Computer Science Faculty
George Mohler
Daniel J. Fitzgerald Professor
George Mohler
Ph.D.
Contact
245 Beacon Street 528D
Telephone: 617-552-6905
Email: mohlerg@bc.edu
Education
- B.S., Indiana University - Bloomington
- Ph.D., University of California - Santa Barbara
Research Interests
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 networks using a combination of point processes and neural networks.
Department
Selected Publications
Short, M. B., & Mohler, G. (2022). A Fully Bayesian, Logistic Regression Tracking Algorithm for Mitigating Disparate Misclassification. To appear in International Journal of Forecasting.
Sledge, D., Thomas, H., Hoang, B. and Mohler, G. Impact of Medicaid, Race/Ethnicity, and Criminal Justice Referral on Opioid Use Disorder Treatment. To appear in Journal of the American Academy of Psychiatry and the Law.
Chiang, W. H., Liu, X., & Mohler, G. (2022). Hawkes process modeling of COVID-19 with mobility leading indicators and spatial covariates. International Journal of Forecasting, 38(2), 505-520.
Badirli, S., Akata, Z., Mohler, G., Picard, C., & Dundar, M. M. (2021). Fine-grained zero-shot learning with DNA as side information. Advances in Neural Information Processing Systems, 34, 19352-19362.
Sha, H., Al Hasan, M., & Mohler, G. (2021). Source detection on networks using spatial temporal graph convolutional networks. In 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA) (pp. 1-11). IEEE.
Brantingham, P. J., Carter, J., MacDonald, J., Melde, C., & Mohler, G. (2021). Is the recent surge in violence in American cities due to contagion? Journal of Criminal Justice, 76, 101848.
Mohler, G., & Porter, M. D. (2021). A note on the multiplicative fairness score in the NIJ recidivism forecasting challenge. Crime Science, 10(1), 1-5.
Mohler, G., Mishra, S., Ray, B., Magee, L., Huynh, P., Canada, M., O’Donnell, D. and Flaxman, S. (2021). 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), 20210195.
Sha, H., Al Hasan, M., & Mohler, G. (2021). Group Link Prediction Using Conditional Variational Autoencoder. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 15, pp. 656-667).
Sha, H., Al Hasan, M., & Mohler, G. (2021). Learning network event sequences using long short‐term memory and second‐order statistic loss. Statistical Analysis and Data Mining: The ASA Data Science Journal, 14(1), 61-73.
Bertozzi, A. L., Franco, E., Mohler, G., Short, M. B., & Sledge, D. (2020). The challenges of modeling and forecasting the spread of COVID-19. Proceedings of the National Academy of Sciences, 117(29), 16732-16738.
Mohler, G., Bertozzi, A.L., Carter, J., Short, M.B., Sledge, D., Tita, G.E., Uchida, C.D. 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, 101692.
Mohler, G., McGrath, E., Buntain, C., & LaFree, G. (2020). Hawkes binomial topic model with applications to coupled conflict-Twitter data. The Annals of Applied Statistics, 14(4), 1984-2002.
Mohler, G., Brantingham, P. J., Carter, J., & Short, M. B. (2019). Reducing bias in estimates for the law of crime concentration. Journal of Quantitative Criminology, 35(4), 747-765.
Mohler, G., Raje, R., Carter, J., Valasik, M., & Brantingham, J. (2018). A penalized likelihood method for balancing accuracy and fairness in predictive policing. In 2018 IEEE international conference on systems, man, and cybernetics (SMC) (pp. 2454-2459). IEEE.
Brantingham, P. J., Valasik, M., & Mohler, G. (2018). Does predictive policing lead to biased arrests? Results from a randomized controlled trial. Statistics and Public Policy, 5(1), 1-6.
Mohler, G., & Porter, M. D. (2018). Rotational grid, PAI‐maximizing crime forecasts. Statistical Analysis and Data Mining: The ASA Data Science Journal, 11(5), 227-236.
Mohler, G., Carter, J., & Raje, R. (2018). Improving social harm indices with a modulated Hawkes process. International Journal of Forecasting, 34(3), 431-439.