filtering Mislabeled Training Data Examples (pdf, 9 pages, 751K).
    Umaa Rebbapragada, Lukas Mandrake, Kiri L. Wagstaff, Damhnait Gleeson, Rebecca Castano, Steve Chien, and Carla E. Brodley.
    Proceedings of the 30th IEEE Aerospace Conference, March 2009.
  • Onboard Detection of Natural Sulfur on a Glacier via a SVM and Hyperion Data (pdf, 14 pages, 7.5M).
    Lukas Mandrake, Kiri L. Wagstaff, Damhnait Gleeson, Umaa Rebbapragada, Daniel Tran, Rebecca Castano, Steven Chien, and Robert Pappalardo.
    Proceedings of the 30th IEEE Aerospace Conference, March 2009.

2008

• Book/thesis
  
  • Biogenicity Analysis for Stromatolite Structures.
    Kiri L. Wagstaff. Master's Thesis (Geological Sciences), University of Southern California, 2008. (abstract as a wordle)
  • Constrained Clustering: Advances in Algorithms, Theory, and Applications.
    Edited by Sugato Basu, Ian Davidson, and Kiri L. Wagstaff, Chapman & Hall/CRC Press, 2008.
• Papers
  • Multiple-Instance Regression with Structured Data (pdf, 10 pages, 307K).
    Kiri L. Wagstaff, Terran Lane, and Alex Roper.
    Proceedings of the 4th International Workshop on Mining Complex Data, December 2008.
  • From Protocol Specification to Statechart to Implementation (pdf, 13 pages, 560K).
    Kiri L. Wagstaff, Ken Peters, and Lucas Scharenbroich.
    JPL Technical Report, October 2008.
  • Automatic Code Generation for Instrument Flight Software (pdf, 8 pages, 289K).
    Kiri L. Wagstaff, Edward Benowitz, DJ Byrne, Ken Peters, and Garth Watney.
    Proceedings of the 9th International Symposium on Artificial Intelligence, Robotics, and Automation in Space, February 2008.
  • Onboard Detection of Active Canadian Sulfur Springs (pdf, 8 pages, 235K).
    Rebecca Castano, Kiri Wagstaff, Damhnait Gleeson, Robert Pappalardo, Steve Chien, Daniel Tran, Lucas Scharenbroich, Benyang Tang, Brian Bue, and Thomas Doggett.
    Proceedings of the 9th International Symposium on Artificial Intelligence, Robotics, and Automation in Space, February 2008.
  • Observations of the North Polar Water Ice Annulus on Mars using THEMIS and TES.
    Kiri L. Wagstaff, Timothy N. Titus, Anton B. Ivanov, and Rebecca Castano.
    Planetary and Space Science, v. 56, p. 256-265, 2008.
• Abstracts and Posters
  • Automatic Landmark Identification in Mars Orbital Imagery.
    Kiri L. Wagstaff, Julian Panetta, Ron Greeley, Norbert Schorghofer, Melissa Bunte, Mary Pendleton Hoffer, and Adnan Ansar.
    Eos Transactions of the American Geophysical Union, 89(53), Fall Meeting Supplement, Abstract #P53C-1469, December 2008.
  • In Situ Assessment of Potential Biogenicity using Image Analysis.
    Kiri L. Wagstaff and Frank Corsetti.
    Astrobiology Science Conference, April 2008.
    Abstract appears in Advances in Astrobiological Instrumentation Development by Sanjoy Som, Steve Vance, Inge ten Kate, and Andrew Steele. Astrobiology, 8(2): 296-301, 2008.

2007

• Papers
  • On-board Analysis of Uncalibrated Data for a Spacecraft at Mars (pdf, 9 pages, 849K).
    Rebecca Castano, Kiri L. Wagstaff, Steve Chien, Timothy M. Stough, and Benyang Tang.
    Proceedings of the Thirteenth International Conference on Knowledge Discovery and Data Mining (KDD), p. 922-930, August 2007.
  • Salience Assignment for Multiple-Instance Regression (pdf, 6 pages, 223K).
    Kiri L. Wagstaff and Terran Lane.
    Proceedings of the ICML 2007 Workshop on Constrained Optimization and Structured Output Spaces, June 2007.
  • The 2006 SIGART/AAAI Doctoral Consortium.
    Kiri L. Wagstaff and Terran Lane.
    AI Magazine, vol. 28, no. 1, p. 84-85, 2007.
  • Value, Cost, and Sharing: Open Issues in Constrained Clustering (pdf, 10 pages, 384K).
    Kiri L. Wagstaff.
    Proceedings of the Fifth International Workshop on Knowledge Discovery in Inductive Databases (KDID 2006), LNCS 4747, p. 1-10, 2007.
• Abstracts and Posters
  • Surface Change Detection from Mars Orbital Imagery.
    Baback Moghaddam, Brian D. Bue, Rebecca Castano, and Kiri L. Wagstaff.
    Eos Transactions of the American Geophysical Union, 88(52), Fall Meeting Supplement, Abstract #P33A-1020, December 2007.
  • County-Level Crop Yield Prediction Using Remote Sensing Data.
    Kiri L. Wagstaff, Alex Roper, and Terran Lane.
    Eos Transactions of the American Geophysical Union, 88(52), Fall Meeting Supplement, Abstract #B13D-1526, December 2007.
  • Automatic Onboard Detection of Planetary Volcanism from Images (pdf, 2 pages, 173K).
    Brian Bue, Kiri L. Wagstaff, Rebecca Castano, and Ashley Davies.
    38th Lunar and Planetary Science Conference, March 2007.

2006

• Papers
  • Measuring Constraint-Set Utility for Partitional Clustering Algorithms (pdf, 12 pages, 276K).
    Ian Davidson, Kiri L. Wagstaff, and Sugato Basu.
    Proceedings of the Tenth European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), p. 115-126, September 2006.
  • Active Learning with Irrelevant Examples (pdf, 8 pages, 395K).
    Dominic Mazzoni, Kiri L. Wagstaff, and Michael Burl.
    Proceedings of the Seventeenth European Conference on Machine Learning (ECML), p. 695-702, September 2006.
  • Learning User Preferences for Sets of Objects (pdf, 8 pages, 270K).
    Marie desJardins, Eric Eaton, and Kiri L. Wagstaff.
    Proceedings of the Twenty-Third International Conference on Machine Learning (ICML), p. 273-280, June 2006.
  • Recent HARVIST Results: Classifying Crops from Remote Sensing Data (pdf, 5 pages, 197K).
    Kiri Wagstaff and Dominic Mazzoni.
    Proceedings of the Second NASA Data Mining Workshop, May 2006.
  • The 2005 SIGART/AAAI Doctoral Consortium.
    Kiri L. Wagstaff.
    AI Magazine, vol. 27, no. 1, p. 96, 2006.
• Abstracts and Posters
  • Real-Time, In-Situ Detection of Dust Devils using Pressure Sensors (html).
    Kiri L. Wagstaff, Rebecca Castano, and Steve Chien.
    Eos Transactions of the American Geophysical Union, 87(52), Fall Meeting Supplement, Abstract #IN52A-03, December 2006.
  • Fast, Interactive Analysis of Remote Sensing Data with the HARVIST System (html).
    Michael J. Kocurek, Kiri L. Wagstaff, Dominic Mazzoni, Stephan R. Sain, Lucas Scharenbroich, and Timothy M. Stough.
    Eos Transactions of the American Geophysical Union, 87(52), Fall Meeting Supplement, Abstract #IN21A-1204, December 2006.
  • Automatic Plume Detection for Planetary Bodies (html).
    Brian Bue, Kiri L. Wagstaff, Rebecca Castano, and Ashley Davies.
    Eos Transactions of the American Geophysical Union, 87(52), Fall Meeting Supplement, Abstract #IN52A-04, December 2006.
  • Automating the Detection of Enceladus-Style Plumes (html).
    Kiri L. Wagstaff, Becky Castano, Ashley Davies, and Brian Bue.
    Division for Planetary Sciences meeting #38, Bulletin of the American Astronomical Society, Vol. 38, p. 522, October 2006.
  • When is Constrained Clustering Beneficial, and Why? (pdf, 2 pages, 71K).
    Kiri L. Wagstaff, Sugato Basu, and Ian Davidson.
    AAAI Member Abstracts and Posters, Proceedings of the Twenty-first National Conference on Artificial Intelligence (AAAI), July 2006.
  • Detecting Dust Storms and Water Ice Clouds Onboard THEMIS (html -- note incorrect citation; should be Spring 2006, not Fall 2007 meeting).
    Kiri L. Wagstaff, Joshua L. Bandfield, Rebecca Castano, Steve Chien, Michael D. Smith, and Timothy M. Stough.
    Spring AGU Joint Assembly, Revolutionary Space Exploration Concepts using Onboard Computing, May 2006.
  • Evidence of Life or Not? Scale Sensitive Analysis of Stromatolite Biogenicity.
    Kiri L. Wagstaff and Frank A. Corsetti.
    Third Annual Southern California Geobiology Symposium, April 2006.
  • Dust Storms and Water Ice Clouds: Feature Detection for Use Onboard THEMIS (pdf, 2 pages, 216K).
    Kiri L. Wagstaff, Joshua L. Bandfield, Rebecca Castano, Steve Chien, and Michael D. Smith.
    37th Lunar and Planetary Science Conference, March 2006.

2005

• Papers
  • Semi-Supervised Data Summarization: Using Spectral Libraries to Improve Hyperspectral Clustering (pdf, 8 pages, 635K).
    Kiri L. Wagstaff, Henry P. Shu, Dominic Mazzoni, and Rebecca Castano.
    The Interplanetary Network Progress Report, vol. 42-163, 2005.
  • Forecasting Space Weather: Predicting Interplanetary Shocks using Neural Networks (pdf, 5 pages, 244K).
    Jon Vandegriff, Kiri Wagstaff, George Ho and Janice Plauger.
    Advances in Space Research, vol. 36, issue 12, p. 2323-2327, 2005.
  • Active Constrained Clustering by Examining Spectral Eigenvectors (pdf, 14 pages, 681K).
    Qianjun Xu, Marie desJardins, and Kiri L. Wagstaff.
    Proceedings of the Eighth International Conference on Discovery Science, p. 294-307, 2005.
  • An Onboard Data Analysis Method to Track the Seasonal Polar Caps on Mars (pdf, 8 pages, 2.8M).
    Kiri L. Wagstaff, Rebecca Castano, Steve Chien, Anton B. Ivanov, and Timothy N. Titus.
    Proceedings of the International Symposium on Artificial Intelligence, Robotics, and Automation in Space, 2005.
  • DD-PREF: A Language for Expressing Preferences Over Sets (pdf, 7 pages, 597K).
    Marie desJardins and Kiri L. Wagstaff.
    Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI), p. 620-626, 2005.
  • HARVIST: A System for Agricultural and Weather Studies Using Advanced Statistical Models (pdf, 5 pages, 3.5M).
    Kiri L. Wagstaff, Dominic Mazzoni, and Stephan Sain.
    Proceedings of the Earth-Sun Systems Technology Conference (ESTC), 2005.
  • Validating Rover Image Prioritizations (pdf, 8 pages, 354K).
    Rebecca Castano, Kiri Wagstaff, Lin Song, and Robert C. Anderson.
    The Interplanetary Network Progress Report, vol. 42-160, 2005.
  • Constrained Spectral Clustering under a Local Proximity Structure (pdf, 2 pages, 55K).
    Qianjun Xu, Marie desJardins, and Kiri Wagstaff.
    Proceedings of the 18th International Florida Artificial Intelligence Research Society (FLAIRS) Conference, p. 866-867, 2005.
  • A Context-Sensitive and User-Centric Approach to Developing Personal Assistants (pdf, 3 pages, 40K).
    Marie desJardins, Eric Eaton, and Kiri Wagstaff.
    Persistent Assistants: Living and Working with AI (AAAI Spring Symposium), 2005.
  • Current Results from a Rover Science Data Analysis System (pdf, 10 pages, 696K).
    Rebecca Castano, Michele Judd, Tara Estlin, Robert C. Anderson, Daniel Gaines, Andres Castano, Ben Bornstein, Tim Stough, and Kiri Wagstaff.
    Proceedings of the 2005 IEEE Aerospace Conference, 2005.
  • Making the Most of Missing Values: Object Clustering with Partial Data in Astronomy (pdf, 5 pages, 85K).
    Kiri Wagstaff and Victoria G. Laidler.
    Astronomical Data Analysis Software and Systems XIV (ADASS), ASP Conference Series, Vol. 347, 2005.
• Abstracts and Posters
  • Revolutionising Science-Driven Deep Space Mission Operations Using Autonomously-Operating Spacecraft as Demonstrated with ASE on EO-1 (html).
    Davies, Chien, Doggett, Ip, Dohm, Greeley, Baker, Castano, Sherwood, and Wagstaff.
    Eos Transactions of the American Geophysical Union, 86(52), Fall Meeting Supplement, Abstract #P41A-0924, December 2005.
  • Compression Analysis and the Biogenic Modification of Abiogenic Substrates (html).
    Frank A. Corsetti, Kiri L. Wagstaff, Nicole Bonuso, and Matthew Smith.
    Annual Meeting of the Geological Society of America (GSA), 2005.
  • Structural Biosignature Detection from Rover Images.
    Kiri L. Wagstaff and Frank A. Corsetti.
    Second Astrobiology Graduate Conference, 2005.
  • Towards Onboard Orbital Tracking of Seasonal Polar Volatiles on Mars (pdf, 2 pages, 115K).
    Kiri L. Wagstaff, Rebecca Castano, Steve Chien, Anton B. Ivanov, and Timothy N. Titus.
    36th Lunar and Planetary Science Conference, 2005.
  • Tracking Retreat of the North Seasonal Ice Cap on Mars: Results from the THEMIS Investigation (pdf, 2 pages, 860K).
    Anton B. Ivanov, Kiri L. Wagstaff, and Timothy N. Titus.
    36th Lunar and Planetary Science Conference, 2005.

2004

• Papers
  • Using Trained Pixel Classifiers to Select Images of Interest (pdf, 8 pages, 262K).
    Dominic Mazzoni, Kiri Wagstaff, and Rebecca Castano.
    The Interplanetary Network Progress Report, vol. 42-158, 2004.
  • Clustering with Missing Values: No Imputation Required (pdf, 10 pages, 244K).
    Kiri Wagstaff.
    Classification, Clustering, and Data Mining Applications (Proceedings of the Meeting of the International Federation of Classification Societies), p. 649-658, 2004.
  • Mining GPS Traces for Map Refinement (pdf, 29 pages, 837K).
    Stefan Schroedl, Kiri Wagstaff, Seth Rogers, Pat Langley, and Christopher Wilson.
    Data Mining and Knowledge Discovery, vol. 9, issue 1, p. 59-87, 2004.
• Abstracts and Posters
  • How to Leverage Existing Spectral Knowledge when Clustering Hyperspectral Data (html).
    Kiri Wagstaff, Henry Shu, and Rebecca Castano.
    Eos Transactions of the American Geophysical Union, 85(52), Fall Meeting Supplement, Abstract #NG43A-0439, December 2004.
  • Science-Based Region-of-Interest Image Compression (pdf, 2 pages, 68K).
    Kiri Wagstaff, Rebecca Castano, Sam Dolinar, Matt Klimesh, and Ryan Mukai.
    35th Lunar and Planetary Science Conference, March 2004.
  • Multiangle Remote Sensing of Optically Thin Cirrus Clouds from MISR using Support Vector Machines (html).
    Michael Garay, Dominic Mazzoni, Roger Davies, and Kiri Wagstaff.
    Spring AGU, Abstract #GC51A-03, 2004.

2003

• Abstracts and Posters
  • Automated Analysis of Mars Multispectral Observations (pdf, 3 pages, 195K).
    Kiri Wagstaff and James F. Bell III.
    Sixth International Mars Conference, 2003.
  • Forecasting Space Weather: Using ACE Data to Provide Real-time Predictions of High-Intensity Energetic Storm Particle Events (pdf, 2 pages, 11K).
    Kiri Wagstaff, Jon Vandegriff, George C. Ho, and Janice Plauger.
    Spring AGU; Geophysical Research Abstracts, vol. 5, p. 14542-3, 2003.
  • A Hybrid Global MISR Cloud Mask using Support Vector Machines and Active Learning.
    Michael Garay, Dominic Mazzoni, Roger Davies, Kiri Wagstaff, Dennis DeCoste, and Amy Braverman.
    Eos Transactions of the American Geophysical Union, 84(46), Fall Meeting Supplement, Abstract #H11F-0913, December 2003.
  • Real-time Upstream Monitoring System: Using ACE Data to Predict the Arrival of Interplanetary Shocks (html).
    Michelle Donegan, Kiri Wagstaff, George C. Ho, and Jon Vandegriff.
    Eos Transactions of the American Geophysical Union, 84(46), Fall Meeting Supplement, Abstract #SH42A-0479, December 2003.

2002

• Dissertation
  • Intelligent Clustering with Instance-Level Constraints (pdf, 140 pages, 980K)
    Kiri L. Wagstaff. Ph.D. Dissertation (Computer Science), Cornell University, 2002.
    (See also: a more detailed page with individual chapters)

2001

• Papers
  • Constrained K-means Clustering with Background Knowledge (ps, 8 pages, 225K; [pdf version]).
    Kiri Wagstaff, Claire Cardie, Seth Rogers, and Stefan Schroedl.
    Proceedings of the International Conference on Machine Learning (ICML), p. 577-584, 2001.
  • Generalized Clustering, Supervised Learning, and Data Assignment (ps, 6 pages, 238K).
    Annaka Kalton, Pat Langley, Kiri Wagstaff, and Jungsoon Yoo.
    Proceedings of the Sixth International Conference on Knowledge Discovery and Data Mining (KDD), p. 299-304, 2001.
  • Multi-document Summarization via Information Extraction (pdf, 7 pages, 49K).
    Mike White, Tanya Korelsky, Claire Cardie, Vincent Ng, David Pierce, and Kiri Wagstaff.
    Proceedings of the Human Language Technology (HLT) Conference, 2001.
  • Detecting Discrepancies and Improving Intelligibility: Two Preliminary Evaluations of RIPTIDES (pdf, 16 pages, 49K).
    Mike White, Claire Cardie, Vincent Ng, Kiri Wagstaff, and Daryl McCullough.
    DUC Workshop on Text Summarization, 2001.

2000

• Papers
  • Refining Bias in Clustering Algorithms via Problem-Specific Constraints (ps, 17 pages, 284K).
    Kiri Wagstaff. Thesis proposal, 2000.
  • Clustering with Instance-level Constraints (ps, 8 pages, 224K; [pdf version]).
    Kiri Wagstaff and Claire Cardie.
    Proceedings of the International Conference on Machine Learning (ICML), p. 1103-1110, 2000.
  • Alpha Seeding for Support Vector Machines (ps, 5 pages, 205K).
    Dennis DeCoste and Kiri Wagstaff.
    Proceedings of the Sixth International Conference on Knowledge Discovery and Data Mining (KDD), p. 345-349, 2000.
• Abstracts and Posters
  • Refining Inductive Bias in Unsupervised Learning via Constraints (pdf, 1 page, 9K).
    Kiri Wagstaff.
    AAAI-2000 Doctoral Consortium; Proceedings of the Seventeenth National Conference on Artificial Intelligence (AAAI), p. 1112, 2000.
  • Clustering with Instance-level Constraints (pdf, 1 page, 11K).
    Kiri Wagstaff and Claire Cardie.
    AAAI-2000 Student Session; Proceedings of the Seventeenth National Conference on Artificial Intelligence, p. 1097, 2000.

1999

• Papers
  • Noun Phrase Coreference as Clustering (ps, 8 pages, 219K).
    Claire Cardie and Kiri Wagstaff.
    Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora (EMNLP), p. 82-89, 1999.
  • Extravehicular Activity Suit Systems Design: How to Walk, Talk, and Breathe on Mars (pdf, 22 pages, 485K).
    George Barton, Akio Cox, Lauren DeFlores, Alison Diehl, Ari Garber, Randall Goldsmith, Joel Haenlein, Alex Iglecia, Kerri Kusza, Brett Lee, Saemi Mathews, Jonathan Mitchell, Abigail Ross, Rachel Sanchez, Stephen Shannon, Sri Priya Sundararajan, Mike Valdepenas, and Kiri Wagstaff. HEDS-UP, 1999.