Publications in chronological order:
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004 and earlier
2024
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)
2023
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)
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)
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)
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)
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)
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)
2022
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)
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)
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)
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)
2021
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)
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)
Konstantin Klemmer, Daniel B. Neill, and Stephen A. Jarvis. Understanding
spatial patterns in rape reporting delays. Royal Society Open
Science 8: 201795, 2021. (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)
2020
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)
Amr Magdy, Xun Zhou, and Daniel B. Neill. Guest editorial: special issue
on analytics for local events and news. Geoinformatica 24: 267-268,
2020. (pdf)
2019
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)
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)
Daniel B. Neill. Bayesian scan statistics. In J. Glaz and M. V. Koutras,
eds., Handbook of Scan Statistics, 2019. (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)
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)
Mallory Nobles, Ramona Lall, Robert Mathes, and Daniel B. Neill.
Multidimensional semantic scan for pre-syndromic disease surveillance.
Online Journal of Public Health Informatics 11(1): e255, 2019.
Winner of the International Society for Disease Surveillance
Outstanding Student or Post-Degree Abstract Award. (pdf)
Roberto C.S.N.P. Souza, Daniel B. Neill, Renato Assuncao, and Wagner Meira
Jr. Identifying high-risk areas for dengue infection using mobility
patterns on Twitter. Online Journal of Public Health Informatics
11(1): e246, 2019. (pdf)
2018
Roberto C.S.N.P. Souza, Renato M. Assuncao, Daniel B. Neill, Luis G.S.
Silva, and Wagner Meira Jr. Spatial risk modeling for infectious disease
surveillance using population movement data. Proc. NeurIPS 2018
Workshop on Modeling and Decision-Making in the Spatio-Temporal
Domain, 2018. (pdf)
Konstantin Klemmer, Daniel B. Neill, and Stephen A. Jarvis. Modeling rape
reporting delays using spatial, temporal and social features. Proc.
NeurIPS 2018 Workshop on Modeling and Decision-Making in the
Spatio-Temporal Domain, 2018. (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)
Maria de Arteaga, William Herlands, Daniel B. Neill, and Artur Dubrawski.
Machine learning for the developing world. ACM Transactions on
Management Information Systems 9(2): 9.1-9.14, 2018. (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)
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)
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)
2017
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,
PMLR 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, PMLR 37: 607-616, 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)
2014
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)
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)
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)
Daniel B. Neill. Cascade effects in heterogeneous
populations. Rationality and Society 17(2): 191-241, 2005.
(pdf)
2004 and earlier
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)
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. Fully automatic word sense induction by
semantic clustering. Cambridge University, masters thesis, M.Phil. in
Computer Speech, 2002.
(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)
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