Here are some selected publications, organized by topic:

Event and Pattern Detection:

SUBSET SCAN
TWITTER EVENT DETECTION
BAYESIAN SCAN STATISTICS
SPATIAL SCAN STATISTICS
GENERAL

Other Research Topics:

CAUSAL INFERENCE
BAYESIAN NONPARAMETRICS / GAUSSIAN PROCESSES / SPATIAL MACHINE LEARNING
ALGORITHMIC FAIRNESS
PUBLIC HEALTH / DISEASE SURVEILLANCE
HEALTH CARE INFORMATION SYSTEMS
YOUTH VIOLENCE
GAME THEORY
NATURAL LANGUAGE PROCESSING


EVENT AND PATTERN DETECTION- SUBSET SCAN

Charles A. Pehlivanian and Daniel B. Neill. Efficient optimization of partition scan statistics via the Consecutive Partitions Property. Journal of Computational and Graphical Statistics 32(2): 712-729, 2023. (pdf)

Chunpai Wang, Daniel B. Neill, and Feng Chen. Calibrated nonparametric scan statistics for anomalous pattern detection in graphs. Proc. 36th AAAI Conf. on Artificial Intelligence, 4201-4209, 2022. (pdf) (technical appendix)

Dylan J. Fitzpatrick, Yun Ni, and Daniel B. Neill. Support vector subset scan for spatial pattern detection. Computational Statistics and Data Analysis 157: 107149, 2021. (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)

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).

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)

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)

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


EVENT AND PATTERN DETECTION- TWITTER EVENT DETECTION

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


EVENT AND PATTERN DETECTION- BAYESIAN SCAN STATISTICS

Daniel B. Neill. Bayesian scan statistics. In J. Glaz and M. V. Koutras, eds., Handbook of Scan Statistics, 2019. (pdf)

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)

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, 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)

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)

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)


EVENT AND PATTERN DETECTION- SPATIAL SCAN STATISTICS

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)

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, 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 and Andrew W. Moore. Anomalous spatial cluster detection. Proceedings of the KDD 2005 Workshop on Data Mining Methods for Anomaly Detection, 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. Rapid detection of significant spatial clusters. Proceedings of the 10th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 256-265, 2004. (pdf)


EVENT AND PATTERN DETECTION- GENERAL

Feng Chen, Petko Bogdanov, Daniel B. Neill, and Ambuj K. Singh. Anomalous and significant subgraph detection in attributed networks. Tutorial presented at IEEE International Conference on Big Data, 2016. (part 1) (part 2)

Daniel B. Neill and Weng-Keen Wong. A tutorial on event detection. Presented at the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2009. (pdf)

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)

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)


CAUSAL INFERENCE

Benjamin Jakubowski, Sriram Somanchi, Edward McFowland III, and Daniel B. Neill. Exploiting discovered regression discontinuities to debias conditioned-on-observable estimators. Journal of Machine Learning Research 24(133): 1-57, 2023. (link) (pdf)

Edward McFowland III, Sriram Somanchi, and Daniel B. Neill. Efficient discovery of heterogeneous treatment effects in randomized experiments via anomalous pattern detection. Working paper, 2019. (arXiv)

William Herlands, Daniel B. Neill, Hannes Nickisch, and Andrew Gordon Wilson. Change surfaces for expressive multidimensional changepoints and counterfactual prediction. Journal of Machine Learning Research 20(99): 1-51, 2019. (
pdf)

William Herlands, Edward McFowland III, Andrew Gordon Wilson, and Daniel B. Neill. Automated local regression discontinuity design discovery. Proceedings of the 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 1512-1520, 2018. (pdf)

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)


BAYESIAN NONPARAMETRICS / GAUSSIAN PROCESSES / SPATIAL MACHINE LEARNING

Konstantin Klemmer, Nathan S. Safir, and Daniel B. Neill. Positional encoder graph neural networks for geographic data. Proc. 26th Intl. Conf. on Artificial Intelligence and Statistics, PMLR 206: 1379-1389, 2023. Also presented at NeurIPS Workshop on Tackling Climate Change with Machine Learning, 2022. (pdf)

K. Klemmer, T. Xu, B. Acciaio, and D. B. Neill. SPATE-GAN: Improved generative modeling of dynamic spatio-temporal patterns with an autoregressive embedding loss. Proc. 36th AAAI Conf. on Artificial Intelligence, 4523-4531, 2022. (pdf) (technical appendix)

Konstantin Klemmer and Daniel B. Neill. Auxiliary-task learning for geographic data with autoregressive embeddings. Proc. 29th ACM SIGSPATIAL Intl. Conf. on Advances in Geographic Information Systems, 141-144, 2021. (short version) (long version)

William Herlands, Daniel B. Neill, Hannes Nickisch, and Andrew Gordon Wilson. Change surfaces for expressive multidimensional changepoints and counterfactual prediction. Journal of Machine Learning Research 20(99): 1-51, 2019. (pdf)

William Herlands, Edward McFowland III, Andrew Gordon Wilson, and Daniel B. Neill. Gaussian process subset scanning for anomalous pattern detection in non-iid data. Proc. 21st International Conference on Artificial Intelligence and Statistics, PMLR 84: 425-434, 2018. (pdf)

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, PMLR 51: 1013-1021, 2016. (pdf)

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)

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, PMLR 37: 607-616, 2015. (pdf)


ALGORITHMIC FAIRNESS

Pavan Ravishankar, Qingyu Mo, Edward McFowland III, and Daniel B. Neill. Provable detection of propagating sampling bias in prediction models. Proc. 37th AAAI Conf. on Artificial Intelligence, 9562-9569, 2023. (pdf) (supplement)

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)


PUBLIC HEALTH / DISEASE SURVEILLANCE

Ougni Chakraborty, Kacie L. Dragan, Ingrid Gould Ellen, Sherry A. Glied, Renata E. Howland, Daniel B. Neill*, and Scarlett Wang (listed alphabetically; *corresponding author). Housing-sensitive health conditions can predict poor-quality housing. Health Affairs 43(2): 297-304, 2024. (open access)

B. Allen, R. C. Schell, V. A. Jent, M. Krieger, C. Pratty, B. D. Hallowell, M. Basta, W. C. Goedel, J. L. Yedinak, Y. Li, A. R. Cartus, B. D. L. Marshall, M. Cerda, J. Ahern, and D. B. Neill. PROVIDENT: Development and validation of a machine learning model to predict neighborhood-level overdose risk in Rhode Island. Epidemiology 35(2): 232-240, 2024. (pdf)

C. A. Koziatek, I. Bohart, R. Caldwell, J. Swartz, P. Rosen, S. Desai, K. Krol, D. B. Neill, and D. C. Lee. Neighborhood-level risk factors for severe hyperglycemia among Emergency Department patients without a prior diabetes diagnosis. Journal of Urban Health 100: 802-810, 2023. (pdf)

B. Allen, D. B. Neill, R. C. Schell, J. Ahern, B. Hallowell, M. Krieger, V. A. Jent, W. C. Goedel, A. R. Cartus, J. L. Yedinak, C. Pratty, B. D. L. Marshall, and M. Cerda. Translating predictive analytics for public health practice: a case study of overdose prevention in Rhode Island. American Journal of Epidemiology 192(10): 1659-1668, 2023. (pdf)

Katie Rosman and Daniel B. Neill. Detecting anomalous networks of opioid prescribers and dispensers in prescription drug data. Proc. 37th AAAI Conf. on Artificial Intelligence, 14470-14477, 2023. (pdf) (supplement)

Mallory Nobles, Ramona Lall, Robert W. Mathes, and Daniel B. Neill. Presyndromic surveillance for improved detection of emerging public health threats. Science Advances 8(44): eabm4920, 2022. (open access) (pdf)

R. C. Schell, B. Allen, W. C. Goedel, B. D. Hallowell, R. Scagos, Y. Li, M. S. Krieger, D. B. Neill, B. D. L. Marshall, M. Cerda, and J. Ahern. Identifying predictors of opioid overdose death at a neighborhood level with machine learning. American Journal of Epidemiology 191(3): 526-533, 2022. (pdf)

B. D. L. Marshall, N. Alexander-Scott, J. L. Yedinak, B. Hallowell, W. C. Goedel, B. Allen, R. C. Schell, Y. Li, M. S. Krieger, C. Pratty, J. Ahern, D. B. Neill, and M. Cerda. Preventing overdose using information and data from the environment (PROVIDENT): Protocol for a randomised, population-based, community intervention trial. Addiction 117(4): 1152-1162, 2022. (pdf)

Daniel Zeng, Zhidong Cao, and Daniel B. Neill. AI-enabled public health surveillance: from local detection to global epidemic monitoring and control. In L. Xing, M. L. Giger, and J. K. Min, eds., Artificial Intelligence in Medicine, 437-453, 2021. (pdf)

Roberto C.S.N.P. Souza, Renato M. Assuncao, Daniel B. Neill, and Wagner Meira Jr. Detecting spatial clusters of disease infection risk using sparsely sampled social media mobility patterns. Proc. 27th ACM SIGSPATIAL Intl. Conf. on Advances in Geographic Information Systems, 359-368, 2019. (pdf)

Roberto C.S.N.P. Souza, Renato M. Assuncao, Derek M. Oliveira, Daniel B. Neill, and Wagner Meira Jr. Where did I get dengue? Detecting spatial clusters of infection risk with social network data. Spatial and Spatio-temporal Epidemiology 29: 163-175, 2019. (pdf)

Daniel B. Neill and William Herlands. Machine learning for drug overdose surveillance. Journal of Technology in Human Services 36(1): 8-14, 2018. Presented at Bloomberg Data for Good Exchange Conference, 2017. (pdf) (link to journal version)

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)

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)

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

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)

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)


HEALTH CARE INFORMATION SYSTEMS

Said A. Ibrahim, Mary E. Charlson, and Daniel B. Neill. Big data analytics and the struggle for equity in health care: the promise and perils. Health Equity 4(1): 99-101, 2020. (pdf)

Sriram Somanchi, Daniel B. Neill, and Anil V. Parwani. Discovering anomalous patterns in large digital pathology images. Statistics in Medicine 37: 3599-3615, 2018. (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)

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

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)

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)

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)

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)


YOUTH VIOLENCE AND CRIME PREVENTION

Konstantin Klemmer, Daniel B. Neill, and Stephen A. Jarvis. Understanding spatial patterns in rape reporting delays. Royal Society Open Science 8: 201795, 2021. (pdf)

Dylan J. Fitzpatrick, Wilpen L. Gorr, and Daniel B. Neill. Keeping score: predictive analytics in policing. Annual Review of Criminology 2: 473-491, 2019. (link)

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)


GAME THEORY

Daniel B. Neill. Cascade effects in heterogeneous populations. Rationality and Society 17(2): 191-241, 2005. (pdf)

Daniel B. Neill. Evolutionary stability for large populations. Journal of Theoretical Biology 227(3): 397-401, 2004. (pdf)

Daniel B. Neill. Evolutionary dynamics with large aggregate shocks. Dept. of Computer Science, Technical Report CMU-CS-03-197, 2003. (pdf)

Daniel B. Neill. Cooperation and coordination in the Turn-Taking Dilemma. Proceedings of the Ninth Conference on Theoretical Aspects of Rationality and Knowledge: 231-244, 2003. (pdf)

Daniel B. Neill. Optimality under noise: higher memory strategies for the Alternating Prisoner's Dilemma. Journal of Theoretical Biology 211(2): 159-180, 2001. (pdf)


NATURAL LANGUAGE PROCESSING

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)

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)

Daniel B. Neill. Fully automatic word sense induction by semantic clustering. Cambridge University, masters thesis, M.Phil. in Computer Speech, 2002. (pdf)



I gratefully acknowledge funding support from the National Science Foundation, grants IIS-1926470, IIS-0916345, IIS-0911032, and IIS-0953330, the NSF Program on Fairness in Artificial Intelligence in Collaboration with Amazon, grant IIS-2040898, a UPMC Healthcare Technology Innovation Grant, funding from the John D. and Catherine T. MacArthur Foundation and Richard King Mellon Foundation, and a gift from the Disruptive Health Technology Institute. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation, UPMC, DHTI, Amazon, Richard King Mellon Foundation, or MacArthur Foundation.

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Last updated: 2/6/2024