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George Mohler

Daniel J. Fitzgerald Professor

Department of Computer Science

Boston College

Contact: mohlerg [AT] bc [DOT] edu

Google Scholar Profile

My research focuses on statistical and deep learning approaches to solving problems in spatial, urban and network data science.  Several current projects include modeling and causal inference for overdose and social harm event data, fairness and interpretability in criminal justice forecasting, and modeling viral processes and link formation on social networks.  


Ph.D. in Mathematics

University of California Santa Barbara



M.A. in Mathematics

University of California Santa Barbara


B.S. in Mathematics (Highest Distinction)

Indiana University Bloomington


Academic Employment

Daniel J. Fitzgerald Professor

Boston College

Department of Computer Science 



Indiana University - Purdue University Indianapolis

Department of Computer and Information Science 



Indiana University - Purdue University Indianapolis​

Institute for Mathematical Modeling and Computational Science


Associate Professor

Indiana University - Purdue University Indianapolis

Department of Computer and Information Science 



Assistant Professor

Santa Clara University

Department of Mathematics and Computer Science 



CAM Assistant Adjunct Professor

University of California, Los Angeles 

Department of Mathematics


Postdoctoral Fellows

Youness Diouane

Alex Knorre

Graduate Students

Ritika Pandey, PhD IUPUI 2023 (Postdoc at BC Social Work)

Xueying Liu, PhD IUPUI 2022 (Postdoc at St Jude)

Wen-Hao Chiang, PhD IUPUI 2022 (Amazon)

Samira Khorshidi, PhD IUPUI 2022 (Apple)

Hao Sha, PhD IUPUI 2021 (Career Builder)

Bo Peng, MS IUPUI 2019 (PhD student at Ohio State)

Raghavendran Vijayan, MS IUPUI 2018 (Palo Alto Networks)

Professional Service

Associate Editor, Journal of Quantitative Criminology, 2022-present

Associate Editor, International Journal of Forecasting, 2020-present

Member, ASA committee on law and justice statistics, 2023-present

Workshop Co-Chair, CIKM 2022

Program Co-Chair, SocialSens 2021

General Chair, IEEE Big Data Workshop on Data Science for Smart and Connected Communities 2020

Program Committee Chair, IEEE BigData Special Symposium: NSF REU Research in Data Science, Systems, and Security 2018, 2019

Grants and Awards

AFOSR MURI grant FA9550-22-1-0380, Learning Dynamics and Detecting Causal Pathways in Coupled Online-Offline Systems, $1,212,914 (Boston College portion). Joint w/ J. Brantingham, E. Hartman, H. Lu, F. Mortstatter, and N. Rodriguez.

NSF grant SCC-2125319, SCC-IRG Track 2: Independent Real-Time Sensing Data to Support Community Well-Being,

$1,422,463.  2021-2024.  Joint w/ J. Brantingham (PI), E. Hartman and J. Hill.

CDC grant R01CE003362, Examining the iatrogenic effect of law enforcement disruptions to the illicit drug market on overdose in the surrounding community, $1,087,500.  9/2021-8/2024. Joint w/ Brad Ray (PI), Jennifer Carroll, Erin Comartin, Steven Korzeniewski, Grant Victor and Brandon del Pozo.

NSF grant ATD-2124313, ATD: Collaborative Research: Multi-task, Multi-Scale Point Processes for Modeling Infectious Disease Threats, $149,992. 2021-2024.

NIJ Recidivism forecasting challenge, 1st, 3rd and 2nd place in three reducing bias categories of the competition.  Joint w/ M. Porter (team PASDA), $30,000.  2021.

IUPUI AI Institute Seed Grant, Leveraging Artificial Intelligence and Machine Learning to Reduce Disparities in Suicide Trajectories: A BioPsychosocial Approach to Identify Modifiable Risk and Protective Processes.  Joint w/ Y. Xiao, J. Carlson, and S. Fang, $25,000.  2021-2022.

IU Racial Justice Research Fund, High-stakes pairing systems for mitigating police bias and misconduct.  Joint w/ J. Carter and J. Hill.  $14,915. 2020-2021.  

NIJ grant 2019-R2-CX-0004,  The Impact of Gunshot Detection Technology on Gun Violence in Kansas City and Chicago: A Multi-Pronged Synthetic Control Evaluation.  Joint w/ E. Piza (PI) and J. Carter, $503,305.  2020-2021.

NSF grant SCC-1737585, SCC-IRG Track 2: Real-Time Algorithms and Software Systems for Heterogeneous Data Driven Policing of Social Harm, joint with J. Carter and R. Raje, $791,513. 9/2017- 8/2020.


NSF grant ATD-1737996, ATD: Collaborative Research: Point Process Algorithms for Threat Detection from Heterogeneous Human Mobility and Activity Data, $100,000.  9/2017- 8/2020.


NSF grant REU-1659488, REU Site: Data Science of Risk and Human Activity, joint with co-PI M. al Hasan, $287,377.  3/2017- 2/2020.

NIJ Real-time crime forecasting challenge.  First place in nine categories of large business division.  Joint w/ M. Porter, $135,000.  2017.


NSF grant SES-1343123, INSPIRE: Computational modeling of grievances and political instability through global media, joint with LaFree (PI), Cunningham, Golbeck, and Torrens, $2,594,533.  9/2014- 8/2017.


NSF grant DMS-0968309, FRG: Collaborative Research: Mathematics of large scale urban crime, joint with A. Bertozzi (PI), G. Tita, J. Brantingham, M. Short, L. Chayes, and F. Schoenberg, $1,008,105.  9/2010-8/2013.

Publications and Preprints 


[79Piza, 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).


[78] 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 Analytic
s (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] 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).

[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).

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

[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).


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

[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).

[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).

[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.

[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.

[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.

[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

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


[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]

[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.

[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. 

[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.

[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.

[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.

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

[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.

[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.

[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.  

[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.

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


[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.

[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.

[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.

[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.

[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).

[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]

[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]

[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).

[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.

[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.

[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]


[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.

[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.

[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.

[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.

[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).

[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).


[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]

[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.​

[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.

[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).

[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).   

[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). 

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

[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.

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

[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). 

[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.

[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.


[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.


[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.


[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.


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

[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.



[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.



[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.



[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.

[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.



[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.



[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.



[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.  



[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.

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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