2025
[88]
Phillips, S., Mohler, G., and Schoenberg, F. Detection of surges of SARS-Cov-2 using nonparametric Hawkes models.
Epidemics (2025).
[87]
Chiang, W. and Mohler, G. Upper confidence bound multi-armed bandits for partially observed Hawkes processes.
International Joint Conference on Neural Networks (IJCNN 2025).
[86]
Manring, I., Wang, H., Mohler, G., and Miscouridou, X. BSTPP: a python package for Bayesian spatiotemporal point processes.
Journal of Applied Statistics (2025).
[85]
Brantingham, P. J., Mohler, G., and Vorobeychik, Y. Calling the police as an interdependent security game.
The Journal of Mathematical Sociology (2025).
[84]
Leverso, J., O'Neill, K. K., Knorre, A., and Mohler, G. The limits of digital liberation: The social locations of gang-affiliated girls and women in the digital streets.
Journal of Criminal Justice (2025).
2024
[83]
Connealy, N., Piza, E. L., Arietti, R., Mohler, G., and Carter, J. G. Staggered deployment of gunshot detection technology in Chicago, IL: a matched quasi-experiment of gun violence outcomes.
Journal of Experimental Criminology (2024).
[82]
Diouane, Y., Schoenberg, F., and Mohler, G. Accurate estimation of cross-excitation in multivariate Hawkes process models of infectious diseases.
11th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2024).
Best paper award (research track).
[81]
Leverso, J., Diouane, Y., and Mohler, G. Measuring Online-Offline Spillover of Gang Violence Using Bivariate Hawkes Processes.
Journal of Quantitative Criminology (2024).
[80]
Piza, E. L., Mohler, G., Connealy, N., Arietti, R. and Carter, J. G. Space-Time Association between Gunshot Detection Alerts, Calls for Service, and Police Enforcement in Chicago: Differences Across Citizen Race and Incident Type.
Journal of Quantitative Criminology (2024).
[79]
Piza, E. L., Hatten, D. N., Mohler, G., Carter, J. G., and Cho, J. Gunshot detection technology effect on gun violence in Kansas City, Missouri: a microsynthetic control evaluation.
Criminology and Public Policy (2024).
2023
[78]
Manring, I., Hill, J., Mohler, G., Brantingham, P.J., Williams, T., and White, B. Low-Cost Gunshot Detection System with Localization for Community Based Violence Interruption.
10th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2023).
[77]
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).
[76]
Piza, E. L., Arietti, R. A., Carter, J. G., and Mohler, G. The effect of gunshot detection technology on evidence collection and case clearance in Kansas City, Missouri.
Journal of Experimental Criminology (2023).
[75]
Pandey, R., Carter, J., Hill, J., and Mohler, G. 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).
[74]
Miscouridou, X., Bhatt, S., Mohler, G., Flaxman, S., and Mishra, S. Cox-Hawkes: doubly stochastic spatiotemporal Poisson processes.
Transactions on Machine Learning Research (2023).
[73]
Mohler, G. and Mateu, J. Second order preserving point process permutations.
Stat (2023).
[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).
[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).
2022
[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).
[69]
Short, M. B. and Mohler, G. A fully Bayesian, logistic regression tracking algorithm for mitigating disparate misclassification.
International Journal of Forecasting (2022).
[68]
Chiang, W.-H., Liu, X., and Mohler, G. Hawkes process modeling of COVID-19 with mobility leading indicators and spatial covariates.
International Journal of Forecasting (2022).
[67]
Chen, B., Shrestha, P., Bertozzi, A. L., Mohler, G., and Schoenberg, F. A Novel Point Process Model for COVID-19: Multivariate Recursive Hawkes Process. In
Predicting Pandemics in a Globally Connected World, Birkhauser-Springer (2022).
[66]
Chiang, W. and Mohler, G. Hawkes process multi-armed bandits for search and rescue.
IEEE International Conference on Machine Learning and Applications (ICMLA 2022).
[65]
Khorshidi, S., Wang, B., and Mohler, G. Adversarial attacks on deep temporal point processes.
IEEE International Conference on Machine Learning and Applications (ICMLA 2022).
[64]
Liu, X., Fang, S., Mohler, G., Carlson, J., and Xiao, Y. Time to event modeling of subreddit transitions to r/SuicideWatch.
IEEE International Conference on Machine Learning and Applications (ICMLA 2022).
[63]
Sledge, D., Thomas, H. F., Hoang, B. L., and Mohler, G. 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).
[62]
Brantingham, P. J., Mohler, G., and MacDonald, J. Changes in Public-Police Cooperation Following the Murder of George Floyd.
PNAS Nexus (2022).
2021
[61]
Mohler, G. and Porter, M. A note on the multiplicative fairness score in the NIJ recidivism forecasting challenge.
Crime Science 10.1 (2021): 1–5.
[60]
Brantingham, P. J., Carter, J., MacDonald, J., Melde, C., and Mohler, G. Is the recent surge in violence in American cities due to contagion?
Journal of Criminal Justice 76 (2021).
[59]
Badirli, S., Akata, Z., Mohler, G., Picard, C., and Dundar, M. Fine-Grained Zero-Shot Learning with DNA as Side Information.
Conference on Neural Information Processing Systems (NeurIPS 2021).
[58]
Sha, H., Al Hasan, M., and Mohler, G. Source detection on networks using spatial temporal graph convolutional networks.
IEEE International Conference on Data Science and Advanced Analytics (DSAA 2021).
[57]
Mohler, G., Mishra, S., Ray, B., Magee, L., Huynh, P., Canada, M., O'Donnell, D., and Flaxman, S. 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.
[56]
Sha, H., Al Hasan, M., and Mohler, G. Group link prediction using Convolutional Variational Autoencoder.
AAAI Conference on Weblogs and Social Media (ICWSM 2021).
[55]
Khorshidi, S., Carter, J., Mohler, G., and Tita, G. Explaining crime diversity with Google street view.
Journal of Quantitative Criminology (2021).
[54]
Brantingham, P. J., Tita, G., and Mohler, G. Gang-related crime in Los Angeles remained stable following COVID-19 social distancing orders.
Criminology and Public Policy (2021).
[53]
Sha, H., Al Hasan, M., and Mohler, G. Learning Network Event Sequences Using Long Short-term Memory and Second-order Statistic Loss.
Statistical Analysis and Data Mining (2021).
[52]
Liu, X., Carter, J., Ray, B., and Mohler, G. Point process modeling of drug overdoses with heterogeneous and missing data.
Annals of Applied Statistics (2021).
[51]
Mohler, G., Short, M. B., Schoenberg, F., and Sledge, D. 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 (2021).
[50]
Carter, J., Mohler, G., Raje, R., Chowdhury, N., and Pandey, S. The Indianapolis Harmspot Policing Experiment.
Journal of Criminal Justice (2021).
2020
[49]
Glober, N., Mohler, G., Huynh, P., Arkins, T., O'Donnell, D., Carter, J., and Ray, B. Impact of COVID-19 Pandemic on Drug Overdoses in Indianapolis.
Journal of Urban Health (2020).
[48]
Khorshidi, S., Carter, J., and Mohler, G. Repurposing recidivism models for forecasting police officer use of force.
IEEE Big Data Workshop on Smart and Connected Communities (2020).
[47]
Sha, H., Al Hasan, M., Carter, J., and Mohler, G. Interpretable Hawkes Process Spatial Crime Forecasting with TV-Regularization.
IEEE Big Data Workshop on Smart and Connected Communities (2020).
[46]
Wong, J., Sha, H., Al Hasan, M., Mohler, G., Becker, S., and Wiltse, C. Automated Corn Ear Height Prediction Using Video-Based Deep Learning.
IEEE BigData Workshop on Smart Farming, Precision Agriculture, and Supply Chain (2020).
[45]
Pandey, R., Brantingham, P. J., Uchida, C., and Mohler, G. Building knowledge graphs of homicide investigation chronologies.
International Workshop on Mining and Learning in the Legal Domain (MLLD-2020).
[44]
Mohler, G., McGrath, E., Buntain, C., and LaFree, G. Hawkes binomial topic model with applications to coupled conflict-Twitter data.
Annals of Applied Statistics (2020).
[43]
Bertozzi, A. L., Franco, E., Mohler, G., Short, M. B., and Sledge, D. The challenges of modeling and forecasting the spread of COVID-19.
Proceedings of the National Academy of Sciences 117 (29), 16732–16738 (2020).
[42]
Sha, H., Al Hasan, M., Brantingham, P. J., and Mohler, G. Dynamic topic modeling of the COVID-19 Twitter narrative among U.S. governors and cabinet executives.
5th International Workshop on Social Sensing (SocialSens 2020).
[41]
Mohler, G., Bertozzi, A., Carter, J. G., Short, M. B., Sledge, D., Tita, G., Uchida, C., and Brantingham, P. J. Impact of social distancing during COVID-19 pandemic on crime in Los Angeles and Indianapolis.
Journal of Criminal Justice 68 (2020).
[40]
Gray, K., Smolyak, D., Badirli, S., and Mohler, G. Coupled IGMM-GANs for deep multimodal anomaly detection in human mobility data.
ACM Transactions on Spatial Algorithms and Systems 6 (4), 1–14 (2020).
[39]
Mohler, G., Porter, M., Carter, J. G., and LaFree, G. Learning to rank spatio-temporal hotspots.
Crime Science 9 (1), 1–12 (2020).
2019
[38]
Chiang, W., Yuan, B., Li, H., Wang, B., Bertozzi, A., Carter, J., Ray, B., and Mohler, G. System for Overdose Spike Early Warning using Drug Mover's Distance-based Hawkes Processes.
ECML-PKDD Workshop on Data Science for Social Good (2019).
[37]
Mohler, G., Brantingham, P. J., Carter, J., and Short, M. B. Reducing bias in estimates for the law of crime concentration.
Journal of Quantitative Criminology (2019).
[36]
Stanhope, A., Sha, H., Barman, D., Al Hasan, M., and Mohler, G. Group Link Prediction.
IEEE International Conference on Big Data (2019).
[35]
Morehead, A., Ogden, L., Magee, G., Hosler, R., White, B., and Mohler, G. Low cost gunshot detection using deep learning on the Raspberry Pi.
IEEE International Conference on Big Data (2019).
[34]
Lu, J., Sridhar, S., Pandey, R., Al Hasan, M., and Mohler, G. Investigate Transitions into Drug Addiction through Text Mining of Reddit Data.
KDD 2019.
[33]
Pandey, S., Chowdhury, N., Raje, R. R., Mohler, G., and Carter, J. Trust estimation of historical social harm events in Indianapolis metro area.
IEEE ISC2 (2019).
2018
[32]
Mohler, G. and Porter, M. Rotational grid, PAI-maximizing crime forecasts.
Statistical Analysis and Data Mining (2018).
[31]
Cheng, Y., Dundar, M., and Mohler, G. A coupled ETAS-I2GMM point process with applications to fault detection.
Annals of Applied Statistics (2018).
[30]
Hosler, R., Liu, X., Carter, J., Ganci, A., Hill, J., Raje, R., Mohler, G., and Saper, M. RaspBary: Hawkes point process Wasserstein barycenters as a service.
Technical Report (2018).
[29]
Vijayan, R. and Mohler, G. Forecasting retweet count during elections using graph convolution neural networks.
IEEE International Conference on Data Science and Advanced Analytics (DSAA 2018).
[28]
Pandey, S., Chowdhury, N., Patil, M., Raje, R., Mohler, G., and Carter, J. CDASH: Community data analytics for social harm.
IEEE ISC2 (2018).
[27]
Pandey, R. and Mohler, G. Evaluation of crime topic models: topic coherence vs. spatial concentration.
IEEE International Symposium on Intelligence and Security Informatics (ISI 2018).
[26]
Mohler, G. and Brantingham, P. J. Privacy preserving, crowd sourced crime Hawkes processes.
SocialSens 2018.
[25]
Brantingham, P. J., Valasik, M., and Mohler, G. Does Predictive Policing Lead to Biased Arrests? Results from a Randomized Controlled Trial.
Statistics and Public Policy (2018).
[24]
Mohler, G., Carter, J., and Raje, R. Improving social harm indices with a modulated Hawkes process.
International Journal of Forecasting (2018).
[23]
Mohler, G., Raje, R., Carter, J., Valasik, M., and Brantingham, P. J. A penalized likelihood method for balancing accuracy and fairness in predictive policing.
IEEE Systems, Man, and Cybernetics (SMC 2018).
[22]
Khorshidi, S., Al Hasan, M., Mohler, G., and Short, M. The role of graphlets in viral processes on networks.
Journal of Nonlinear Science (2018).
[21]
Carter, J., Mohler, G., and Ray, B. Spatial Concentration of Opioid Overdose Deaths in Indianapolis.
Journal of Contemporary Criminal Justice (2018).
2017
[20]
Mohler, G., Short, M., and Brantingham, P. J. The concentration-dynamics tradeoff in crime hot spotting. In
Unraveling the Crime-Place Connection, ed. Weisburd and Eck (2017).
2016
[19]
Ramaiah, C., Tran, A., Cox, E., and Mohler, G. Deep learning for driving detection from mobile phones.
KDD Workshop (2016).
2015
[18]
Mohler, G., Short, M., Malinowski, S., Johnson, M., Tita, G., Bertozzi, A., and Brantingham, P. J. Randomized controlled field trials of predictive policing.
Journal of the American Statistical Association 110 (512) (2015).
2014
[17]
Mohler, G. Learning convolution filters for inverse covariance estimation of neural network connectivity.
NeurIPS 2014.
[16]
Woodworth, J. T., Mohler, G., Bertozzi, A. L., and Brantingham, P. J. Nonlocal crime density estimation incorporating housing information.
Phil. Trans. Roy. Soc. A (2014).
[15]
Mohler, G. Marked point process hotspot maps for homicide and gun crime prediction in Chicago.
International Journal of Forecasting 30, 491 (2014).
[14]
Short, M., Mohler, G., Brantingham, P. J., and Tita, G. Gang rivalry dynamics via coupled point process networks.
Discrete and Continuous Dynamical Systems B 34, 1459 (2014).
2013
[13]
Mohler, G. Discussion of: Estimating the historical and future probabilities of large terrorist events.
Annals of Applied Statistics 7 (4), 1866 (2013).
[12]
Mohler, G. Modeling and estimation of multi-source clustering in crime and security data.
Annals of Applied Statistics 7 (3), 1525 (2013).
[11]
Lewis, E. and Mohler, G. A nonparametric EM algorithm for multiscale Hawkes processes.
Technical Report (2013).
[10]
Sledge, D. and Mohler, G. 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).
2012
[9]
Mohler, G. and Short, M. Geographic profiling from kinetic models of criminal behavior.
SIAM J. on Applied Math 72 (1), 163 (2012).
[8]
Lewis, E., Mohler, G., Brantingham, P. J., and Bertozzi, A. Self-exciting point process models of civilian deaths in Iraq.
Security Journal 25 (3), 244 (2012).
2011
[7]
Ascenzi, M. G., Blanco, C., Drayer, I., Kim, H., Wilson, R., Retting, K., Lyons, K., and Mohler, G. Effect of localization, length and orientation of chondrocytic primary cilium on murine growth plate organization.
Journal of Theoretical Biology 285 (1), 147 (2011).
[6]
Mohler, G., Short, M., Brantingham, P., Schoenberg, F., and Tita, G. Self-exciting point process modeling of crime.
Journal of the American Statistical Association 106 (493), 100 (2011).
[5]
Mohler, G., Bertozzi, A., Goldstein, T., and Osher, S. Fast TV Regularization for 2D Maximum Penalized Likelihood Estimation.
Journal of Computational and Graphical Statistics 20 (2), 479 (2011).
2010
[4]
Smith, L., Keegan, M., Wittman, T., Mohler, G., and Bertozzi, A. Improving Density Estimation By Incorporating Spatial Information.
EURASIP J. on Advances in Signal Processing (2010).
2009
[3]
Ceniceros, H. D., Fredrickson, G. H., and Mohler, G. O. Coupled flow-polymer dynamics via statistical field theory: modeling and computation.
Journal of Computational Physics 228 (5), 1624 (2009).
2008
[2]
Lennon, E. M., Mohler, G. O., Ceniceros, H. D., Garcia-Cervera, C. J., and Fredrickson, G. H. Numerical solutions of the complex Langevin equations in polymer field theory.
Multiscale Modeling and Simulation 6 (4), 1347 (2008).
2007
[1]
Ceniceros, H. D. and Mohler, G. O. A practical splitting method for stiff SDEs with applications to problems with small noise.
Multiscale Modeling and Simulation 6 (1), 212 (2007).