Publications, Slides, Posters

2024

• Black, M., Lin, L., Wong W-K. and Nayyeri, A. (2024). "Biharmonic Distance of Graphs and its Higher-Order Variants: Theoretical Properties with Applications to Centrality and Clustering". Proceedings of the 41st International Conference on Machine Learning, volume 235, pp. 4077-4102. (Github)
• Olson, M. L., Liu, S., Thiagarajan, J. J., Kustowski, B., Wong, W-K. and Anirudh, R. (2024). "Transformer-Powered Surrogates Close the ICF Simulation-Experiment Gap with Extremely Limited Data". Machine Learning: Science and Technology, Volume 5(2):025054, doi: 10.1088/2632-2153/ad4e03
• Chen, J., Wong, W-K. and Hamdaoui, B. (2024). "Unsupervised Contrastive Learning for Robust RF Device Fingerprint Under Time-Domain Shift". In Proceedings of the IEEE International Conference on Communications.
• Puppo, L., Wong, W-K., Hamdaoui, B., Elmaghbub, A. and Lin, L. (2024). On the Extraction of RF Fingerprints from LSTM Hidden-State Values for Robust Open-Set Detection. ITU Journal on Future and Evolving Technologies (ITU J-FET), Volume 5(1):134-146. https://doi.org/10.52953/MOGL1293. (pdf)

2023

• Gaskin, J., Elmaghbub, A., Hamdaoui, B. and Wong, W-K. (2023). "Deep Learning Model Portability for Domain-Agnostic Device Fingerprinting," in IEEE Access, vol. 11, pp. 86801-86823, doi: 10.1109/ACCESS.2023.3305257. (pdf)
• Puppo, L., Wong, W-K., Hamdaoui, B. and Elmaghbub, A. (2023). HiNoVa: A Novel Open-Set Detection Method for Automating RF Device Authentication. In the 2023 IEEE Symposium on Computers and Communications, pages 1122-1128, doi: 10.1109/ISCC58397.2023.10217841. (pdf)
• Elmaghbub, A., Hamdaoui, B. and Wong, W-K. (2023). ADL-ID: Adversarial Disentanglement Learning for Wireless Device Fingerprinting Temporal Domain Adaptation. In the IEEE International Conference on Communication 2023 - Mobile and Wireless Networks Symposium, pp. 6199-6204, doi: 10.1109/ICC45041.2023.10279347 (pdf, pdf (arXiv version))
• Olson, M. L., Liu, S., Anirudh, R., Thiagarajan, J. J., Bremer, P-T. and Wong, W-K. (2023). Cross-GAN Auditing: Unsupervised Identification of Attribute Level Similarities and Differences between Pretrained Generative Models. In the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2023, pp. 7981-7990. (pdf)

2022

• Gaskin, J., Hamdaoui, B., Wong, W-K. (2022). Tweak: Towards Portable Deep Learning Models for Domain-Agnostic LoRa Device Authentication. 2022 IEEE Conference on Communications and Network Security (CNS), pp. 1-9, doi: 10.1109/CNS56114.2022.10227829
• Luhrmann, F., Park, J., Wong, W-K., Corcoran, F., Lewis, C. (2022). Detecting Traveling Ionospheric Disturbances with LSTM Based Anomaly Detection, Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022), Denver, Colorado, September 2022, pp. 3002-3011. https://doi.org/10.33012/2022.18343
• Dinkins, T., Bhattacharyya, S., Chatterjee, S., Reis, S. and Wong, W-K. (2022). Towards Explainable Precision Changepoint Detection through Linear Decomposition. To appear in the Proceedings of the 2nd ANDEA (Anomaly and Novelty Detection, Explanation and Accommodation) Workshop, in conjunction with the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. (pdf)
• Chen, J., Wong, W-K., Hamdaoui, B., Elmaghbub, A., Sivanesan, K., Dorrance, R., and Yang, L. L. (2022). An Analysis of Complex-Valued CNNs for RF Data-Driven Wireless Device Classification. Proceedings of the IEEE International Conference on Communications 2022, pp. 4318-4323. doi: 10.1109/ICC45855.2022.9838694. (pdf).

2021

• Olson, M. L., Liu, S., Anirudh, R., Thiagarajan, J. J., Wong, W-K., Bremer, P-T. (2021). Unsupervised Attribute Alignment for Characterizing Distribution Shift. In Proceedings of the NeurIPS 2021 Workshop on Distribution Shifts. (pdf)
• Olson, M., Nguyen, T-V., Dixit, G., Razlaff, N. Wong, W-K. and Kahng, M. (2021). Contrastive Identification of Covariate Shift in Image Data. In Proceedings of the IEEE Visualization Conference (VIS'21) (short paper). (IEEE pdf, arXiv pdf)
• Olson, M., Khanna, R., Neal, L., Li, F. and Wong, W-K. (2021). Counterfactual State Explanations for Reinforcement Learning Agents via Generative Deep Learning. Artificial Intelligence, 295:103455, doi: 10.1016/j.artint.2021.103455 (pdf, Code)
• Gomez-Fernandez, M., Wong, W-K., Tokuhiro, A., Welter, K., Adlhawsawi, A. M., Yang, H., and Highley, K. (2021). Isotope Identification using Deep Learning: An Explanation. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Volume 998, 164925. (pdf)

2020

• Das, S., Wong, W-K., Dietterich, T., Fern, A., and Emmott, A. (2020). Discovering Anomalies by Incorporating Feedback from an Expert. ACM Transactions on Knowledge Discovery from Data, 14(4): 1-32. (pdf, Python code, R code (older))
• Bao, J., Hamdaoui, B. and Wong, W-K. (2020). IoT Device Type Identification Using Hybrid Deep Learning Approach for Increased IoT Secruity. Proceedings of 2020 International Wireless Communications and Mobile Computing (IWCMC), pp 565-570, doi: 10.1109/IWCMC48107.2020.9148110. (pdf)
• Moore, T. and Wong, W-K. (2020). The Quantile Snapshot Scan: Comparing Quantiles of Spatial Data from Two Snapshots in Time. Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, PMLR 108:2677-2686. (pdf, Supplementary Material, Code)
• Fernandez, M. G., Highley, K., Tokuhiro, A., Welter, K., Wong, W-K. and Yang, H. (2020). Status of Research and Development of Learning-based Approaches in Nuclear Science and Engineering: A Review. Nuclear Engineering and Design, 359(1), doi: 10.1016/j.nucengdes.2019.110479. (pdf)

2019

• Gomes, C., Dietterich, T., Barrett, C., Conrad, J., Dilkina, B., Ermon, S., Fang, F., Farnsworth, A., Fern, A., Fern, X., Fink, D., Fisher, D., Flecker, A., Freund, D., Fuller, A., Gregoire, J., Hopcroft, J., Kelling, S., Kolter, Z., Powell, W., Sintov, N., Selker, J., Selman, B., Sheldon, D., Shmoys, D., Tambe, M., Wong, W-K., Wood, C., Wu, X., Xue, Y., Yadav, A., Yakubu, A-A., Zeeman, M. (2019). Computational Sustainability: computing for a better world and a sustainable future. Communications of the ACM, 62(9): 56-65, New York, NY: ACM. (pdf)
• Olson, M., Neal, L., Li, F. and Wong, W-K. (2019). Counterfactual States for Atari Agents via Generative Deep Learning. Proceedings of the IJCAI 2019 Workshop on Explainable Artificial Intelligence, 7 pages. (pdf)
• Moore, T. and Wong, W-K. Finding Migration Paths in eBird Using the Quantile Snapshot Scan. Proceedings of the KDD 2019 Data Mining and AI for Conservation Workshop, 8 pages. (pdf)
• Siddiqui, M. A., Fern, A., Dietterich, T. G. and Wong, W-K. (2019). Sequential Feature Explanations. ACM Transactions on Knowledge Discovery from Data, 13(1): 1-22. doi: 10.1145/3230666. (pdf)

2018

• Pham, A. T., Raich, R., Fern, X. Z., Wong, W-K and Guan, X. (2018). Discriminative Probabilistic Framework for Generalized Multi-Instance Learning. Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing. doi: 10.1109/ICASSP.2018.8462099. (pdf)
• Neal, L., Olson, M., Fern, X., Wong, W-K. and Li, F. (2018). Open Set Learning with Counterfactual Images. Proceedings of European Conference on Computer Vision 2018, (pp. 620-635). (pdf)
• Moore, T. and Wong, W-K. (2018). An Efficient Quantile Spatial Scan Statistic for Finding Unusual Regions in Continuous Spatial Data with Covariates. In Proceedings of the Conference on Uncertainty in Artificial Intelligence 2018, (pp 756-765). (pdf, code)

2017

• Das, S., Wong, W-K., Fern, A., Dietterich, T. and Siddiqui, M. A. (2017). Incorporating Feedback into Tree-based Anomaly Detection. In Proceedings of the KDD 2017 Workshop on Interactive Data Exploration and Analytics. (slides)

2016

• Das, S., Wong, W-K., Dietterich, T., Fern, A. and Emmott, A. (2016). Incorporating Expert Feedback into Active Anomaly Discovery. In Proceedings of the 2016 IEEE 16th International Conference on Data Mining (ICDM), (pp. 853-858). (pdf)
• Guan, X., Raich, R. and Wong, W-K. (2016). Efficient Multi-Instance Learning for Activity Recognition from Time Series Data Using an Auto-Regressive Hidden Markov Model. Proceedings of the 33rd International Conference on Machine Learning, PMLR 48:2330-2339 (pdf, supplement, poster, slides)

2015

• Guan, X., Raich, R. and Wong, W-K. (2015). Multi-Instance Learning for Activity Recognition from Time Series Data Using a Mixture of Auto-Regressive Processes. In Proceedings of the the NIPS Time Series Workshop 2015.
• Siddiqui, M. A., Fern, A., Dietterich, T. G. and Wong, W-K. (2015). Sequential Feature Explanations for Anomaly Detection. In Proceedings of the ACM SIGKDD 2015 Workshop on ODDx3: Outlier Definition, Detection, and Description.
• Kelling, S., Johnston, A., Hochachka, W. M., Iliff, M., Fink, D., Gerbracht, J., Lagoze, C., La Sorte, F. A., Moore, T., Wiggins, A., Wong, W-K., Wood, C. and Yu, J. (2015). Can Observation Skills of Citizen Scientists Be Estimated Using Species Accumulation Curves? PLoS ONE, 10(10): e0139600. doi:10.1371/journal.pone.0139600. (link)
• Morton, T., Wong, W-K. and Megraw, M. (2015). TIPR: Transcription Initiation Pattern Recognition on a Genome Scale. Bioinformatics, 31(23): 3725-32. (link)
• Goins, A. K., Carpenter, R., Wong, W-K. and Balasubramanian, R. (2015). Implementation of a Gaussian process-based machine learning grasp predictor. Autonomous Robots, 40(4): 687-699. (link)
• Kulesza, T., Burnett, M., Wong, W-K. and Stumpf, S. (2015). Principles of Explanatory Debugging to Personalize Interactive Machine Learning. Proceedings of the 20th International Conference on Intelligent User Interfaces, (pp. 126-137), New York, NY: ACM. (pdf)
• Moore, T. and Wong, W-K. (2015). Discovering Hotspots and Coldspots of Species Richness in eBird Data. To appear at the AAAI-15 Workshop on Computational Sustainability.

2014

• Pei, Y., Christi, A., Fern, X., Groce, A. and Wong, W-K. (2014). Taming a Fuzzer Using Delta Debugging Trails. To appear at the 3rd International Workshop on Software Mining, ICDM 2014.
• Wiggins, A., Lagoze, C., Wong, W-K. and Kelling, S. (2014). A sensor network approach to managing data quality in citizen science. To appear at Citizen + X: Workshop on Volunteer-based Crowdsourcing in Science, Public Health and Government, Human Computation 2014.
• Unrath, M., Zhang, Z., Goins, A., Carpenter, R., Wong, W-K. and Balasubramanian, R. (2014). Using Crowdsourcing to Generate Surrogate Training Data for Robotic Grasp Prediction. In Proceedings of the Second AAAI Conference on Human Computation and Crowdsourcing (HCOMP-14), (pp. 60-61). (Extended Abstract)
• Goins, A. K., Carpenter, R., Wong, W-K. and Balasubramanian, R. (2014). Evaluating the Efficacy of Grasp Metrics for Utilization in a Gaussian Process-Based Grasp Predictor. Proceedings of the 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, (pp. 3353-3360), IEEE.
• Trost, S., Zheng, Y. and Wong, W-K. (2014). Machine learning for activity recognition: Hip versus wrist data. Physiological Measurement, 35(11):2183-2189.
• Illan, J. G., Thomas, C. D., Jones, J. A., Wong, W-K, Shirley, S. and Betts, M. G. (2014). Precipitation and winter temperature predict long-term range-scale abundance changes in Western North American birds. Global Change Biology, 20(11): 3351-3364. (Online version)
• Yu, J., Hutchinson, R. and Wong, W-K. (2014). A Latent Variable Model for Discovering Bird Species Commonly Misidentified by Citizen Scientists. In Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, (pp. 500-507), Palo Alto, CA: AAAI Press. (pdf)
• Yu, J., Wong, W-K. and Kelling, S. (2014). Clustering Species Accumulation Curves to Identify Skill Levels of Citizen Scientists Participating in the eBird Project. In Proceedings of the Twenty-Sixth Innovative Applications of Artificial Intelligence Conference, (pp. 3017-3023), Palo Alto, CA: AAAI Press. (pdf)
• Yu, J., Mohan, S., Putthividhya, D. and Wong, W-K. (2014). Latent Dirichlet Allocation based Diversified Retrieval for E-commerce Search. In Proceedings of the 7th ACM Web Search and Data Mining conference, (pp. 463-472), New York, NY: ACM. (pdf)
• Groce, A., Kulesza, T., Zhang, C., Shamasunder, S., Burnett, M., Wong, W-K., Stumpf, S., Das, S., Shinsel, A., Bice, F., and McIntosh, K. (2014). You are the only possible oracle: Effective test selection for end users of interactive machine learning systems. IEEE Transactions on Software Engineering, 40(3): 307-323.
• Sullivan, B. L., Aycrigg, J. L, Barry, J. H., Bonney, R. E., Bruns, N., Cooper, C. B., Damoulas, T., Dhondt, A. A., Dietterich, T., Farnsworth, A., Fink, D., Fitzpatrick, J. W., Fredericks, T., Gerbracht, J., Gomes, C., Hochachka, W. M., Iliff, M. J., Lagoze, C., La Sorte, F. A., Merrifield, M., Morris, W., Phillips, T. B., Reynolds, M., Rodewald, A. D., Rosenberg, K. V., Trautmann, N. M., Wiggins, A., Winkler, D. W., Wong, W-K., Wood, C. L., Yu, J. and Kelling, S. (2014). The eBird enterprise: An integrated approach to development and application of citizen science. Biological Conservation, 169: 31-40. (pdf)

2013

• Wong, W-K., Anderson, M. and Trost, S. (2013). Real-Time Segmentation and Classification of Free-Living Accelerometer Data Using Changepoint Detection and Machine Learning [abstract]. The 3rd International Conference on Ambulatory Monitoring of Physical Activity and Movement, Amherst, MA.
• Trost, S., Zheng, Y. and Wong, W-K. (2013). Machine Learning for Activity Recognition: Hip versus Wrist Data [abstract]. The 3rd International Conference on Ambulatory Monitoring of Physical Activity and Movement, Amherst, MA.
• Yu, J., Hutchinson, R. A. and Wong, W-K. (2013). Modeling Misidentifcation of Bird Species by Citizen Scientists. In Proceedings of the NIPS 2013 Machine Learning for Sustainability Workshop.
• Yu, J., Wong, W-K. and Kelling, S. (2013). Clustering Species Accumulation Curves to Identify Groups of Citizen Scientists with Similar Skill Levels. In Proceedings of the NIPS 2013 Machine Learning for Sustainability Workshop.
• Das, S., Moore, T., Wong, W-K., Stumpf, S., Oberst, I., McIntosh, K. and Burnett, M. (2013). End-user feature labeling: Supervised and semi-supervised approaches based on locally-weighted logistic regression. Artificial Intelligence, 204:56-74. (Online copy)
• Emmott, A. F., Das, S., Dietterich, T., Fern, A. and Wong, W-K. (2013). Systematic Construction of Anomaly Detection Benchmarks from Real Data. In Proceedings of the KDD 2013 Workshop on Outlier Detection and Description.
• Senator, T. E., Goldberg, H. G., Memory, A., Young, W. T., Rees, B., Pierce, R., Huang, D., Reardon, M., Bader, D. A., Chow, E., Essa, I., Jones, J., Bettadapura, V., Chau, D. H., Zakrzewska, A., Briscoe, E., Mappus IV, R. L, McColl, R., Weiss, L., Dietterich, T. G., Fern, A., Wong, W-K., Das, S., Emmott, A., Irvine, J., J-Y. Lee, D. Koutra, C. Faloutsos, Corkill, D., Friedland, L., Gentzel, A. and Jensen, D. (2013). Detecting Insider Threats in a Real Corporate Database of Computer Usage Activity. Proceedings of the 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, (pp. 1393-1401), New York, NY: ACM Press. (pdf)
• Kulesza, T., Stumpf, S., Burnett, M., Yang, S., Kwan, I. and Wong, W-K. (2013). Too Much, Too Little, or Just Right? Ways Explanations Impact End Users' Mental Models. Proceedings of the IEEE Conference on Visual Languages and Human-Centric Computing (VL/HCC), (pp. 3-10), Piscataway, NJ: IEEE Computer Society. (pdf)
• Zheng, Y., Wong, W-K., Guan, X. and Trost, S. (2013). Physical Activity Recognition from Accelerometer Data Using a Multi-Scale Ensemble Method. Proceedings of the Twenty-Fifth Annual Conference on Innovative Applications of Artificial Intelligence, (pp. 1575-1580), Palo Alto, CA: AAAI Press. (pdf, slides, data)
• Chen, Y., Groce, A., Zhang, C., Wong, W-K., Fern, X., Eide, E. and Regehr, J. (2013). Taming Compiler Fuzzers. In Proceedings of the 34th ACM SIGPLAN conference on Programming language design and implementation, (pp. 197-208), New York, NY: ACM. (pdf)
• Kelling, S., Gerbracht, J., Fink, D., Lagoze, C., Wong, W-K., Yu, J., Damoulas, T., Gomes, C. P. (2013). A Human/Computer Learning Network to Improve Biodiversity Conservation and Research. AI Magazine, 34(1):10-20.

2012

• Trost, S.G., Zheng, Y., Wong, W-K. (2012). Raw tri-axial acceleration data improves the recognition of physical activity type in children and adolescents [abstract]. Journal of Science and Medicine in Sport, 15, S92.
• Wiggins, A., Gerbracht, J., Lagoze, C., Yu, J., Wong, W-K. and Kelling, S. (2012). Crowdsourcing citizen science data quality with a human-computer learning network. In Proceedings of the Human Computation for Science and Computational Sustainability Workshop.
• Stumpf, S., Burnett, M. M., Pipek, V., Wong, W-K. (2012). End-user interactions with intelligent and autonomous systems. CHI '12 Extended Abstracts on Human Factors in Computing Systems, (pp. 2755-2758).
• Zhang, X., Shrestha, B., Yoon, S.W., Kambhampati, S., DiBona, P., Guo, J., McFarlane, D., Hofmann, M. O., Whitebread, K. R., Appling, D. S., Whitaker, E. T., Trewhitt, E., Ding, L., Michaelis, J., McGuinness, D. L., Hendler, J. A., Doppa, J. R., Parker, C., Dietterich, T. G., Tadepalli, P., Wong, W-K., Green, D. T., Rebguns, A., Spears, D. F., Kuter, U., Levine, G., DeJong, G., MacTavish, R., Ontanon, S., Radhakrishnan, J., Ram, A., Mostafa, H., Zafar, H., Zhang, C., Corkill, D. D., Lesser, V. R., Song, Z. (2012). An Ensemble Architecture for Learning Complex Problem-Solving Techniques from Demonstration. ACM TIST 3(4): 75:1-75:38.
• Yu, J., Kelling, S., Gerbracht, J., Wong, W-K. (2012). Automated Data Verification in a Large-scale Citizen Science Project: a Case Study. In the Proceedings of the 8th IEEE International Conference on E-Science, (pp. 42-49). (pdf)
• Stumpf, S., Wong, W-K., Burnett, M., and Kulesza, T. (2012). Making intelligent systems understandable and controllable by end users. In Proceedings of the Second Workshop on Intelligibility and Control in Pervasive Computing. (pdf)
• Kelling, S., Gerbact, J., Fink, D., Lagoze, C., Wong, W-K., Yu, J., Damoulas, T., and Gomes, C. (2012). eBird: A Human/Computer Learning Network for Biodiversity Conservation and Research. Proceedings of the Twenty-Fourth Conference on Innovative Applications of Artificial Intelligence, (pp. 2229-2236). (pdf)
• Trost, S., Wong, W-K., Pfeiffer, K. A., Zheng, Y. (2012). Artificial Neural Networks to Predict Activity Type and Energy Expenditure in Youth. (2012). Medicine and Science in Sports and Exercise, 44(9), 1801-1809.
• Curran, W., Moore, T., Kulesza, T., Wong, W-K., Todorovic, S., Stumpf, S., White, R., and Burnett, M. (2012). Towards Recognizing "Cool": Can End Users Help Computer Vision Recognize Subjective Attributes of Objects in Images? Proceedings of the 2012 International Conference on Intelligent User Interfaces, (pp. 285-288), New York, NY: ACM Press. (pdf)
• Hollingsworth, S., Lewis, M. C., Berkholz, D. S., Wong, W-K., and Karplus, A. (2012) (< phi >, < psi >) 2-motifs: a purely conformation-based, fine-grained enumeration of protein parts at the two-residue level. (2012). Journal of Molecular Biology, 416: 78-93. (pdf)

2011

• Hochachka, W. M., Fink, D., Hutchinson, R. A., Sheldon, D., Wong, W-K., and Kelling, S. (2011). Data-intensive science applied to broad-scale citizen science. Trends in Ecology and Evolution, 27(2): 130-137. (pdf)
• Kelling, S., Yu, J, Gerbracht, J. and Wong, W-K. (2011). The Implementation of Automated Data Verification Proceses in a Large-scale Citizen Science Project. Proceedings of the IEEE eScience 2011 Computing for Citizen Science Workshop, (pp 20-27).
• Shinsel, A., Kulesza, T., Burnett, M., Curran, W., Groce, A., Stumpf, S., and Wong, W-K. (2011). Mini-Crowdsourcing End-User Assessment of Intelligent Assistants: A Cost-Benefit Study. In the Proceedings of the IEEE Symposium on Visual Languages and Human-Centric Computing 2011, (pp. 47-54). (pdf)
• Yu, J., Wong, W-K., Dietterich, T., Jones, J., Betts, M., Frey, S., Shirley, S., Miller, J., and White, M. (2011). Multi-label Classification for Species Distribution Modeling. In Proceedings of the ICML 2011 Workshop on Machine Learning for Global Challenges. (pdf)
• Wong, W-K., Oberst, I., Das, S., Moore, T., Stumpf, S., McIntosh, K., and Burnett, M. (2011). End-User Feature Labeling via Locally Weighted Logistic Regression. Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, (pp. 1575-1578). [Shorter version of IUI 2011 paper] (pdf)
• Kulesza, T., Stumpf, S., Wong, W-K., Burnett, M., Perona, S., Ko, A., and Oberst, I. (2011). Why-Oriented End-User Debugging of Naive Bayes Text Classification, ACM Transactions on Interactive Intelligent Systems, 1(1): 1-31. (pdf)
• Kulesza, T., Burnett, M., Stumpf, S., Wong, W-K., Das, S. Groce, A., Shinsel, A., Bice, F., and McIntosh, K. (2011). Where Are My Intelligent Assistant's Mistakes? A Systematic Testing Approach, Third International Symposium on End-User Development (Lecture Notes in Computer Science 6654), (pp. 171-186). (pdf)
• Wong, W-K., Oberst, I., Das, S., Moore, T., Stumpf, S., McIntosh, K., and Burnett, M. (2011). End-User Feature Labeling: A Locally-Weighted Regression Approach. ACM International Conference on Intelligent User Interfaces, (pp. 115-124), New York, NY: ACM Press. Best Paper Nomination at IUI 2011. (pdf)

2010

• Bryant, D. W., Fox, S. E., Rowley, E. R., Priest, H. D., Shen., R., Wong W.-K., and Mockler, T. C. (2010). Discovery of SNP Markers in Expressed Genes of Hazelnut. ISHS Acta Horticulturae, 859: 289-294. (pdf)
• Yu, J., Wong, W-K., and Hutchinson, R. (2010). Modeling Experts and Novices in Citizen Science Data for Species Distribution Modeling. Proceedings of the 2010 IEEE International Conference on Data Mining, (pp. 1157-1162), Los Alamitos, CA: IEEE Computer Society. (pdf)
  

  The extended technical report version can be found here:
  Yu, J., Wong, W-K., and Hutchinson, R. (2010). Modeling Experts and Novices in Citizen Science Data for Species Distribution Modeling. Technical Report, Oregon State University. (pdf).

• Kulesza, T., Stumpf, S., Burnett, M., Wong, W-K., Riche, Y., Moore, T., Oberst, I., Shinsel, A., and McIntosh, K. Explanatory Debugging: Supporting End-User Debugging of Machine-Learned Programs. (2010). In Proceedings of the IEEE Symposium on Visual Languages and Human-Centric Computing 2010, (pp. 41-48). (pdf)
• Bryant Jr., D. W., Shen, R., Priest, H. D., Wong W-K., Mockler, T. C. (2010). Supersplat -- Spliced RNA-seq Alignment. Bioinformatics, 26(12): 1500-1505. (Preprint)
• Margineantu, D., Wong W-K., and Dash, D. (2010). Machine learning algorithms for event detection. Machine Learning 79(3):257-259.
• Filichkin, S., Priest, H. D., Given, S. A., Shen, Rongkun, Bryant, D. W., Fox, S. E., Wong, W-K., and Mockler, T. C. (2010). Genome-wide mapping of alternative splicing in Arabidopsis thaliana. Genome Research, 20(1): 45-58. (pdf)

2009

• Vatturi, P., and Wong, W-K. (2009). Category Detection using Hierarchical Mean Shift. In Proceedings of the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, (pp. 847-856), New York, NY: ACM Press. (pdf, ppt)
• Stumpf, S., Rajaram, V., Li, L., Wong, W-K., Burnett, M., Dietterich, T., Sullivan, E., and Herlocker, J. (2009). Interacting Meaningfully with Machine Learning Systems: Three Experiments. International Journal of Human-Computer Studies, 67(8): 639-662. (preprint, IJHCS website)
• Zhang, X., Yoon, S., DiBona, P., Appling, D. S., Ding, L., Doppa, J. R., Green, D., Guo, J. K., Kuter, U., Levine, G., MacTavish, R. L., McFarlane, D., Michaelis, J. R., Mostafa, H., Ontan, S., Parker, C., Radhakrishnan, J., Rebguns, A., Song, Z., Trewhitt, E. B., Zafar, H., Zhang, C., Corkill, D., DeJong, G., Dietterich, T., Kambhampati, S., Lesser, V., McGuinness, D. L., Ram, A., Spears, D., Tadepalli, P., Whitaker, E. T., Wong, W-K., Hendler, J. A., Hofmann, M., Whitebread, K. (2009). An Ensemble Learning and Problem Solving Architecture for Airspace Management. In Proceedings of the Twenty-First Innovative Applications of Artificial Intelligence Conference, (pp. 203-210). (pdf)
• Burrows, E. H., Wong, W-K., Fern, X., Chaplen, F. W. R., Ely, R. L. (2009). Optimization of pH and Nitrogen for Enhanced Hydrogen Production by Synechocystis sp. PCC 6803 via Statistical and Machine Learning Methods, Biotechnology Progress, 25(4): 1009-1017.
• Bryant, D. W. Jr, Wong, W-K., Mockler, T. C. (2009). QSRA - a quality-value guided de novo short read assembler. BMC Bioinformatics, 10:69, doi:10.1186/1471-2105-10-69. (pdf)
• Kulesza, T., Wong, W-K., Stumpf, S., Perona, S., White, R., Burnett., M, Oberst, I., and Ko., A. J. (2009). Fixing the Program My Computer Learned: Barriers for End Users, Barriers for the Machine. In Proceedings of the 2009 International Conference on Intelligent User Interfaces, (pp. 187-196), New York, NY: ACM Press. (pdf)

2008

• Natarajan, S., Bui, H. H., Tadepalli, P., Kersting, K., and Wong, W.-K. (2008). Logical Hierarchical Hidden Markov Models for Modeling User Activities. In Proceedings of the Eighteenth International Conference on Inductive Logic Programming, (pp. 192-209), Springer. (pdf)
• Shen, J., Li, L., and Wong, W.-K. (2008). Markov Blanket Feature Selection for Support Vector Machines. In Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, (pp. 696-701), Menlo Park, CA: AAAI Press. (pdf)
• Stumpf, S., Sullivan, E., Fitzhenry, E., Oberst, I., Wong, W.-K., Burnett, M. (2008). Integrating Rich User Feedback into Intelligent User Interfaces. In Proceedings of the 2008 International Conference on Intelligent User Interfaces, (pp. 50-59), New York, NY: ACM Press. (pdf) [Note: The camera-ready version in the official IUI 2008 proceedings was missing Table 2. This web version includes Table 2]
• Hillier, M., Wong, W.-K., Sheehy, S., and Jones, J. A. (2008). Google Earth as a visualization tool for understanding changing patterns of exotic plants along forest roads in western Oregon. In Proceedings of the Annual Meeting of the Association of American Geographers.

2007

• Shen, Y., Wong, W.-K., Levander, J., and Cooper, G. F. (2007). An outbreak detection algorithm that efficiently performs complete Bayesian model averaging over all possible spatial distributions of disease. Advances in Disease Surveillance; 4:113.
• Mills-Price, C., Wong, W.-K., Tadepalli, P., Dereszynski, E. (2007). Bi-level Optimization for Learning Cost Functions from Demonstration. Proceedings of the 2007 AAAI Workshop on Acquiring Planning Knowledge via Demonstration, 18-20. (pdf)
• Parker, C., Tadepalli, P., Wong W-K., Dietterich, T., Fern, A. Learning from Demonstrations via Structured Prediction. Proceedings of the 2007 AAAI Workshop on Acquiring Planning Knowledge via Demonstration, 34-40.
• Kaufman, Z., Wong W-K., Peled-Leviatan, T., Cohen, E., Lavy, C., Aharonowitz, G. Dichtiar, R., Bromberg, M., Havkin, O., Kokia, E., Green, M. (2007). Evaluation of a Syndromic Surveillance System Using the WSARE Algorithm for Early Detection of an Unusual, Localized Summer Outbreak of Influenza B: Implications for Bioterrorism Surveillance. Israel Medical Association Journal, 9:3-7.
• Rolka, H., Burkom, H., Cooper, G. F., Kulldorff, M., Madigan, D., and Wong, W-K. (2007). Issues in applied statistics for public health bioterrorism surveillance using multiple data streams: research needs. Statistics in Medicine, 26(8): 1834-1856. (pdf)

2006

• Natarajan, S., Wong, W.-K., and Tadepalli, P. (2006). Structure Refinement in First Order Conditional Influence Language. Workshop on Open Problems in Statistical Relational Learning, ICML 2006. (pdf)
• Cooper, G., Dash, D., Levander, J., Wong, W.-K., Hogan, W., and Wagner, M. (2006). Bayesian Biosurveillance. In M. M. Wagner, A. W. Moore and R. A. Aryel (Eds.), Handbook of Biosurveillance. New York City, NY: Academic Press.
• Wong, W.-K. and Moore, A. (2006) Outbreak Detection with Time Series Data. In M. M. Wagner, A. W. Moore and R. A. Aryel (Eds.), Handbook of Biosurveillance. New York City, NY: Academic Press.

2005

• Sabhnani, M. R., Neill, D. B., Moore, A. W., Dubrawski, A., and Wong, W.-K. (2005). Efficient analytics for effective monitoring of biomedical security. Proceedings of the International Conference on Information and Automation, (pp. 87-92).(pdf)
• Wong, W.-K., Moore, A., Cooper, G. and Wagner, M. (2005). What's Strange About Recent Events (WSARE): An Algorithm for the Early Detection of Disease Outbreaks. Journal of Machine Learning Research, 6: 1961-1998. (pdf)
• Adamou, C., Cooper, G., Wong, W.-K., Dowling, J., and Hogan, W. (2005). Modeling clinician detection time of a disease outbreak due to inhalational anthrax. Proceedings of the National Conference on Syndromic Surveillance. (abstract) (poster)
  
• Garman, C., Wong, W.-K., and Cooper, G. (2005). The effect of inferring work location from home location in performing bayesian biosurveillance. Proceedings of the National Conference on Syndromic Surveillance. (abstract) (poster)
  
• Shen, Y., Wong, W.-K., and Cooper, G. (2005). A generalization of the standard AMOC curve. Proceedings of the National Conference on Syndromic Surveillance. (abstract) (slides)
  
• Wong, W.-K., and Moore, A. (2005). Bayesian network approaches to detection. In A. B. Lawson and K. Kleinman (Eds.), Spatial and syndromic surveillance for public health, 169-187. New York: John Wiley and Sons, Ltd.
  
• Wong, W.-K., Cooper, G., Dash, D., Levander, J., Dowling, J., Hogan, W., and Wagner, M. (2005). Population-wide Anomaly Detection. KDD 2005 Workshop on Data Mining Methods for Anomaly Detection(pp. 79-83). (pdf) (slides)
  
• Wong, W.-K., Cooper, G., Dash, D., Levander, J., Dowling, J., Hogan, W., and Wagner, M. (2005). Use of multiple data streams to conduct Bayesian biologic surveillance. In: Syndromic Surveillance: Reports from a National Conference, 2004. MMWR 2005; 54 (Suppl): 63-69. (pdf) (slides)
  

2004

• Cooper, G., Dash, D., Levander, J., Wong W.-K., Hogan, W., and Wagner, M. (2004). Bayesian biosurveillance of disease outbreaks. Proceedings of the Twentieth Conference of Uncertainty in Artificial Intelligence (UAI-2004) (pp. 94-103). Arlington, VA: AUAI Press. (pdf) (slides)
  
• Wong, W.-K. (2004) Data mining for early disease outbreak detection. Doctoral dissertation, Carnegie Mellon University, Pittsburgh. (pdf) (slides)
  

2003

• Wong, W.-K., Moore, A., Cooper, G., and Wagner, M. (2003) Bayesian network anomaly pattern detection for disease outbreaks. Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003) (pp. 808-815). Menlo Park, California: AAAI Press. (pdf) (slides) (poster)
  
• Moore, A., and Wong, W.-K. (2003). Optimal Reinsertion: A new search operator for accelerated and more accurate Bayesian network structure learning. Proceedings of the Twentieth International Conference on Machine Learning (ICML 2003) (pp. 552-559). Menlo Park, CA: AAAI Press. (pdf)
  
• Wong, W.-K., Moore, A., Cooper, G., and Wagner, M. (2003). What's strange about recent events. Journal of Urban Health, 80: i66-i75. (pdf)
  

2002

• Tsui, F. C., Espino, J. U., Wagner, M. M., Gesteland, P., Ivanov, O., Olszewski, R. T., Liu, Z., Zeng, X., Chapman, W., Wong, W-K., Moore, A. (2002). Data, network, and application: technical description of the Utah RODS Winter Olympic Biosurveillance System. Proc AMIA Symp, 815-819.
• Wong, W.-K., Moore A.., Cooper, G., and Wagner, M. (2002). Rule-based anomaly pattern detection for detecting disease outbreaks. Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-02) (pp. 217-223). AAAI Press. (pdf)
  
• Wong, W.-K., and Moore, A. (2002). Efficient algorithms for non-parametric clustering with clutter. Computer Science and Statistics (pp.541-553). Fairfax Station, VA: Interface Foundation of North America, Inc. (pdf) (slides)
  

1999

• Schneider, J., Wong, W.-K., Moore, A., and Riedmiller, M. (1999) Distributed value functions. Proceedings of the Sixteenth International Conference on Machine Learning (ICML-99) (pp.371-378). San Francisco, CA: Morgan Kaufmann. (pdf)
  

Slides from Talks

• Modeling Experts and Novices in Citizen Science Data (2009). Given at the Computational Sustainability 2010 conference (slides)
• Category Detection using Hierarchical Mean Shift (2009). Given at Google Pittsburgh (slides)
• End-user Debugging of Machine Learning Systems (2008). Given at Microsoft Research (slides)
• Bayesian Networks tutorial (2006). Given at the National Syndromic Surveillance Conference, Baltimore, MD. (slides)
• What's Strange About Recent Events (WSARE). (2004). Given at the Anomaly Detection Workshop at Stanford. (slides)
• What's Strange About Recent Events (WSARE) v3.0: Adjusting for a Changing Baseline. (2003). Given at the National Syndromic Surveillance Conference. (slides)
• What's Strange About Recent Events (WSARE). (2003). Given at a DIMACS workshop. (slides)
• Efficient algorithms for non-parametric clustering with clutter. (2002). Given at the CMU Machine Learning Lunch. (slides)

Posters

• Data Mining for the Early Detection of Disease Outbreaks. Presented at the Kelley Engineering Center Open House. ppt

Data

Here are some publicly available datasets that have been used in my papers:

• Accelerometer dataset used for activity recognition (LVAY.tar.gz)

Software