Publications

2018

Daniel B. Neill. Bayesian scan statistics. In J. Glaz and M. V. Koutras, eds., Handbook of Scan Statistics, 2018, in press.

2017

Daniel B. Neill and William Herlands. Machine learning for drug overdose surveillance. Proc. Bloomberg
Data for Good Exchange Conference
, 2017. (pdf)

Daniel B. Neill. Subset scanning for event and pattern detection. In S. Shekhar and H. Xiong, eds., Encyclopedia of GIS, 2nd ed., Springer, 2017, pp. 2218-2228. (pdf)

Sriram Somanchi and Daniel B. Neill. Graph structure learning from unlabeled data for early outbreak detection. IEEE Intelligent Systems 32(2): 80-84, 2017. (pdf) (extended version on arXiv)

Zhe Zhang and Daniel B. Neill. Identifying significant predictive bias in classifiers. Presented at NIPS Workshop on Interpretable Machine Learning for Complex Systems, 2016, and 4th Workshop on Fairness, Accountability, and Transparency in Machine Learning, 2017. (Interpret ML version) (FAT ML version)

Daniel B. Neill. Multidimensional tensor scan for drug overdose surveillance. Online Journal of Public Health Informatics 9(1): e20, 2017. (pdf)

Dylan Fitzpatrick, Yun Ni, and Daniel B. Neill. Support vector subset scan for spatial outbreak detection. Online Journal of Public Health Informatics 9(1): e21, 2017. (pdf)

2016

Skyler Speakman, Sriram Somanchi, Edward McFowland III, and Daniel B. Neill. Penalized fast subset scanning. Journal of Computational and Graphical Statistics, 25(2): 382-404, 2016. Selected for “Best of JCGS” invited session by the journal’s editor in chief. (pdf).

Brad J. Bushman, Katherine Newman, Sandra L. Calvert, Geraldine Downey, Mark Dredze, Michael Gottfredson, Nina G. Jablonski, Ann S. Masten, Calvin Morrill, Daniel B. Neill, Daniel Romer, and Daniel W. Webster. Youth violence: what we know and what we need to know. American Psychologist 71(1): 17-39, 2016. (pdf) (APA press release)

William Herlands, Andrew Gordon Wilson, Hannes Nickisch, Seth Flaxman, Daniel B. Neill, Willem van Panhuis, and Eric P. Xing. Scalable Gaussian processes for characterizing multidimensional change surfaces. Proc. 19th International Conference on Artificial Intelligence and Statistics, JMLR: W&CP 51: 1013-1021, 2016. (pdf)

Abhinav Maurya, Kenton Murray, Yandong Liu, Chris Dyer, William Cohen, and Daniel B. Neill. Semantic scan: detecting subtle, spatially localized events in text streams. Technical report, Carnegie Mellon University, 2016. Winner of the Yelp Dataset Challenge. (working paper on arXiv)

2015

Seth R. Flaxman, Daniel B. Neill, and Alexander J. Smola. Gaussian processes for independence tests with non-iid data in causal inference. ACM Transactions on Intelligent Systems and Technology, 7(2): 22:1-22:23, 2015. (pdf)

Skyler Speakman, Edward McFowland III, and Daniel B. Neill. Scalable detection of anomalous patterns with connectivity constraints. Journal of Computational and Graphical Statistics 24(4): 1014-1033, 2015. (pdf)

Feng Chen and Daniel B. Neill. Human rights event detection from heterogeneous social media graphs. Big Data 3(1): 34-40, 2015. (pdf)

Seth R. Flaxman, Andrew Gordon Wilson, Daniel B. Neill, Hannes Nickisch, and Alexander J. Smola. Fast Kronecker inference in Gaussian processes with non-Gaussian likelihoods. Proc. 32nd International Conference on Machine Learning, JMLR: W&CP 37, 2015. (pdf)

Daniel Gartner, Rainer Kolisch, Daniel B. Neill, and Rema Padman. Machine learning approaches for early DRG classification and resource allocation. INFORMS Journal of Computing 27(4): 718-734, 2015. (pdf) (supplementary material)

William Herlands, Maria de Arteaga, Daniel B. Neill, and Artur Dubrawski. Lass-0: Sparse non-convex regression by local search. Proc. 8th NIPS Workshop on Optimization for Machine Learning, 2015. (pdf)

Sriram Somanchi, David Choi, and Daniel B. Neill. StarScan: a novel scan statistic for irregularly-shaped spatial clusters. Online Journal of Public Health Informatics 7(1): e55, 2015. (pdf)

Mallory Nobles, Lana Deyneka, Amy Ising, and Daniel B. Neill. Identifying emerging novel outbreaks in textual emergency department data. Online Journal of Public Health Informatics 7(1): e45, 2015. (pdf)

Zachary Faigen, Lana Deyneka, Amy Ising, Daniel B. Neill, Mike Conway, Geoffrey Fairchild, Julia Gunn, David Swenson, Ian Painter, Lauren Johnson, Chris Kiley, Laura Streichert, and Howard Burkom. Cross-disciplinary consultancy to bridge public health technical needs and analytic developers: asyndromic surveillance use case. Online Journal of Public Health Informatics, 7(3):e228, 2015. (pdf)

Edward McFowland III. Efficient Methods for Anomalous Pattern Detection and Discovery. Ph.D. thesis, H.J. Heinz III College, Carnegie Mellon University, 2015. (link)

Seth R. Flaxman. Machine Learning in Space and Time: Spatiotemporal Learning and Inference with Gaussian Processes and Kernel Methods. Ph.D. thesis, H.J. Heinz III College, Carnegie Mellon University, 2015. (link)

Sriram Somanchi. Detecting Anomalous Patterns in Health Care Data. Ph.D. thesis, H.J. Heinz III College, Carnegie Mellon University, 2015. (link)

2014

Feng Chen and Daniel B. Neill. Non-parametric scan statistics for disease outbreak detection on Twitter. Online Journal of Public Health Informatics 6(1): e155, 2014. (pdf)

Skyler Speakman, Sriram Somanchi, Edward McFowland III, and Daniel B. Neill. Disease surveillance, case study. In R. Alhajj and J. Rokne, eds., Encyclopedia of Social Network Analysis and Mining, pp. 380-385. Springer, 2014. (pdf)

Feng Chen and Daniel B. Neill. Non-parametric scan statistics for event detection and forecasting in heterogeneous social media graphs. Proceedings of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 1166-1175, 2014. (pdf)

Skyler Speakman. Fast Constrained Subset Scanning for Pattern Detection. Ph.D. thesis, H.J. Heinz III College, Carnegie Mellon University, 2014. (link)

2013

Edward McFowland III, Skyler Speakman, and Daniel B. Neill. Fast generalized subset scan for anomalous pattern detection. Journal of Machine Learning Research, 14: 1533-1561, 2013. (pdf)

Skyler Speakman, Yating Zhang, and Daniel B. Neill. Dynamic pattern detection with temporal consistency and connectivity constraints. Proc. 13th IEEE International Conference on Data Mining, 697-706, 2013. (pdf)

Sriram Somanchi and Daniel B. Neill. Discovering anomalous patterns in large digital pathology images. Proc. 8th INFORMS Workshop on Data Mining and Health Informatics, 2013. (pdf)

Daniel B. Neill, Edward McFowland III, and Huanian Zheng. Fast subset scan for multivariate event detection. Statistics in Medicine 32: 2185-2208, 2013. (pdf)

Daniel B. Neill. Using artificial intelligence to improve hospital inpatient care. IEEE Intelligent Systems 28(2): 92-95, 2013. (pdf)

Skyler Speakman, Yating Zhang, and Daniel B. Neill. Tracking dynamic water-borne outbreaks with temporal consistency constraints. Online Journal of Public Health Informatics 5(1), 2013. (pdf)

Daniel B. Neill and Tarun Kumar. Fast multidimensional subset scan for outbreak detection and characterization. Online Journal of Public Health Informatics 5(1), 2013. (pdf)

2012

Daniel B. Neill. Fast subset scan for spatial pattern detection. Journal of the Royal Statistical Society (Series B: Statistical Methodology) 74(2): 337-360, 2012. (pdf)

Daniel B. Neill. New directions in artificial intelligence for public health surveillance. IEEE Intelligent Systems 27(1): 56-59, 2012. (pdf)

Christopher A. Harle, Daniel B. Neill, and Rema Padman. Information visualization for chronic disease risk assessment. IEEE Intelligent Systems 27(6): 81-85, 2012. (pdf)

2011

Kan Shao, Yandong Liu, and Daniel B. Neill. A generalized fast subset sums framework for Bayesian event detection. Proceedings of the 11th IEEE International Conference on Data Mining, 617-625, 2011. (pdf)

Daniel B. Neill. Fast Bayesian scan statistics for multivariate event detection and visualization. Statistics in Medicine 30(5): 455-469, 2011. (pdf)

Sharique Hasan, George T. Duncan, Daniel B. Neill, and Rema Padman. Automatic detection of omissions in medication lists. Journal of the American Medical Informatics Association 18(4): 449-458, 2011. (pdf)

Daniel Oliveira, Daniel B. Neill, James H. Garrett Jr., and Lucio Soibelman. Detection of patterns in water distribution pipe breakage using spatial scan statistics for point events in a physical network. Journal of Computing in Civil Engineering 25(1): 21-30, 2011. (pdf)

Yandong Liu and Daniel B. Neill. Detecting previously unseen outbreaks with novel symptom patterns. Emerging Health Threats Journal 4: 11074, 2011. (pdf)

Sriram Somanchi and Daniel B. Neill. Fast graph structure learning from unlabeled data for outbreak detection. Emerging Health Threats Journal 4: 11017, 2011. (pdf)

Skyler Speakman, Edward McFowland III, Sriram Somanchi, and Daniel B. Neill. Scalable detection of irregular disease clusters using soft compactness constraints. Emerging Health Threats Journal 4: 11121, 2011. (pdf)

Daniel B. Neill, Edward McFowland III, and Huanian Zheng. Fast subset scan for multivariate spatial biosurveillance. Emerging Health Threats Journal 4: s42, 2011. (pdf)

Daniel B. Neill and Yandong Liu. Generalized fast subset sums for Bayesian detection and visualization. Emerging Health Threats Journal 4: s43, 2011. (pdf)

2010

Daniel B. Neill and Gregory F. Cooper. A multivariate Bayesian scan statistic for early event detection and characterization. Machine Learning 79: 261-282, 2010. (pdf)

Daniel B. Neill. Fast subset sums for multivariate Bayesian scan statistics. Proceedings of the 2009 International Society for Disease Surveillance Annual Conference, 2010. (pdf)

Skyler Speakman and Daniel B. Neill. Fast graph scan for scalable detection of arbitrary connected clusters. Proceedings of the 2009 International Society for Disease Surveillance Annual Conference, 2010. (pdf)

Huanian Zheng, Rema Padman, Sharique Hasan, and Daniel B. Neill. A comparison of collaborative filtering methods for medication reconciliation. Proceedings of the 13th International Congress on Medical Informatics, 2010. (pdf)

2009

Daniel B. Neill. An empirical comparison of spatial scan statistics for outbreak detection. International Journal of Health Geographics 8: 20, 2009. (pdf) (open access)

Daniel B. Neill. Expectation-based scan statistics for monitoring spatial time series data. International Journal of Forecasting 25: 498-517, 2009. (pdf)

Daniel B. Neill, Gregory F. Cooper, Kaustav Das, Xia Jiang, and Jeff Schneider. Bayesian network scan statistics for multivariate pattern detection. In J. Glaz, V. Pozdnyakov, and S. Wallenstein, eds., Scan Statistics: Methods and Applications, 221-250, 2009. (pdf)

Xia Jiang, Gregory F. Cooper, and Daniel B. Neill. Generalized AMOC curves for evaluation and improvement of event surveillance. Proceedings of the American Medical Informatics Association Annual Symposium, 281-285, 2009. (pdf)

2008

Kaustav Das, Jeff Schneider, and Daniel B. Neill. Anomaly pattern detection in categorical datasets. Proceedings of the 14th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 169-176, 2008. (pdf)

Maxim Makatchev and Daniel B. Neill. Learning outbreak regions in Bayesian spatial scan statistics. Proceedings of the ICML/UAI/COLT Workshop on Machine Learning for Health Care Applications, 2008. (pdf)

Sharique Hasan, George T. Duncan, Daniel B. Neill, and Rema Padman. Towards a collaborative filtering approach to medication reconciliation. Proceedings of the American Medical Informatics Association Annual Symposium, 288-292, 2008. (pdf)

Christopher A. Harle, Daniel B. Neill, and Rema Padman. An information visualization approach to classification and assessment of diabetes risk in primary care. Proceedings of the 3rd INFORMS Workshop on Data Mining and Health Informatics, 2008. (pdf)

2007

Daniel B. Neill and Wilpen L. Gorr. Detecting and preventing emerging epidemics of crime. Advances in Disease Surveillance 4:13, 2007. (pdf)

Daniel B. Neill and Jeff Lingwall. A nonparametric scan statistic for multivariate disease surveillance. Advances in Disease Surveillance 4:106, 2007. (pdf)

2006

Daniel B. Neill. Detection of spatial and spatio-temporal clusters. Ph.D. thesis, Carnegie Mellon University, Department of Computer Science, Technical Report CMU-CS-06-142, 2006. (pdf)

Daniel B. Neill, Andrew W. Moore, and Gregory F. Cooper. A Bayesian spatial scan statistic. In Y. Weiss, et al., eds. Advances in Neural Information Processing Systems 18, 1003-1010, 2006. (pdf)

2005

Daniel B. Neill, Andrew W. Moore, Maheshkumar Sabhnani, and Kenny Daniel. Detection of emerging space-time clusters. Proceedings of the 11th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 218-227, 2005. (pdf)

Daniel B. Neill, Andrew W. Moore, Francisco Pereira, and Tom Mitchell. Detecting significant multidimensional spatial clusters. In L.K. Saul, et al., eds. Advances in Neural Information Processing Systems 17, 969-976, 2005. (pdf)

Daniel B. Neill and Andrew W. Moore. Anomalous spatial cluster detection. Proceedings of the KDD 2005 Workshop on Data Mining Methods for Anomaly Detection, 2005. (pdf)

Maheshkumar R. Sabhnani, Daniel B. Neill, Andrew W. Moore, Fu-Chiang Tsui, Michael M. Wagner, and Jeremy U. Espino. Detecting anomalous patterns in pharmacy retail data. Proceedings of the KDD 2005 Workshop on Data Mining Methods for Anomaly Detection, 2005. (pdf)

Paul Hsiung, Andrew Moore, Daniel Neill, and Jeff Schneider. Alias detection in link data sets. Proceedings of the First International Conference on Intelligence Analysis, 2005. (pdf)

2004

Daniel B. Neill and Andrew W. Moore. Rapid detection of significant spatial clusters. Proceedings of the 10th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 256-265, 2004. (pdf)

M. Wagner, F.-C. Tsui, J. Espino, W. Hogan, J. Hutman, J. Hersh, D. Neill, A. Moore, G. Parks, C. Lewis, and R. Aller. A national retail data monitor for public health surveillance. Morbidity and Mortality Weekly Report 53: 40-42, 2004. (pdf)