Daniel B. Neill, Ph.D.|
Associate Professor of Information Systems
Director, Event and Pattern Detection Laboratory
H.J. Heinz III College of Information Systems and Public Policy
Carnegie Mellon University
neill @ cs.cmu.edu
Visiting Professor of Urban Analytics
Center for Urban Science and Progress
New York University
daniel.neill @ nyu.edu
I am an Associate Professor of Information Systems in the Heinz College at Carnegie Mellon
University, where I have been the H.J. Heinz III College Dean's Career Development Professor.
I also hold courtesy appointments in the Machine Learning Department and Robotics Institute in CMU's School of Computer Science, and an
adjunct appointment in the Department
of Biomedical Informatics at the University of Pittsburgh. I received my
Ph.D. in Computer Science from CMU in 2006. Before that, I received my
B.S.E. from Duke University, M.Phil. from Cambridge University, and M.S. from
Carnegie Mellon. At CMU, I direct the Event and Pattern Detection
Laboratory, and co-direct the Healthcare
Information Technology thrust of Heinz College's iLab.
I will be on leave from CMU, as Visiting Professor of Urban Analytics at New York University's Center for Urban Science and Progress, from 7/1/2016-6/30/2018. I will continue to direct the EPD Lab and advise my current students, but am not accepting new CMU students while on leave.
I am guest co-editor of a special issue of GeoInformatica on "Analytics for Local Events and News". Submissions are due August 15th. Please feel free to distribute this call for papers. Note that all papers should be submitted through the Springer GeoInformatica website.
Our "pre-syndromic surveillance" team (Daniel Neill and Mallory Nobles)
has been named one of five first-stage winners of
the Department of Homeland Security's Hidden Signals Challenge, pioneering new ways to detect emerging biothreats.
Our rodent prevention work was recently featured in an article on CityLab.
According to the article, "The city of Chicago is still running Neill's predictive analytics approach and has
touted that it's 20 percent more effective than the traditional method of baiting rats after they've been
Our paper on Semantic Scan: Detecting Subtle, Spatially Localized Events in Text Streams was named the
winner of this year's Yelp Dataset Challenge. Our approach for identifying emerging topics can be
used both for public health (detecting "novel" outbreaks with rare or previously unseen symptom patterns) as well as identifying emerging regional
business trends. Thanks to both Yelp and CMU for their very
nice press coverage of this work!
Our crime prediction work with the Pittsburgh Bureau of Police was featured in an editorial in the 30 Sep 2016 issue of
Our comprehensive review article, "Youth violence: what we know and what we need to know", was featured in
a press release by the
American Psychological Association. The article was published in the January 2016 issue of the APA's
flagship journal, American Psychologist, and is available here.
We are grateful to the Richard King Mellon Foundation for their support of our project, "Urban Predictive Analytics for a Safer and Cleaner Pittsburgh", as part of the award, "Metro21: Knowledge-Powered Pittsburgh to Improve Urban Quality of Life". More information on this project is available here.
What can machine learning do for the healthcare industry? Here are some examples from my
own work, presented as part of the UPMC Enterprises "Inspiration, Innovation, and Excellence" talk series. And here is a related summary of our lab's recent work and ongoing projects in
healthcare and other domains.
Click here for more EPD Lab news updates.
I am currently teaching four courses at the Heinz College. Course
descriptions, sample syllabi, and lecture slides can be obtained by
clicking on the course names below, and current course materials are
available on Blackboard.
Statistics for IT
Managers (95-796) is the core statistics course for students in the Master
of Information Systems Management program.
Large Scale Data Analysis for Public
Policy (90-866) is a master's level course which focuses on the
application of artificial intelligence and machine learning methods to
real-world policy problems.
I am also teaching two Ph.D.-level seminar courses, intended for doctoral
students (and qualified master's students) from Heinz College, the Machine
Learning Department, and other university departments who wish to engage
in cutting-edge research at the intersection of machine learning and
public policy. The Research Seminar in
Machine Learning and Policy (90-904, cross-listed in MLD as 10-830)
is a half-semester course which covers a broad range of MLP topics.
Special Topics in Machine Learning and Policy (90-921, cross-listed in MLD
as 10-831) is a half-semester course which will explore a single MLP topic
in detail. Topics covered include Event and Pattern Detection
(Spring 2010 and Spring 2014), Machine Learning for the Developing World (Spring
2011), Harnessing the Wisdom of Crowds (Spring 2012), and Mining Massive Datasets (Spring 2013).
I also direct the Joint Ph.D. Program in Machine Learning and
Public Policy, offered jointly by the Heinz College and Machine Learning
Department at CMU. Information about this program is available
My research is focused on novel statistical and computational methods for discovery of emerging events and other relevant patterns in complex
and massive datasets, applied to real-world policy problems ranging from medicine and public health to law enforcement and security.
Application areas include disease surveillance (e.g., using electronically available public health data such as hospital visits and
medication sales to automatically identify and characterize emerging outbreaks), law enforcement (e.g., detection and prediction of
crime patterns using offense reports and 911 calls), health care (e.g., detecting anomalous patterns of care which significantly
impact patient outcomes), and urban analytics (e.g., helping city governments to predict and proactively respond to emerging patterns
of citizen needs).
A more detailed description of my research, updated December 2015, is available here,
and a complete list of publications is available in my CV. Also, please see our new Event and Pattern Detection
Laboratory web site, http://epdlab.heinz.cmu.edu, for the most up to date descriptions of our
ongoing research projects, and links to our publications and presentations.
My research has been partially supported by the following grants from the National Science Foundation:
NSF IIS-0953330, CAREER: Machine Learning and Event Detection for the
Public Good (summary) (NSF
page) (project page).
NSF IIS-0916345, Fast Subset Scan for Anomalous Pattern Detection (summary) (NSF
page) (project page).
NSF IIS-0911032, Discovering Complex Anomalous Patterns (summary) (NSF
I also gratefully acknowledge funding support from a UPMC Healthcare Technology Innovation Grant, NSF Graduate Research Fellowship, the John
D. and Catherine T. MacArthur Foundation, Richard King Mellon Foundation, and 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, Richard King Mellon Foundation, or MacArthur Foundation.
Below are links to some recent papers, organized by topic. Additional papers and presentations are accessible through
the Event and Pattern Detection Laboratory. A complete list of publications is available in my CV.
EVENT AND PATTERN DETECTION- SUBSET SCAN
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, in press.
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.
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.
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,
Daniel B. Neill. An empirical comparison of spatial scan statistics for
outbreak detection. International Journal of Health Geographics 8:
20, 2009. (pdf) (open
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.
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.
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,
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,
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.
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.
BAYESIAN NONPARAMETRICS / GAUSSIAN PROCESSES
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, 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, JMLR: W&CP 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, JMLR: W&CP 37, 2015. (pdf)
PUBLIC HEALTH / DISEASE SURVEILLANCE
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.
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.
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.
HEALTH CARE INFORMATION SYSTEMS
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.
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.
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.
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.
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)
Daniel B. Neill. Cascade effects in heterogeneous
populations. Rationality and Society 17(2): 191-241, 2005.
Daniel B. Neill. Evolutionary stability for large populations.
Journal of Theoretical Biology 227(3): 397-401, 2004.
Daniel B. Neill. Evolutionary dynamics with large aggregate
shocks. Dept. of Computer Science, Technical Report CMU-CS-03-197, 2003.
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.
Daniel B. Neill. Optimality under noise: higher memory
strategies for the Alternating Prisoner's Dilemma. Journal of
Theoretical Biology 211(2): 159-180, 2001.
NATURAL LANGUAGE PROCESSING
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.
Daniel B. Neill. Fully automatic word sense induction by
semantic clustering. Cambridge University, masters thesis, M.Phil. in
Computer Speech, 2002.
Event and Pattern Detection Laboratory
Arts and Letters Daily