George Mohler

George Mohler
Boston College, USA | BC · Computer Science Department

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100
Publications
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2,706
Citations

Publications

Publications (100)
Article
We develop a fully Bayesian tracking algorithm with the purpose of providing classification prediction results that are unbiased when applied uniformly to individuals with differing sensitive variable values, e.g., of different races, sexes, etc. Here, we consider bias in the form of group-level differences in false prediction rates between the dif...
Preprint
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Insects represent a large majority of biodiversity on Earth, yet so few species are described. Describing new species typicallyrequires specific taxonomic expertise to identify morphological characters that distinguish it from other known species andDNA-based methods have aided in providing additional evidence of separate species. Machine learning...
Preprint
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Fine-grained zero-shot learning task requires some form of side-information to transfer discriminative information from seen to unseen classes. As manually annotated visual attributes are extremely costly and often impractical to obtain for a large number of classes, in this study we use DNA as side information for the first time for fine-grained z...
Preprint
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Insects represent a large majority of biodiversity on Earth, yet only 20% of the estimated 5.5 million insect species are currently described. While describing new species typically requires specific taxonomic expertise to identify morphological characters that distinguish it from other potential species, DNA-based methods have aided in providing a...
Article
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Firearm violence rates have increased in U.S. cities in 2020 and into 2021. We investigate contagious and non-contagious space-time clustering in shooting events in four U.S. cities (Chicago, Los Angeles, New York and Philadelphia) from 2016-2020. We estimate the dynamic reproduction number (Rt) of shootings, a measure of contagion, using a Hawkes...
Article
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Background: The 2021 NIJ recidivism forecasting challenge asks participants to construct predictive models of recidivism while balancing false positive rates across groups of Black and white individuals through a multiplicative fairness score. We investigate the performance of several models for forecasting 1-year recidivism and optimizing the NIJ...
Article
Hawkes processes are used in statistical modeling for event clustering and causal inference, while they also can be viewed as stochastic versions of popular compartmental models used in epidemiology. Here we show how to develop accurate models of COVID-19 transmission using Hawkes processes with spatial–temporal covariates. We model the conditional...
Article
Full-text available
Recent research has shown an association between monthly law enforcement drug seizure events and accidental drug overdose deaths using cross-sectional data in a single state, whereby increased seizures correlated with more deaths. In this study, we conduct statistical analysis of street-level data on law enforcement drug seizures, along with street...
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Objectives Crime diversity is a measure of the variety of criminal offenses in a local environment, similar to ecological diversity. While crime diversity distributions have been explained via neutral models, to date the environmental and social mechanisms behind crime diversity have not been investigated. Building on recent work demonstrating that...
Conference Paper
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Though link prediction is a well-studied problem with a large body of solutions, existing methods do not handle the case where the predicted link is between an individual and a group. This limitation prevents link prediction models from being directly applicable to many real-life prediction problems where more than two individuals are involved. Exa...
Article
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Purpose This 100-day experiment explored the impact of a dynamic place-based policing strategy on social harm in Indianapolis. Scholars have recently called for place-based policing to consider the co-occurrence of substance abuse and mental health problems that correlate within crime hot spots. Moreover, severity is not ubiquitous across harmful e...
Article
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Research Summary The onset of extreme social distancing measures is expected to have a dramatic impact on crime. Here, we examine the impact of mandated, city‐wide social distancing orders aimed at limiting the spread of COVID‐19 on gang‐related crime in Los Angeles. We hypothesize that the unique subcultural processes surrounding gangs may superse...
Article
Full-text available
Dynamic estimation of the reproduction number of COVID-19 is important for assessing the impact of public health measures on virus transmission. State and local decisions about whether to relax or strengthen mitigation measures are being made in part based on whether the reproduction number, Rt , falls below the self-sustaining value of 1. Employin...
Conference Paper
Full-text available
We review several concepts and modeling techniques from statistical and machine learning that have been developed to forecast recidivism. We show how these methods might be repurposed for forecasting police officer use of force. Using open Chicago police department use-of-force complaint data for illustration, we discuss feature engineering, constr...
Conference Paper
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Analysis and modeling of crime text report data has important applications, including refinement of crime classifications, clustering of documents, and feature extraction for spatiotemporal forecasts. Having better neural network representations of crime text data may facilitate all of these tasks. This paper evaluates the ability of generative adv...
Article
Full-text available
Modeling temporal event sequences on the vertices of a network is an important problem with widespread applications; examples include modeling influences in social networks, preventing crimes by modeling their space–time occurrences, and forecasting earthquakes. Existing solutions for this problem use a parametric approach, whose applicability is l...
Preprint
Full-text available
Opioid overdose rates have increased in the United States over the past decade and reflect a major public health crisis. Modeling and prediction of drug and opioid hotspots, where a high percentage of events fall in a small percentage of space-time, could help better focus limited social and health services. In this work we present a spatial-tempor...
Article
Full-text available
We described the change in drug overdoses during the COVID-19 pandemic in one urban emergency medical services (EMS) system. Data was collected from Marion County, Indiana (Indianapolis), including EMS calls for service (CFS) for suspected overdose, CFS in which naloxone was administered, and fatal overdose data from the County Coroner's Office. Wi...
Article
Full-text available
Predicting the evolution of viral processes on networks is an important problem with applications arising in biology, the social sciences, and the study of the Internet. In existing works, mean-field analysis based upon degree distribution is used for the prediction of viral spreading across networks of different types. However, it has been shown t...
Preprint
Full-text available
We develop a fully Bayesian, logistic tracking algorithm with the purpose of providing classification results that are unbiased when applied uniformly to individuals with differing sensitive variable values. Here, we consider bias in the form of differences in false prediction rates between the different sensitive variable groups. Given that the me...
Preprint
We develop a fully Bayesian, logistic tracking algorithm with the purpose of providing classification results that are unbiased when applied uniformly to individuals with differing sensitive variable values. Here, we consider bias in the form of differences in false prediction rates between the different sensitive variable groups. Given that the me...
Article
Full-text available
Opioid overdose rates have increased in the United States over the past decade and reflect a major public health crisis. Modeling and prediction of drug and opioid hotspots, where a high percentage of events fall in a small percentage of space-time, could help better focus limited social and health services. In this work we present a spatial-tempor...
Article
Full-text available
The coronavirus disease 2019 (COVID-19) pandemic has placed epidemic modeling at the forefront of worldwide public policy making. Nonetheless, modeling and forecasting the spread of COVID-19 remains a challenge. Here, we detail three regional-scale models for forecasting and assessing the course of the pandemic. This work demonstrates the utility o...
Preprint
Full-text available
Hawkes processes are used in machine learning for event clustering and causal inference, while they also can be viewed as stochastic versions of popular compartmental models used in epidemiology. Here we show how to develop accurate models of COVID-19 transmission using Hawkes processes with spatial-temporal covariates. We model the conditional int...
Article
Full-text available
Detecting anomalous activity in human mobility data has a number of applications, including road hazard sensing, telematics-based insurance, and fraud detection in taxi services and ride sharing. In this article, we address two challenges that arise in the study of anomalous human trajectories: (1) a lack of ground truth data on what defines an ano...
Preprint
Full-text available
Dynamic estimation of the reproduction number of COVID-19 is important for assessing the impact of public health measures on virus transmission. State and local decisions about whether to relax or strengthen mitigation measures are being made in part based on whether the reproduction number, R, falls below the self-sustaining value of 1. Using bran...
Article
Full-text available
Governments have implemented social distancing measures to address the ongoing COVID-19 pandemic. The measures include instructions that individuals maintain social distance when in public, school closures, limitations on gatherings and business operations, and instructions to remain at home. Social distancing may have an impact on the volume and d...
Article
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A combination of federal and state-level decision making has shaped the response to COVID-19 in the United States. In this paper we analyze the Twitter narratives around this decision making by applying a dynamic topic model to COVID-19 related tweets by U.S. Governors and Presidential cabinet members. We use a network Hawkes binomial topic model t...
Preprint
Full-text available
A combination of federal and state-level decision making has shaped the response to COVID-19 in the United States. In this paper we analyze the Twitter narratives around this decision making by applying a dynamic topic model to COVID-19 related tweets by U.S. Governors and Presidential cabinet members. We use a network Hawkes binomial topic model t...
Preprint
Full-text available
We present three data driven model-types for COVID-19 with a minimal number of parameters to provide insights into the spread of the disease that may be used for developing policy responses. The first is exponential growth, widely studied in analysis of early-time data. The second is a self-exciting branching process model which includes a delay in...
Preprint
Full-text available
Governments have implemented social distancing measures to address the ongoing COVID-19 pandemic. The measures include instructions that individuals maintain a distance from one another when in public, limitations on gatherings and the operation of businesses, and instructions to remain at home. Social distancing may have a critical impact on the v...
Preprint
Full-text available
We propose a novel framework for integrating Hawkes processes with multi-armed bandit algorithms to solve spatio-temporal event forecasting and detection problems when data may be undersampled or spatially biased. In particular, we introduce an upper confidence bound algorithm using Bayesian spatial Hawkes process estimation for balancing the trade...
Chapter
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Opioid addictions and overdoses have increased across the U.S. and internationally over the past decade. In urban environments, overdoses cluster in space and time, with 50% of overdoses occurring in less than 5% of the city and dozens of calls for emergency medical services being made within a 48-hour period. In this work, we introduce a system fo...
Chapter
In today’s world, with the shifting nature of artificial intelligence (AI) to explainable AI, which involves humans and machines working and complementing each other, there is a need for a mechanism to govern their collaboration. We have proposed a trust-based mechanism to manage collaboration between them. Our trust-based mechanism has the ability...
Article
Full-text available
Background Crime, traffic accidents, terrorist attacks, and other space-time random events are unevenly distributed in space and time. In the case of crime, hotspot and other proactive policing programs aim to focus limited resources at the highest risk crime and social harm hotspots in a city. A crucial step in the implementation of these strategi...
Preprint
Full-text available
We analyze changes in the reproduction number, R, of COVID-19 in response to public health interventions. Our results indicate that public health measures undertaken in China reduced R from 1.5 in January to 0.4 in mid-March 2020. They also suggest, however, the limitations of isolation, quarantine, and large-scale attempts to limit travel. While t...
Conference Paper
Full-text available
Many cities using gunshot detection technology depend on expensive systems that ultimately rely on humans differentiating between gunshots and non-gunshots, such as ShotSpotter. Thus, a scalable gunshot detection system that is low in cost and high in accuracy would be advantageous for a variety of cities across the globe, in that it would favorabl...
Article
Full-text available
Objectives The law of crime concentration states that half of the cumulative crime in a city will occur within approximately 4% of the city’s geography. The law is demonstrated by counting the number of incidents in each of N spatial areas (street segments or grid cells) and then computing a parameter based on the counts, such as a point estimate o...
Conference Paper
Increasing rates of opioid drug abuse and heightened prevalence of online support communities underscore the necessity of employing data mining techniques to better understand drug addiction using these rapidly developing online resources. In this work, we obtained data from Reddit, an online collection of forums, to gather insight into drug use/mi...
Preprint
Full-text available
Increasing rates of opioid drug abuse and heightened prevalence of online support communities underscore the necessity of employing data mining techniques to better understand drug addiction using these rapidly developing online resources. In this work, we obtain data from Reddit, an online collection of forums, to gather insight into drug use/misu...
Article
Full-text available
The law of crime concentration at place has become a criminological axiom and the foundation for one of the strongest evidence-based policing strategies to date. Using longitudinal data from three sources, emergency medical service calls, death toxicology reports from the Marion county (IN) coroner's office, and police crime data, we provide four u...
Preprint
Full-text available
Detecting anomalous activity in human mobility data has a number of applications including road hazard sensing, telem-atic based insurance, and fraud detection in taxi services and ride sharing. In this paper we address two challenges that arise in the study of anomalous human trajectories: 1) a lack of ground truth data on what defines an anomaly...
Article
Full-text available
Epidemic-type aftershock sequence (ETAS) point process is a common model for the occurrence of earthquake events. The ETAS model consists of a stationary background Poisson process modeling spontaneous earthquakes and a triggering kernel representing the space-time-magnitude distribution of aftershocks. Popular non-parametric methods for estimation...
Preprint
Full-text available
We introduce an API for forecasting the intensity of space-time events in urban environments and spatially allocating vehicles during times of peak demand to minimize response time. Our service is applicable to dynamic resource allocation problems that arise in ride sharing, mobile delivery, emergency vehicle placement, etc. We illustrate the servi...
Preprint
Full-text available
Objectives. The law of crime concentration states that a large percentage of crime falls within a small area of a city. The law is demonstrated by counting the number of incidents in each of N spatial areas (street segments or grid cells) and then computing a statistic based on the counts, such as a point estimate on the Lorenz curve or the Gini in...
Conference Paper
Full-text available
Sensor based activity recognition is a critical component of mobile phone based applications aimed at driving detection. Current methodologies consist of hand-engineered features input into discriminative models, and experiments to date have been restricted to small scale studies of O(10) users. Here we show how convolutional neural networks can be...
Conference Paper
Full-text available
A retweet refers to sharing a tweet posted by another user on Twitter and is a primary way information spreads on the Twitter network. Political parties use Twitter extensively as a part of their campaign to promote their presence, announce their propaganda, and at times debating with opponents. In this work we consider the problem of early predict...
Conference Paper
Full-text available
Racial bias of predictive policing algorithms has been the focus of recent research and, in the case of Hawkes processes, feedback loops are possible where biased arrests are amplified through self-excitation, leading to hotspot formation and further arrests of minority populations. In this article we develop a penalized likelihood approach for int...
Conference Paper
Full-text available
Crime, traffic accidents, terrorist attacks, and other space-time random events are unevenly distributed in space and time. In the case of crime, predictive policing algorithms aim to focus limited resources at the highest risk crime hotspots in a city. A crucial step in the implementation of these strategies is the construction of scoring models u...
Conference Paper
Full-text available
Communities are adversely affected by heterogeneous social harm events (e.g., crime, traffic crashes, medical emergencies, drug use) and police, fire, health and social service departments are tasked with mitigating social harm through various types of interventions. Smart cities of the future will need to leverage IoT, data analytics, and governme...
Article
Full-text available
Crime forecasts are sensitive to the spatial discretizations on which they are defined. Furthermore, while the Predictive Accuracy Index (PAI) is a common evaluation metric for crime forecasts, most crime forecasting methods are optimized using maximum likelihood or other smooth optimization techniques. Here we present a novel methodology that join...
Article
Full-text available
Communities are adversely affected by heterogeneous social harm events, e.g. crime, traffic crashes, medical emergencies, and drug use. Police, fire, health and social service departments are tasked with mitigating social harm through various types of interventions. While social harm indices have been proposed for allocating resources to spatially...