Publications and Preprints 

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2023

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[77] Manring, I, Hill, J., Mohler, G., P.J. Brantingham, Williams, T., and White, B.  Low-Cost Gunshot Detection System with Localization for Community Based Violence Interruption. The 10th IEEE International Conference on
Data Science and Advanced Analytics (DSAA 2023).

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[76] Ray, B., Korzeniewski, S., Mohler, G., Carroll, J., del Pozo, B., Victor, G., Hedden, B., and Hyun, P.  Spatiotemporal analysis exploring the effect of law enforcement drug market disruptions on overdose.  American Journal of Public Health (2023).

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[75] R. Pandey, J. Carter, J. Hill and G. Mohler.  Rewiring police officer training networks to reduce forecasted use of force.  Proceedings of 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, (KDD 2023).

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[74] X. Miscouridou, S. Bhatt, G. Mohler, S. Flaxman, and S. Mishra.  Cox-Hawkes: doubly stochastic spatiotemporal Poisson processes.  Transactions on Machine Learning Research (2023).

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[73] G. Mohler and J. Mateu.  Second order preserving point process permutations.  Stat (2023).  [code]

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[72] Badirli, S., Picard, C. J., Mohler, G., Richert, F., Akata, Z., and Dundar, M.  Classifying the unknown: Insect identification with deep hierarchical Bayesian learning. Methods in Ecology and Evolution (2023).

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[71] Piza, E. L., Hatten, D. N., Carter, J. G., Baughman, J. H., and Mohler, G.  Gunshot Detection Technology Time Savings and Spatial Precision: An Exploratory Analysis in Kansas City. Policing (2023).

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2022

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[70] MacDonald, J., Mohler, G. and Brantingham, P.J.  Association between race, shooting hot spots, and the surge in gun violence during the COVID-19 pandemic in Philadelphia, New York and Los Angeles.  Preventive Medicine (2022).

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[69] M. B. Short and G. Mohler.  A fully Bayesian, logistic regression tracking algorithm for mitigating disparate misclassfication.  International Journal of Forecasting (2022).

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[68] Wen-Hao Chiang, X. Liu, and G. Mohler.  Hawkes process modeling of COVID-19 with mobility leading indicators and spatial covariates.  International Journal of Forecasting (2022).

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[67] B. Chen, P. Shrestha, A. L. Bertozzi, G. Mohler, and F. Schoenberg, A Novel Point Process Model for COVID-19: Multivariate Recursive Hawkes Process, in Predicting Pandemics in a Globally Connected World, Birkhauser-Springer, eds. Nicola Bellomo and Mark A.J. Chaplain (2022).

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[66] W. Chiang and G. Mohler.  Hawkes process multi-armed bandits for search and rescue.  IEEE International Conference on Machine Learning and Applications, ICMLA 2022.

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[65] S. Khorshidi, B. Wang, and G. Mohler.  Adversarial attacks on deep temporal point processes.  IEEE International Conference on Machine Learning and Applications, ICMLA 2022.

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[64] X. Liu, S. Fang, G. Mohler, J. Carlson and Y. Xiao.  Time to event modeling of subreddit transitions to r/SuicideWatch.  IEEE International Conference on Machine Learning and Applications, ICMLA 2022.

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[63] D. Sledge, H. F. Thomas, B. L. Hoang, and G. Mohler. Impact of Medicaid, Race/Ethnicity, and Criminal Justice Referral on Opioid Use Disorder Treatment.  Journal of the American Academy of Psychiatry and the Law.  (2022).  DOI: 10.29158/JAAPL.210137-21

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[62] Brantingham, P. Jeffrey, George Mohler, and John MacDonald. Changes in Public-Police Cooperation Following the Murder of George Floyd. PNAS Nexus (2022).

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2021

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[61] G. Mohler and M. Porter.  A note on the multiplicative fairness score in the NIJ recidivism forecasting challenge.  Crime Science 10.1 (2021): 1-5. [code]

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[60] Brantingham, P. J., Carter, J., MacDonald, J., Melde, C., and Mohler, G. (2021). Is the recent surge in violence in American cities due to contagion?  Journal of Criminal Justice, 76.

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[59] S. Badirli, Z. Akata, G. Mohler, C. Picard, and M. Dundar.  Fine-Grained Zero-Shot Learning with DNA as Side Information.  Conference on Neural Information Processing Systems, NeurIPS 2021. 

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[58] H. Sha, M. Al Hasan, and G. Mohler.  Source detection on networks using spatial temporal graph convolutional networks.  IEEE International Conference on Data Science and Advanced Analytics, DSAA 2021.

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[57] G. Mohler, S. Mishra, B. Ray, L. Magee, P. Huynh, M. Canada, D. O'Donnell, and S. Flaxman.  A modified two-process Knox test for investigating the relationship between law enforcement opioid seizures and overdoses.  Proceedings of the Royal Society A 477.2250 (2021): 20210195.

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[56] H. Sha, M. Al Hasan, and G. Mohler.   Group link prediction Using Convolutional Variational Autoencoder.  AAAI Conference on Weblogs and Social Media, ICWSM 2021.

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[55] S. Khorshidi, J. Carter, G. Mohler, and G. Tita.  Explaining crime diversity with Google street view.  Journal of Quantitative Criminology.

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[54] P. J. Brantingham, G. Tita, and G. Mohler.  Gang-related crime in Los Angeles remained stable following COVID-19 social distancing orders.  Criminology and Public Policy.

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[53] H. Sha, M. Al Hasan, G. Mohler.  Learning Network Event Sequences Using Long Short-term Memory and Second-order Statistic Loss.  Statistical Analysis and Data Mining.

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[52] X. Liu, J. Carter, B. Ray, and G. Mohler.  Point process modeling of drug overdoses with heterogeneous and missing data.  Annals of Applied Statistics.  

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[51] G. Mohler, M.B. Short, F. Schoenberg and D. Sledge.  Analyzing the Impacts of Public Policy on COVID-19 Transmission: A Case Study of the Role of Model and Dataset Selection Using Data from Indiana.  Statistics and Public Policy.

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[50] J. Carter, G. Mohler, R. Raje, N. Chowdhury, and S. Pandey.  The Indianapolis Harmspot Policing Experiment.  Journal of Criminal Justice.

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2020

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[49] N. Glober, G. Mohler, P. Huynh, T. Arkins, D. O'Donnell, J. Carter and B. Ray.  Impact of COVID-19 Pandemic on Drug Overdoses in Indianapolis.  Journal of Urban Health.

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[48] S. Khorshidi, J. Carter, and G. Mohler.  Repurposing recidivism models for forecasting police officer use of force.  IEEE Big Data Workshop on Smart and Connected Communities.

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[47] H. Sha, M. Al Hasan, J. Carter and G. Mohler.  Interpretable Hawkes Process Spatial Crime Forecasting with TV-Regularization.  IEEE Big Data Workshop on Smart and Connected Communities.

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[46] J. Wong, H. Sha, M. Al Hasan, G. Mohler, S. Becker, and C. Wiltse.  Automated Corn Ear Height Prediction Using Video-Based Deep Learning.  IEEE BigData Workshop on Smart Farming, Precision Agriculture, and Supply Chain.

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[45] R. Pandey, P.J. Brantingham, C. Uchida, and G. Mohler.  Building knowledge graphs of homicide investigation chronologies.  International Workshop on Mining and Learning in the Legal Domain (MLLD-2020).

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[44] G. Mohler, E. McGrath, C. Buntain, and G. LaFree.  Hawkes binomial topic model with applications to coupled conflict-Twitter data.  Annals of Applied Statistics.  [code]

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[43] Andrea L Bertozzi, Elisa Franco, George Mohler, Martin B Short, Daniel Sledge. The challenges of modeling and forecasting the spread of COVID-19, Proceedings of the National Academy of Sciences, 117 (29), 16732-16738. [code]

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[42] H. Sha, M. Al Hasan, P.J. Brantingham, and G. Mohler.  (2020).  Dynamic topic modeling of the COVID-19 Twitter narrative among U.S. governors and cabinet executives.  5th International Workshop on Social Sensing (SocialSens 2020).

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[41] Mohler, G., Bertozzi, A., Carter, J.G., Short, M.B., Sledge, D., Tita, G., Uchida, C. and Brantingham, P.J.  (2020).  Impact of social distancing during COVID-19 pandemic on crime in Los Angeles and Indianapolis.  Journal of Criminal Justice.  68, 2020.

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[40] K. Gray, D. Smolyak, S. Badirli, and G. Mohler.  Coupled IGMM-GANs for deep multimodal anomaly detection in human mobility data.  ACM Transactions on Spatial Algorithms and Systems.  6 (4), 1-14.

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[39] G. Mohler, M. Porter, J.G. Carter and G. LaFree.  Learning to rank spatio-temporal hotspots (journal version).  Crime Science. 9 (1), 1-12. [code]

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2019

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[38] W. Chiang, B. Yuan, H. Li, B. Wang, A. Bertozzi, J. Carter, B. Ray, and G. Mohler.  System for Overdose Spike Early Warning using Drug Mover’s Distance-based Hawkes Processes.  ECML-PKDD Workshop on Data Science for Social Good.  2019.

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[37] G. Mohler, P.J. Brantingham, J. Carter and M.B. Short.  Reducing bias in estimates for the law of crime concentration.  Journal of Quantitative Criminology (2019), DOI: 10.1007/s10940-019-09404-1.

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[36] Stanhope, A., Sha, H., Barman, D., Al Hasan, M., & Mohler, G.  Group Link Prediction. In 2019 IEEE International Conference on Big Data (Big Data) (pp. 3045-3052). IEEE.

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[35] A. Morehead, L. Ogden, G. Magee, R. Hosler, B. White, and G. Mohler.  Low cost gunshot detection using deep learning on the Raspberry Pi.  In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019.

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[34] J. Lu, S. Sridhar, R. Pandey, M. Al Hasan, and G. Mohler,  Investigate Transitions into Drug Addiction through Text Mining of Reddit Data.  Proceedings of 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, (KDD 2019).

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[33] Pandey, S., Chowdhury, N., Raje, R. R., Mohler, G., and Carter, J. Trust estimation of historical social harm events in Indianapolis metro area. In 2019 IEEE International Smart Cities Conference (ISC2 2019).

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2018

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[32] G. Mohler and M. Porter.  Rotational grid, PAI-maximizing crime forecasts.  Statistical Analysis and Data Mining: The ASA Data Science Journal 11.5 (2018): 227-236. [code]

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[31] Y. Cheng, M. Dundar, G. Mohler.  A coupled ETAS-I2GMM point process with applications to fault detection.  Annals of Applied Statistics 12(3), DOI: 10.1214/18-AOAS1134.​

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[30] R. Hosler, X. Liu, J. Carter, A. Ganci, J. Hill, R. Raje, G. Mohler and M. Saper.  RaspBary: Hawkes point process Wasserstein barycenters as a service.  Technical Report.

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[29] R. Vijayan and G. Mohler.  Forecasting retweet count during elections using graph convolution neural networks.  IEEE International Conference on Data Science and Advanced Analytics (DSAA 2018).

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[28] S. Pandey, N. Chowdhury, M. Patil, R. Raje, G. Mohler, and J. Carter.  CDASH: Community data analytics for social harm.  IEEE International Smart Cities Conference (ISC2 2018).   

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[27] R. Pandey and  G. Mohler.  Evaluation of crime topic models: topic coherence vs. spatial concentration.  IEEE International Conference on Intelligence and Security Informatics (ISI 2018). 

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[26] G. Mohler and P.J. Brantingham.  Privacy preserving, crowd sourced crime Hawkes processes.  International Workshop on Social Sensing (SocialSens).  IEEE 2018.

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[25] Brantingham, P.J., M. Valasik, G. Mohler.  Does Predictive Policing Lead to Biased Arrests? Results from a Randomized Controlled Trial.  Statistics and Public Policy. 5(1), 2018.

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[24] G. Mohler, J. Carter, R. Raje.  Improving social harm indices with a modulated Hawkes process.  International Journal of Forecasting, 34 (3), 2018. [code]

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[23] G. Mohler, R. Raje, J. Carter,  M Valasik, and P.J. Brantingham.  A penalized likelihood method for balancing accuracy and fairness in predictive policing.  IEEE International Conference on Systems, Man, and Cybernetics (SMC2018). 

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[22] S. Khorshidi, M. Al Hasan, G. Mohler, and M. Short.  The role of graphlets in viral processes on networks.  Journal of Nonlinear Science, 2018.

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[21] J. Carter, G. Mohler and B. Ray.  Spatial Concentration of Opioid Overdose Deaths in Indianapolis: An Application of the Law of Crime Concentration at Place to a Public Health Epidemic.  Journal of Contemporary Criminal Justice, 35 (2), 2018.

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2017

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[20] G. Mohler, M. Short, P.J. Brantingham.  The concentration-dynamics tradeoff in crime hot spotting.  In Unraveling the Crime-Place Connection: New Directions in Theory and Policy, edited by David Weisburd and John Eck.  2017.

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2016 

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[19] C. Ramaiah, A. Tran, E. Cox, and G. Mohler.  Deep learning for driving detection from mobile phones.  KDD Workshop on Machine learning for large scale transportation systems.  2016.

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2015 

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[18] G. Mohler, M. Short, S. Malinowski, M. Johnson, G. Tita, A. Bertozzi, P.J. Brantingham. Randomized controlled field trials of predictive policing.  Journal of the American Statistical Association.  110 (512).  2015.

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2014

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[17] G. Mohler.  Learning convolution filters for inverse covariance estimation of neural network connectivity.  Advances in Neural Information Processing Systems.  2014.

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[16] J. T. Woodworth, G. Mohler, A. L. Bertozzi and P. J. Brantingham, Nonlocal crime density estimation incorporating housing information, Phil. Trans. Roy. Soc. A, 2014.

 

[15] G. Mohler, Marked point process hotspots maps for homicide and gun crime prediction in Chicago, International Journal of Forecasting, 30, 491, 2014.

 

[14] M. Short, G. Mohler, P. J. Brantingham, and G. Tita, Gang rivalry dynamics via coupled point process networks, Discrete and Continuous Dynamical Systems B, 34, 1459, 2014.

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2013

 

[13] G. Mohler, Discussion of: Estimating the historical and future probabilities of large terrorist events, Annals of Applied Statistics, 7 (4), 1866, 2013.

 

[12] G. Mohler, Modeling and estimation of multi-source clustering in crime and security data, Annals of Applied Statistics, 7 (3), 1525, 2013

 

[11] E. Lewis and G. Mohler, A nonparametric EM algorithm for multiscale Hawkes processes, Technical Report.

 

[10] D. Sledge and G. Mohler, Eliminating malaria in the American South:  An analysis of the decline of malaria in 1930s Alabama, American Journal of Public Health, 103 (8), 1381, 2013.

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2012

 

[9] G. Mohler and M. Short, Geographic profiling from kinetic models of criminal behavior, SIAM J. on Applied Math, 72 (1), 163, 2012.

 

[8] E. Lewis, G. Mohler, P. J. Brantingham, and A. Bertozzi, Self-exciting point process models of civilian deaths in Iraq, Security Journal, 25 (3), 244, 2012.

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2011

 

[7] M. G. Ascenzi, C. Blanco, I Drayer, H. Kim, R. Wilson, K. Retting, K. Lyons, and G. Mohler, Effect of localization, length and orientation of chondrocytic primary cilium on murine growth plate organization, Journal of Theoretical Biology, 285 (1), 147, 2011.

 

[6] G. Mohler, M. Short, P. Brantingham, F. Schoenberg, and G. Tita, Self-exciting point process modeling of crime, Journal of the American Statistical Association, 106 (493), 100, 2011.

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[5] G. Mohler, A. Bertozzi, T. Goldstein and S. Osher, Fast TV Regularization for 2D Maximum Penalized Likelihood Estimation, Journal of Computational and Graphical Statistics, 20 (2), 479, 2011.

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2010

 

[4] L. Smith, M. Keegan, T. Wittman, G. Mohler, and A Bertozzi,  Improving Density Estimation By Incorporating Spatial Information, EURASIP J. on Advances in Signal Processing, Volume 2010, 12 pages.

 

2009

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[3] H. D. Ceniceros, G. H. Fredrickson, and G. O. Mohler, Coupled flow-polymer dynamics via statistical field theory: modeling and computation,  Journal of Computational Physics, 228 (5), 1624, 2009.

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2008

 

[2] E. M. Lennon, G. O. Mohler, H. D. Ceniceros, C. J. Garcia-Cervera, and G. H. Fredrickson,  Numerical solutions of the complex Langevin equations in polymer field theory, Multiscale Modeling and Simulation, 6 (4), 1347, 2008.  

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2007

 

[1] H. D. Ceniceros and G. O. Mohler, A practical splitting method for stiff SDEs with applications  to problems with small noise, Multiscale Modeling and Simulation, 6 (1), 212, 2007.

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Lecture notes

 

Computational modeling of contagion and epidemics. These lecture notes were developed for an undergraduate course on modeling contagion.  They serve as a companion to the book The Rules of Contagion by Adam Kucharski.

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