Tina Eliassi-Rad's Publications

(DBLP, Google Scholar)

  • PORTFILER: Port-Level Network Profiling for Self-Propagating Malware Detection (with T. Ongun, O. Spohngellert, B. Miller, S. Boboila, A. Oprea, J. Hiser, A. Nottingham, J. Davidson, and M. Veeraraghavan), In Proceedings of the 9th IEEE Conference on Communications and Network Security (IEEE CNS), 2021.

  • PATHATTACK: Attacking Shortest Paths in Complex Networks (with B.A. Miller, Z. Shafi, W. Ruml, Y. Vorobeychik, and S. Alfeld), In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2021.

  • Optimal Edge Weight Perturbations to Attack Shortest Paths (with B.A. Miller, Z. Shafi, W. Ruml, Y. Vorobeychik, and S. Alfeld), arXiv:2107.03347, July 2021.

  • Information Access Equality on Network Generative Models (with X. Wang and O. Varol), arXiv:2107.02263, July 2021.

  • Measuring Algorithmically Infused Societies (with C. Wagner, M. Strohmaier, A. Olteanu, E. Kiciman, and N. Contractor), Nature 595, pp. 197-204, 2021. [DOI]

  • The Why, How, and When of Representations for Complex Systems (with L. Torres, A. Sizemore Blevins, and D.S. Bassett), SIAM Review 63(3), pp. 435-485, 2021.

  • Nonbacktracking Eigenvalues under Node Removal: X-Centrality and Targeted Immunization (with L. Torres, K.S. Chan, and H. Tong), SIAM Journal on Mathematics of Data Science 3(2), pp. 656-675, 2021.

  • Collaborative Information Sharing for ML-Based Threat Detection (with T. Ongun, S. Boboila, A. Oprea, A. Nottingham, J. Hiser, and J.W. Davidson), arXiv:2104.11636, April 2021.

  • Selective Network Discovery via Deep Reinforcement Learning on Embedded Spaces (with P. Morales and R. Caceres), Applied Network Science 6(1), Article 24, 2021.

  • RAWLSNET: Altering Bayesian Networks to Encode Rawlsian Fair Equality of Opportunity (with D. Liu, Z. Shafi, W. Fleisher, and S. Alfeld), Proceedings of the 2021 AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES), pp. 745-755, 2021.

  • POTION: Optimizing Graph Structure for Targeted Diffusion (with S. Yu, L. Torres, S. Alfeld, and Y. Vorobeychik), Proceedings of the 2021 SIAM International Conference on Data Mining (SDM), pp. 154-162, 2021.

  • Collaborative Information Sharing for ML-Based Threat Detection (with T. Ongun, S. Boboila, A. Oprea, A. Nottingham, J. Hiser, and J. Davidson), The 2021 SIAM SDM Workshop on AI for Cybersecurity, April 2021.

  • Exploring Impossibility Results for Algorithmic Fairness Using PrSAT (with B. Fitelson), Technical Report, March 2021.

  • Understanding the Limitations of Network Online Learning (with T. LaRock, T. Sakharov, and S. Bhadra), Applied Network Science 5(1), Article 60, 2020.

  • What Science Can Do For Democracy: A Complexity Science Approach (with H. Farrell, D. Garcia, S. Lewandowsky, P. Palacios, D. Ross, D. Sornette, K. Thebault, and K. Wiesner), Humanities & Social Sciences Communications 7, Article 30, 2020.

  • GLEE: Geometric Laplacian Eigenmap Embedding (with L. Torres and K. S. Chan), Journal of Complex Networks 8(2), cnaa007, 2020.

  • Residual Core Maximization: An Efficient Algorithm for Maximizing the Size of the k-Core (with R. Laishram, A.E. Sariyüce, A. Pinar, and S. Soundarajan), Proceedings of the 2020 SIAM International Conference on Data Mining (SDM), pp. 325-333, 2020.

  • HYPA: Efficient Detection of Path Anomalies in Time Series Data on Networks (with T. LaRock, V. Nanumyan, I. Scholtes, G. Casiraghi, and F. Schweitzer), Proceedings of the 2020 SIAM International Conference on Data Mining (SDM), pp. 460-468, 2020.

  • Reshaping A Nation: Mobility, Commuting, and Contact Patterns during the COVID-19 Outbreak (with B. Klein, T. LaRock, S. McCabe, L. Torres, L. Friedland, F. Privitera, B. Lake, M.U.G. Kraemer, J. S. Brownstein, D. Lazer, S.V. Scarpino, A. Vespignani, and M. Chinazzi), Network Science Institute Technical Report. Northeastern University, Boston, MA, May 2020.

  • Assessing Changes in Commuting and Individual Mobility in Major Metropolitan Areas in the United States during the COVID-19 Outbreak (with B. Klein, T. LaRock, S. McCabe, L. Torres, F. Privitera, B. Lake, M.U.G. Kraemer, J.S. Brownstein, D. Lazer, S.V. Scarpino, M. Chinazzi, and A. Vespignani), Network Science Institute Technical Report. Northeastern University, Boston, MA, March 2020.

  • Topological Effects on Attacks Against Vertex Classification (with B.A. Miller, M. Çamurcu, A.J. Gomez, and K.S. Chan), arXiv:2003.05822v1, March 2020.

  • Deep Reinforcement Learning for Task-driven Discovery of Incomplete Networks (with P. Morales and R. S. Caceres), Proceedings of the 8th International Conference on Complex Networks and their Applications (COMPLEX NETWORKS), pp. 903-914, 2019.

  • Success in Books: Predicting Book Sales Before Publication (with X. Wang, B. Yucesoy, O. Varol, and A.-L. Barabási), EPJ Data Science 8(1), Article 31, 2019.

  • Improving Robustness to Attacks Against Vertex Classification (with B.A. Miller, M. Çamurcu, A. Gomez, and K. Chan), The 15th International Workshop on Mining and Learning with Graphs (MLG), Anchorage, AL, August 2019. [Best Paper Award]

  • L2P: An Algorithm for Estimating Heavy-tailed Outcomes (with X. Wang and O. Varol), arXiv:1908.04628v1, August 2019.

  • Non-backtracking Cycles: Length Spectrum Theory and Graph Mining Applications (with L. Torres and P. Suárez-Serrato), Applied Network Science 4(1), Article 41, 2019.

  • Multilevel Network Alignment (with S. Zhang, H. Tong, and R. Maciejewski), Proceedings of the 2019 World Wide Web Conference (WWW), pp. 2344-2354, 2019.

  • Stability of Democracies: A Complex Systems Perspective (with K. Wiesner, A. Birdi, H. Farrell, D. Garcia, S. Lewandowsky, P. Palacios, D. Ross, D. Sornette, and K. Thébault), European Journal of Physics, November 2018. (Press Release)

  • Robustness Analysis and Anomaly Detection of Interdependent Physical and Social Networks (with T. Abdelzaher, J. Han, and C. Faloutsos), Technical Report (HDTRA1-10-1-0120), University of Illinois Urbana-Champaign, September 2018.

  • Reducing Network Incompleteness Through Online Learning: A Feasibility Study (with T. LaRock, T. Sakharov, and S. Bhadra), The 14th International Workshop on Mining and Learning with Graphs (MLG), London, United Kingdon, August 2018.

  • Measuring and Improving the Core Resilience of Networks (with R. Laishram, A.E. Sariyüce, A. Pinar, and S. Soundarajan), Proceedings of the 2018 World Wide Web Conference (WWW), pp. 609-618, 2018.

  • Patterns and Anomalies in K-cores of Real-world Graphs with Applications (with K. Shin and C. Faloutsos), Knowledge and Information Systems 54(3), pp. 677-710, 2018.

  • ε-WGX: Adaptive Edge Probing for Enhancing Incomplete Networks (with S. Soundarajan, A. Pinar, and B. Gallagher), Proceedings of the 2017 ACM on Web Science Conference (WebSci), pp. 161-170, 2017.

  • CoreScope: Graph Mining Using k-Core Analysis--Patterns, Anomalies, and Algorithms (with K. Shin and C. Faloutsos), Proceedings of the 16th IEEE International Conference on Data Mining (ICDM), pp. 469-478, 2016.

  • Some Advances in Role Discovery in Graphs (with S. Gilpin, C.-T. Kuo, and I. Davidson), arXiv:1609.02646v1, September 2016.

  • MaxReach: Reducing Network Incompleteness through Node Probes (with S. Soundarajan, B. Gallagher, and A. Pinar), Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 152-157, 2016.

  • NimbleCore: A Space-efficient External Memory Algorithm for Estimating Core Numbers (with P. Govindan, S. Soundarajan, and C. Faloutsos), Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 207-214, 2016.

  • GOTCHA! Network-based Fraud Detection for Social Security Fraud (with V. Van Vlasselaer, L. Akoglu, M. Snoeck, and B. Baesens), Management Science, pp 1-21, July 2016. [permalink]

  • Eigen-Optimization on Large Graphs by Edge Manipulation (with C. Chen, H. Tong, B.A. Prakash, M. Faloutsos, and C. Faloutsos), ACM Transactions on Knowledge Discovery in Data 10(4), Article 49, June 2016.

  • Current and Future Challenges in Mining Large Networks: Report on the Second SDM Workshop on Mining Networks and Graphs (with L.B. Holder, R. Caceres, D.F. Gleich, E.J. Riedy, M. Khan, N.V. Chawla, R. Kumar, Y. Wu, C. Klymko, and B.A. Prakash), SIGKDD Explorations 18(1), pp. 39-45, 2016.

  • Fast Best-Effort Search on Graphs with Multiple Attributes (with S. Basu Roy and S. Papadimitriou), Proceedings of the 32nd IEEE International Conference on Data Engineering (ICDE), pp. 1574-1575, 2016.

  • Generating Graph Snapshots from Streaming Edge Data (with S. Soundarajan, A. Tamersoy, E.B. Khalil, D.H. Chau, B. Gallagher, and K. Roundy), Proceedings of the 25th International World Wide Web Conference (WWW), pp. 109-110, 2016.

  • Node Immunization on Large Graphs: Theory and Algorithms (with C. Chen, H. Tong, B.A. Prakash, C.E. Tsourakakis, C. Faloutsos, D.H. Chau), IEEE Transactions on Knowledge and Data Engineering 28(1), pp. 113-126, 2016.

  • MaxOutProbe: An Algorithm for Increasing the Size of Partially Observed Networks (with S. Soundarajan, B. Gallagher, and A. Pinar), NIPS Workshop on Networks in the Social and Information Sciences, Montreal, Canada, December 2015.

  • A Probabilistic Model for Using Social Networks in Personalized Item Recommendation (with A. Chaney and D. Blei), Proceedings of the 9th ACM Recommender Systems Conference (RecSys), pp. 43-50, 2015.

  • AFRAID: Fraud Detection via Active Inference in Time-evolving Social Networks (with V. Van Vlasselaer, L. Akoglu, M. Snoeck, and B. Baesens), Proceedings of the 7th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 659-666, 2015.

  • Minimizing Dissemination in a Population While Maintaining its Community Structure (with C. Zhang), The 1st ACM SIGKDD Workshop on Population Informatics for Big Data (PopInfo), Sydney, Australia, August 2015.

  • APATE: A Novel Approach for Automated Credit Card Transaction Fraud Detection using Network-Based Extensions (with C. Bravo, V. Van Vlasselaer, O. Caelen, L. Akoglu, M. Snoeck, and B. Baesens), Decision Support Systems 75(1), pp. 38-48, 2015.

  • MET: A Fast Algorithm for Minimizing Propagation in Large Graphs with Small Eigen-Gaps (with L.T. Le and H. Tong), Proceedings of the 2015 SIAM International Conference on Data Mining (SDM), pp. 694-702, 2015.

  • EP-MEANS: An Efficient Nonparametric Clustering of Empirical Probability Distributions (with K. Henderson and B. Gallagher), Proceedings of the 30th ACM SIGAPP Symposium On Applied Computing (SAC), pp. 893-900, 2015.

  • Guilt-by-Constellation: Fraud Detection by Suspicious Clique Memberships (with V. Van Vlasselaer, L. Akoglu, M. Snoeck, and B. Baesens), Proceedings of the 48th Annual Hawaii International Conference on System Sciences (HICSS), pp. 918-927, 2015.

  • Fast Best-Effort Search on Graphs with Multiple Attributes (with S. Basu Roy and S. Papadimitriou), IEEE Transactions on Knowledge and Data Engineering 27(3), pp. 755-768, 2015.

  • A Proposal for Decreasing Geographical Inequality in College Admissions (with B. Fitelson), Chapter 12 Appendix in The Future of Affirmative Action, Eds: J. Renker and J. Miller, The Century Foundation Press, 2014.

  • Threatening Privacy across Social Graphs: A Structural Features Approach (with P. Govindan, J. Xu, S. Hill, and C. Volinsky), Workshops Proceedings of the 14th IEEE International Conference on Data Mining (ICDM Workshops), pp. 554-561, 2014.

  • Finding the Most Appropriate Auxiliary Data for Social Graph De-anonymization (with P. Govindan and S. Soundarajan), The 1st ACM SIGKDD Workshop on Data Ethics, New York, NY, August 2014.

  • Measuring Coverage and Divergence of Reading Behaviors Among Friends (with L.T. Le), The 1st ACM SIGKDD Workshop on Data Science for News Publishing (NewsKDD'14), New York, NY, August 2014.

  • A Guide to Selecting a Network Similarity Method (with S. Soundarajan and B. Gallagher), Proceedings of the 2014 SIAM International Conference on Data Mining (SDM), pp. 1037-1045, 2014.

  • Social Order in Online Social Networks, Encyclopedia of Social Network Analysis and Mining, pp. 1918-1920, 2014, Springer.

  • Hyperlocal: Inferring Location of IP Addresses in Real-time Bid Requests for Mobile Ads (with L.T. Le, F. Provost, and L. Moores), Proceedings of the 6th ACM SIGSPATIAL International Workshop on Location-Based Social Networks (LBSN), pp. 24-33, 2013.

  • Network Similarity via Multiple Social Theories (with M. Berlingerio, D. Koutra, and C. Faloutsos), Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 1439-1440, 2013. [Long version]

  • Guided Learning for Role Discovery (GLRD): Framework, Algorithms, and Applications (with S. Gilpin and I. Davidson), Proceedings of the 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp. 113-121, 2013.

  • Entelecheia: Detecting P2P Botnets in their Waiting Stage (with H. Hang, X. Wei, and M. Faloutsos), Proceedings of the 12th IEEE IFIP Networking Conference (Networking), pp. 1-9, 2013.

  • Gelling, and Melting, Large Graphs by Edge Manipulation (with H. Tong, B.A. Prakash, M. Faloutsos, and C. Faloutsos), Proceedings of the 21st ACM Conference on Information and Knowledge Management (CIKM), pp. 245-254, 2012. [Best Paper Award]

  • RolX: Structural Role Extraction and Mining in Large Networks (with K. Henderson, B. Gallagher, H. Tong, S. Basu, L. Akoglu, D. Koutra, C. Faloutsos, and L. Li), Proceedings of the 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp. 1231-1239, 2012.

  • Measuring Tie Strength in Implicit Social Networks (with M. Gupte), Proceedings of the 4th ACM International Conference on Web Science (WebSci), pp. 109-118, 2012.

  • Gateway Finder in Large Graphs: Problem Definitions and Fast Solutions (with H. Tong, S. Papadimitriou, C. Faloutsos, and P.S. Yu), Information Retrieval 15(3-4), pp. 391-411, 2012.

  • Correcting Evaluation Bias of Relational Classifiers with Network Cross Validation (with J. Neville, B. Gallagher, and T. Wang), Knowledge and Information Systems 30(1), pp. 31-55, 2012.

  • Correcting Bias in Statistical Tests for Network Classifier Evaluation (with T. Wang, J. Neville, and B. Gallagher), Proceedings of the 2011 European Conference on Machine Learning & Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), pp. 506-521, 2011.

  • It's Who You Know: Graph Mining Using Recursive Structural Features (with K. Henderson, B. Gallagher, L. Li, L. Akoglu, H. Tong, and C. Faloutsos), Proceedings of the 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp. 663-671, 2011.

  • Ranking Information in Networks (with K. Henderson), Proceedings of the 2011 International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction (SBP), pp. 268-275, 2011.

  • Massively Parallel Acceleration of a Document-Similarity Classifier to Detect Web Attacks (with C. Ulmer, M. Gokhale, B. Gallagher, and P. Top), Journal of Parallel and Distributed Computing 71(2), pp. 225-235, 2011.

  • Detecting Novel Discrepancies in Communication Networks (with J. Abello and N. Devanur), Proceedings of the 10th IEEE International Conference on Data Mining (ICDM), pp. 8-17, 2010.

  • On the Vulnerability of Large Graphs: Measures and Fast Immunization Algorithms (with H. Tong, B.A. Prakash, C. Tsourakakis, C. Faloutsos, and D.H. Chau), Proceedings of the 10th IEEE International Conference on Data Mining (ICDM), pp. 1091-1096, 2010.

  • Profiling-by-Association: A Resilient Traffic Profiling Solution for the Internet Backbone (with M. Iliofotou, B. Gallagher, G. Xie, and M. Faloutsos), Proceedings of the 6th ACM International Conference on Emerging Networking Experiments and Technologies (CoNEXT), Article 2, pp. 1-12, 2010.

  • A Renewal Theory Approach to Anomaly Detection in Communication Networks (with B. Thompson), Notes of the 2nd Workshop on Information in Networks (WIN'10), New York, NY, September 2010.

  • MetricForensics: A Multi-Level Approach for Mining Volatile Graphs (with K. Henderson, C. Faloutsos, L. Akoglu, L. Li, K. Maruhashi, B.A. Prakash, and H. Tong), Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 163-172, 2010.

  • Basset: Scalable Gateway Finder in Large Graphs (with H. Tong, S. Papadimitriou, C. Faloutsos, and P. Yu), Proceedings of the 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pp. 449-463, 2010.

  • HCDF: A Hybrid Community Discovery Framework (with K. Henderson, S. Papadimitriou, and C. Faloutsos), Proceedings of the SIAM International Conference on Data Mining (SDM), pp. 754-765, 2010.

  • Literature Search through Mixed-Membership Community Discovery (with K. Henderson), Proceedings of the 2010 International Conference on Social Computing, Behavioral Modeling, and Prediction (SBP), pp. 70-78, 2010.

  • Homophily in Application Layer and its Usage in Traffic Classification (with B. Gallagher, M. Iliofotou, and M. Faloutsos), Proceedings of the 29th IEEE Conference on Computer Communications (INFOCOM), pp. 221-225, 2010. [Long version]

  • Leveraging Label-Independent Features for Classification in Sparsely Labeled Networks: An Empirical Study (with B. Gallagher), In Advances in Social Network Mining and Analysis, pp. 1-19, 2010, Springer.

  • Continuous Time Group Discovery in Dynamic Graphs (with K. Miller), NIPS 2009 Workshop on Analyzing Networks and Learning with Graphs, Whistler, BC, Canada, December 2009.

  • Evaluating Statistical Tests for Within-Network Classifiers of Relational Data (with J. Neville and B. Gallagher), Proceedings of the 9th IEEE International Conference on Data Mining (ICDM), pp. 397-406, 2009. [Best Paper Award Runner-up]

  • DAPA-V10: Discovery and Analysis of Patterns and Anomalies in Volatile Time-Evolving Networks (with B. Thompson), Notes of the 1st Workshop on Information in Networks (WIN), New York, NY, September 2009.

  • Classification of HTTP Attacks: A Study on the ECML/PKDD 2007 Discovery Challenge (with B. Gallagher), Technical Report LLNL-TR-414570, Lawrence Livermore National Laboratory, Livermore, CA, July 2009.

  • PaCK: Scalable Parameter-Free Clustering on K-Partite Graphs (with J. He, H. Tong, S. Papadimitriou, C. Faloutsos, J. Carbonell), Proceedings of the 2009 SIAM SDM Workshop on Link Analysis, Counterterrorism and Security, Reno, NV, May 2009.

  • Applying Latent Dirichlet Allocation to Group Discovery in Large Graphs (with K. Henderson), Proceedings of the 24th Annual ACM Symposium on Applied Computing (SAC), pp. 1456-1461, 2009.

  • Solving the Top-K Problem with Fixed-Memory Heuristic Search (with K. Henderson), Technical Report LLNL-TR-410187, Lawrence Livermore National Laboratory, Livermore, CA, January 2009. (Updated 2010 version)

  • GRAPHITE: A Visual Query System for Large Graphs (with D. H. Chau, C. Faloutsos, H. Tong, J. Hong, and B. Gallagher), Workshop Proceedings of the 8th IEEE International Conference on Data Mining (ICDM), pp, 963-966, 2008.

  • Fast Mining of Complex Time-Stamped Events (with H. Tong, Y. Sakurai, and C. Faloutsos), Proceedings of the 17th ACM Conference on Information and Knowledge Management (CIKM), pp. 759-768, 2008.

  • Two Heads Better than One: Pattern Discovery in Time-evolving Multi-Aspect Data (with J. Sun, C. Tsourakakis, E. Hoke, and C. Faloutsos), Data Mining and Knowledge Discovery, 17(1), pp. 111-128, 2008.

  • Collective Classification in Network Data (with P. Sen, G. Namata, M. Bilgic, L. Getoor, and B. Gallagher), AI Magazine 29(3), pp. 93-106, 2008.

  • Using Ghost Edges for Classification in Sparsely Labeled Networks (with B. Gallagher, H. Tong, and C. Faloutsos), Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 256-264, 2008.

  • Leveraging Label-Independent Features for Classification in Sparsely Labeled Networks: An Empirical Study (with B. Gallagher), Proceedings of the 2nd ACM SIGKDD Workshop on Social Network Mining and Analysis (SNAKDD), pp. 1-19, 2008.

  • Finding Mixed-Memberships in Social Networks (with P.S. Koutsourelakis), Papers of the 2008 AAAI Spring Symposium: Social Information Processing (AAAI-SS), pp. 48-53, 2008.

  • An Examination of Experimental Methodology for Classifiers of Relational Data (with B. Gallagher), Workshops Proceedings of the 7th IEEE International Conference on Data Mining (ICDM Workshops), pp. 411-416, 2007.

  • Fast Best-Effort Pattern Matching in Large Attributed Graphs (with H. Tong, B. Gallagher, and C. Faloutsos), Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 737-746, 2007.

  • A Position Paper: Value of Information for Evidence Detection. (with D.L. Roberts), Papers of the 2006 AAAI Fall Symposium: Capturing and Using Patterns for Evidence Detection (AAAI-FS), pp. 37-44, 2006.

  • Visual Analysis of Large Heterogeneous Social Networks by Semantic and Structural Abstraction (with Z. Shen and K.-L. Ma), IEEE Transactions on Visualization and Computer Graphics, Special Issue on Visual Analytics 12(6), pp. 1427-1439, 2006

  • Similarity in Computational Sciences (with T. Critchlow), Abstracts from the 2005 Learning Workshop (invited contribution), Snowbird, UT, April 5-8, 2005.

  • Knowledge Representation Issues in Semantic Graphs for Relationship Detection (with M. Barthelemy and E. Chow), Papers of the 2005 AAAI Spring Symposium: AI Technologies for Homeland Security (AAAI-SS), AAAI Press, Stanford, CA, 2005, pp. 91-98, 2005.

  • A Hybrid Approach to Multiresolution Modeling of Large-Scale Scientific Data (with T. Critchlow), Proceedings of the 20th Annual ACM Symposium on Applied Computing (SAC), pp. 511-518, 2005.

  • Statistical Modeling of Large-Scale Scientific Simulation Data (with C. Baldwin, G. Abdulla, and T. Critchlow), New Generation of Data Mining Applications, Eds: J. Zurada and M. Kantardzic, IEEE Press/Wiley Publishers, February 2005.

  • Using Ontological Information to Accelerate Path-Finding in Large Semantic Graphs: A Probabilistic Approach (with E. Chow), Technical Report UCRL-CONF-202002, Lawrence Livermore National Laboratory, Livermore, CA, 2005.

  • Multivariate Clustering of Large-Scale Scientific Simulation Data (with T. Critchlow), Technical Report UCRL-JC-151860-REV-1, Lawrence Livermore National Laboratory, Livermore, CA, 2003.

  • A System for Building Intelligent Agents that Learn to Retrieve and Extract Information (with J. Shavlik), User Modeling and User-Adapted Interaction 13(1-2), pp. 35-88, 2003.

  • The Evolution of a Hierarchical Partitioning Algorithm for Large-Scale Scientific Data: Three Steps of Increasing Complexity (with C. Baldwin, G. Abdulla, and T. Critchlow), Proceedings of the 15th International Conference on Scientific and Statistical Data Base Management (SSDBM), p. 225-228, 2003.

  • Intelligent Web Agents that Learn to Retrieve and Extract Information (with J. Shavlik), Intelligent Exploration of the Web, Eds: P.S. Szczepaniak, F. Segovia, J. Kacprzyk, and L.A. Zadeh, pp. 255-274, 2003, Springer-Verlag.

  • Statistical Modeling of Large-Scale Simulation Data (with T. Critchlow and G. Abdulla), Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 488-494, 2002.

  • Building Intelligent Agents that Learn to Retrieve and Extract Information. PhD Thesis. Computer Sciences Department. University of Wisconsin, Madison, WI, 2001.

  • A Theory-Refinement Approach to Information Extraction (with J. Shavlik), Proceedings of the 18th International Conference on Machine Learning (ICML), pp. 130-137, 2001.

  • An Instructable, Adaptive Interface for Discovering and Monitoring Information on the World Wide Web (with J. Shavlik, S. Calcari, and J. Solock), Proceedings of the 1999 International Conference on Intelligent User Interfaces (IUI), pp. 157-160, 1999.

  • Using a Trained Text Classifier to Extract Information (with J. Shavlik), Technical Report, July 1999.

  • Intelligent Agents for Web-Based Tasks: An Advice-Taking Approach (with J. Shavlik), Working Notes of the AAAI/ICML 1998 Workshop on Learning for Text Categorization, Madison, WI, 1998.

  • Building Intelligent Agents for Web-Based Tasks: A Theory-Refinement Approach (with J. Shavlik), Presented at the Conf on Automated Learning and Discovery Workshop on Learning from Text and the Web (CONALD), Pittsburgh, PA, 1998.

  • Visual Support for the ISLE Simulation Environment. Master's Thesis. Department of Computer Science. University of Illinois, Urbana, IL, 1995.