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)
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