National Science Foundation, Award IIS-0953330
CAREER: Machine Learning and Event Detection for the Public Good
PI: Daniel B. Neill (neill @ cs.cmu.edu)

Funding duration: July 1, 2010 - June 30, 2016
Funding amount: $529,962

NOTE: This project has been completed as of June 2016, and the page is no longer being updated.

Project personnel:

Daniel B. Neill (Dean's Career Development Professor and Associate Professor of Information Systems, Heinz College, CMU)
Dylan Fitzpatrick (Ph.D. student, Joint Ph.D. in Machine Learning and Public Policy, Heinz College and School of Computer Science, CMU)
William Herlands (Ph.D. student, Joint Ph.D. in Machine Learning and Public Policy, Heinz College and School of Computer Science, CMU)
Abhinav Maurya (Ph.D. student, Heinz College, CMU)
Mallory Nobles (Ph.D. student, Heinz College, CMU)
Predrag Punosevac (research programmer and system administrator, CMU)
Zhe Zhang (Ph.D. student, Heinz College, CMU)

Project alumni:

Michael Baysek (research programmer and system administrator, CMU)
Feng Chen (Postdoctoral fellow, Heinz College, CMU)
Seth Flaxman (Ph.D. in Machine Learning and Public Policy, CMU)
Tarun Kumar (M.S., Very Large Information Systems, CMU)
Yandong Liu (M.S., Language Technologies, CMU)
Kai Liu (M.S., Very Large Information Systems, CMU)
Rajas Lonkar (M.S., Information Systems Management, CMU)
Edward McFowland III (Ph.D. in Information Systems, Heinz College, CMU)
Kenton Murray (M.S.,Language Technologies, CMU)
Amrut Nagasunder (M.S., Very Large Information Systems, CMU)
Yun Ni (M.S., Information Systems Management, CMU)
Kan Shao (Ph.D., Engineering and Public Policy, and M.S., Machine Learning, CMU)
Sriram Somanchi (Ph.D. in Information Systems, Heinz College, CMU)
Skyler Speakman (Ph.D. in Information Systems, Heinz College, CMU)
Donghan (Jarod) Wang (research programmer and system administrator, CMU)
Xin Wu (M.S., Very Large Information Systems, CMU)
Yating Zhang (M.S., Information Systems Management, CMU)

Project description:

The goal of this research is to create and explore novel methods for detection of emerging events in massive, complex real-world datasets. The approach consists of new algorithms to efficiently and exactly find the most anomalous subsets of a large, high-dimensional dataset, as well as methodological advances to incorporate incremental model learning from user feedback into event detection, incorporate society-scale data from emerging, transformative technologies such as cellular phones and user-generated web content, and augment event detection by creating methods and tools for event characterization, explanation, visualization, investigation and response.

The experimental research is integrated with a multi-pronged educational initiative to incorporate machine learning into the public policy curriculum through development of courses and seminars, workshops in machine learning and policy research and education, and establishment of a new Joint Ph.D. Program in Machine Learning and Policy. The results of this project will be incorporated into deployed event surveillance systems and applied to the public health, law enforcement, and health care domains, enabling more timely and accurate detection of emerging outbreaks of disease, prediction of emerging hot-spots of violent crime, and identification of anomalous patterns of patient care.

Detailed descriptions of our current research and educational activities, and results/findings are available here.


Publications:

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

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)

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. 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)

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)

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

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. 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)

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)

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)

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)

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)

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)

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)

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)


Presentations:

Daniel B. Neill. Fast subset sums for scalable Bayesian detection and visualization. Fifth International Workshop on Applied Probability, Madrid, Spain, July 2010. (pdf)

Skyler Speakman, Edward McFowland III, and Daniel B. Neill. Scalable detection of anomalous patterns with connectivity constraints. INFORMS Annual Conference, Austin, TX, November 2010. (pdf)

Edward McFowland III, Skyler Speakman, and Daniel B. Neill. Fast generalized subset scan for anomalous pattern detection. INFORMS Annual Conference, Austin, TX, November 2010. (pdf)

Daniel B. Neill, Edward McFowland III, and Huanian Zheng. Fast subset scan for multivariate spatial biosurveillance. International Society for Disease Surveillance Annual Conference, Park City, UT, December 2010. (pdf)

Daniel B. Neill and Yandong Liu. Generalized fast subset sums for Bayesian detection and visualization. International Society for Disease Surveillance Annual Conference, Park City, UT, December 2010. (pdf)

Daniel B. Neill. Research challenges for biosurveillance: the next ten years (invited plenary). International Society for Disease Surveillance Annual Conference, Park City, UT, December 2010. (pdf)

Daniel B. Neill. Spatial and subset scanning for multivariate health surveillance. Data Fusion Research Meeting, Ottawa, ON, March 2011. (pdf)

Daniel B. Neill. Machine learning for population health and disease surveillance. Advanced Analytics Workshop, Washington, DC, April 2011. (pdf)

Edward McFowland III and Daniel B. Neill. Fast generalized subset scan for anomalous pattern detection in mixed data sets. 17th Conference for African-American Researchers in the Mathematical Sciences, Los Angeles, CA, June 2011.

Daniel B. Neill. Fast multivariate subset scanning for scalable cluster detection. Joint Statistical Meetings 2011, Miami, FL, August 2011. (pdf)

Edward McFowland III and Daniel B. Neill. Efficient methods for anomalous pattern detection in general datasets. INFORMS Annual Conference, Charlotte, NC, November 2011. (pdf)

Sriram Somanchi and Daniel B. Neill. Fast learning of graph structure from unlabeled data for anomalous pattern detection. INFORMS Annual Conference, Charlotte, NC, November 2011. (pdf)

Skyler Speakman and Daniel B. Neill. Dynamic pattern detection with connectivity and temporal consistency constraints. INFORMS Annual Conference, Charlotte, NC, November 2011. (pdf)

Daniel B. Neill. Analytical methods for large scale surveillance of unstructured data. International Conference on Digital Disease Detection, Boston, MA, February 2012. (pdf)

Daniel B. Neill and Edward McFowland III. Fast generalized subset scan for anomalous pattern detection. Sixth International Workshop on Applied Probability, Jerusalem, Israel, June 2012. (pdf)

Daniel B. Neill, Skyler Speakman, Edward McFowland III, and Sriram Somanchi. Efficient subset scanning with soft constraints. Sixth International Workshop on Applied Probability, Jerusalem, Israel, June 2012. (pdf)

Skyler Speakman, Edward McFowland III, and Daniel B. Neill. Scalable detection of anomalous patterns with connectivity constraints. 29th Quality and Productivity Research Conference, Long Beach, CA, June 2012. (pdf)

Daniel B. Neill and Seth Flaxman. Detecting spatially localized subsets of leading indicators for event prediction. 32nd International Symposium on Forecasting, Boston, MA, June 2012. (pdf)

Daniel B. Neill. Predicting and preventing emerging outbreaks of crime. CMU Workshop on Machine Learning and Social Sciences, Pittsburgh, PA, October 2012. (pdf)

Sriram Somanchi and Daniel B. Neill. Fast graph structure learning from unlabeled data for event detection. INFORMS Annual Conference, Phoenix, AZ, October 2012.

Skyler Speakman, Yating Zhang, and Daniel B. Neill. Tracking dynamic water-borne outbreaks with temporal consistency constraints. International Society for Disease Surveillance Annual Conference, San Diego, CA, December 2012. (pdf)

Daniel B. Neill and Tarun Kumar. Fast multidimensional subset scan for outbreak detection and characterization. International Society for Disease Surveillance Annual Conference, San Diego, CA, December 2012. (pdf)

Daniel B. Neill. Fast subset scanning for scalable event and pattern detection. Stony Brook University, Stony Brook, NY, May 2013. (pdf)

Seth Flaxman and Daniel B. Neill. New tests for space-time interaction in spatio-temporal point processes. 2nd Spatial Statistics Conference, Columbus, OH, June 2013. (pdf)

Daniel B. Neill. Machine learning and event detection for the public good. Data Science for the Social Good Summer Fellowship Program, Chicago, IL, July 2013. (pdf)

Feng Chen and Daniel B. Neill. Non-parametric scan statistics for event detection and forecasting in heterogeneous social media graphs. INFORMS Annual Meeting, Minneapolis, MN, October 2013. (pdf)

Feng Chen and Daniel B. Neill. Non-parametric scan statistics for disease outbreak detection on Twitter. International Society for Disease Surveillance Annual Conference, New Orleans, LA, December 2013. (pdf)

Skyler Speakman, Sriram Somanchi, Edward McFowland III, and Daniel B. Neill. Penalized fast subset scanning. 6th International Conference on Computational and Methodological Statistics, London, UK, December 2013. (pdf)

Daniel B. Neill. Scaling up event and pattern detection to big data. MIT Workshop on Challenges in Big Data for Data Mining, Machine Learning and Statistics, Cambridge, MA, March 2014. (pdf)

Daniel B. Neill. Scaling up event and pattern detection to big data. NYU Stern School of Business, Information Systems Seminar, New York, NY, April 2014. (pdf)

Feng Chen and Daniel B. Neill. Non-parametric scan statistics for event detection and forecasting in heterogeneous social media graphs. Seventh International Workshop on Applied Probability, Antalya, Turkey, June 2014. (pdf)

Sriram Somanchi and Daniel B. Neill. A star-shaped scan statistic for detecting irregularly-shaped spatial clusters. Seventh International Workshop on Applied Probability, Antalya, Turkey, June 2014. (pdf)

Edward McFowland III and Daniel B. Neill. Discovering novel anomalous patterns in general data. Statistical Learning and Data Mining Meeting on Data Mining in Business and Industry, Durham, NC, June 2014. (pdf)

Seth R. Flaxman, Alexander J. Smola, and Daniel B. Neill. Kernel space-time interaction tests for identifying leading indicators of crime. Joint Statistical Meetings, Boston, MA, August 2014. (pdf)

Mallory Nobles, Seth Flaxman, and Daniel B. Neill. Urban predictive analytics. INFORMS Annual Meeting, San Francisco, CA, November 2014. (pdf)

Sriram Somanchi, David Choi, and Daniel B. Neill. StarScan: a novel scan statistic for irregularly-shaped spatial clusters. International Society for Disease Surveillance Annual Conference, Philadelphia, PA, December 2014. (pdf)

Mallory Nobles, Lana Deyneka, Amy Ising, and Daniel B. Neill. Identifying emerging novel outbreaks in textual emergency department data. International Society for Disease Surveillance Annual Conference, Philadephia, PA, December 2014. (pdf)

Skyler Speakman, Sriram Somanchi, Edward McFowland III, and Daniel B. Neill. Penalized fast subset scanning. 45th Symposium on the Interface of Computing Science and Statistics ("Best of JCGS" invited session), Morgantown, WV, June 2015. (pdf)

Daniel B. Neill and Feng Chen. Human rights event detection from heterogeneous social media graphs. Human Rights Media Central Workshop, Pittsburgh, PA, July 2015. (pdf)

Seth R. Flaxman, Andrew Gelman, Andrew Gordon Wilson, Daniel B. Neill, Hannes Nickisch, Alexander J. Smola, and Aki Vehtari. Large-scale Gaussian processes for spatiotemporal modeling of disease incidence. Joint Statistical Meetings, Seattle, WA, August 2015. (pdf)

Jason Hong, Tom Mitchell, Daniel B. Neill, and Aarti Singh. Machine learning and health: from neurons to society. World Economic Forum: Annual Meeting of the New Champions, Dalian, China, September 2015. (pdf)

Daniel B. Neill. Event and pattern detection at the societal scale. University of Chicago, Harris School of Public Policy, October 2015. (pdf)

Daniel B. Neill. Event and pattern detection at the societal scale. Harvard University, School of Engineering and Applied Sciences, November 2015. (pdf)

Seth R. Flaxman, Andrew Gordon Wilson, Daniel B. Neill, Hannes Nickisch, and Alexander J. Smola. Novel approaches to local area spatiotemporal crime rate forecasting with Gaussian processes. American Society of Criminology Annual Meeting, Washington, DC, November 2015. (pdf)

Daniel B. Neill. Event and pattern detection for urban systems. New York University, Center for Urban Science and Progress, February 2016. (pdf)

Daniel B. Neill. Event and pattern detection at the societal scale. New York University, Courant Institute, Department of Computer Science, February 2016. (pdf)

Daniel B. Neill. Event and pattern detection at the societal scale. Georgia Institute of Technology, School of Computational Science and Engineering, March 2016. (pdf)

Daniel B. Neill. Event and pattern detection for urban systems. New York University, Wagner School of Public Service, April 2016. (pdf)

Daniel B. Neill. Fast subset scan for population health and disease surveillance. Harvard University, Department of Biostatistics, T.H. Chan School of Public Health, May 2016. (pdf)

Edward McFowland III, Sriram Somanchi, and Daniel B. Neill. Efficient discovery of heterogeneous treatment effects in randomized experiments via anomalous pattern detection. Eighth International Workshop on Applied Probability, Toronto, Canada, June 2016. (pdf)

Sriram Somanchi, Edward McFowland III, and Daniel B. Neill. Detecting anomalous patterns of care using health insurance claims. Eighth International Workshop on Applied Probability, Toronto, Canada, June 2016. (pdf)

Dylan Fitzpatrick, Yun Ni, and Daniel B. Neill. Support vector subset scan for spatial pattern detection. Eighth International Workshop on Applied Probability, Toronto, Canada, June 2016. (pdf)


Broader Impacts: The Machine Learning and Policy (MLP) Initiative

With the critical importance of addressing global policy problems ranging from disease pandemics to crime and terrorism, and the continuously increasing size and complexity of policy data, the use of machine learning has become increasingly essential for data-driven policy analysis and for development of new, practical information technologies that can be directly applied for the public good. The numerous challenges facing our world will require broad, successful innovations at the intersection of machine learning and public policy. This endeavor will require widespread collaboration between machine learning and policy researchers, increased emphasis on the education of future researchers with in-depth knowledge of both disciplines, and a broadly shared research focus on developing novel machine learning methods which directly address critical policy challenges. We are working to build a multi-pronged curricular program, the Machine Learning and Policy (MLP) initiative. This program will facilitate the widespread use of machine learning methods for the public good by incorporating machine learning throughout the public policy curriculum. Key components of this program include a new Joint Ph.D. program in Machine Learning and Public Policy, an introductory course in machine learning ("Large Scale Data Analysis for Policy") geared toward public policy students, a Ph.D.-level research seminar in Machine Learning and Policy, and a course series in "Special Topics in Machine Learning and Policy", with courses including "Event and Pattern Detection" (Spring 2010, Spring 2014), "Machine Learning for the Developing World" (Spring 2011), "Harnessing the Wisdom of Crowds" (Spring 2012), and "Mining Massive Datasets" (Spring 2013). Project PI Daniel Neill was also involved in the creation of a CMU workshop and seminar series in "Machine Learning and Social Sciences" and in creating a new "Policy Analytics" track for Heinz College's MS in Public Policy and Management program.


Tutorials and Educational Material:

Daniel B. Neill. Lecture slides for the course, Large Scale Data Analysis for Public Policy. Last taught Fall 2015. (link)

Daniel B. Neill. Machine learning and event detection for the public good. Guest lecture, April 2011. (pdf)

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)

Daniel B. Neill. Spatial scan tips and tricks for practical outbreak detection. Invited webinar for the International Society for Disease Surveillance, January 2011. (pdf)


News and Awards:

The Project PI, Dr. Neill, was named one of the "AI's 10 to Watch" by IEEE Intelligent Systems, Jan/Feb 2011. (link)

Edward McFowland III was awarded an NSF Graduate Research Fellowship (link) and an AT&T Labs Research Fellowship, 2011. (link)

Edward McFowland III was the 2012 winner of the Suresh Konda Award, presented yearly to Heinz College's best First Heinz Research Paper.

Seth Flaxman was the 2013 winner of the Suresh Konda Award, presented yearly to Heinz College's best First Heinz Research Paper.

Sriram Somanchi was the 2013 winner of the George Duncan Award, presented yearly to Heinz College's best Second Heinz Research Paper.

Edward McFowland III was the 2015 winner of the William W. Cooper Doctoral Dissertation Award, presented yearly to Heinz College's best Ph.D. dissertation.

Our paper, "Penalized Fast Subset Scanning", in the Journal of Computational and Graphical Statistics, was selected for the "Best of JCGS" invited session at the 45th Symposium on the Interface of Computing Science and Statistics (June 2015) by the journal's editor in chief.

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.

William Herlands was the 2016 winner of the Suresh Konda Award, presented yearly to Heinz College's best First Heinz Research Paper.

Our crime prediction work with the Pittsburgh Bureau of Police was featured in an editorial in the 30 Sep 2016 issue of Science.


This material is based upon work supported by the National Science Foundation, grants IIS-0953330 (primary funding source), IIS-0916345, and IIS-0911032. 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.

Back to Daniel's home page
Contact the PI: Daniel Neill, neill (at) cs (dot) cmu (dot) edu
Final update: June 2017.