Search Results for author:
Found 11 papers, 4 papers with code
Time-to-event modeling of subreddits transitions to r/SuicideWatch
Recent data mining research has focused on the analysis of social media text, content and networks to identify suicide ideation online.
Cox-Hawkes: doubly stochastic spatiotemporal Poisson processes
Here we develop a new class of spatiotemporal Hawkes processes that can capture both triggering and clustering behavior and we provide an efficient method for performing inference.
Fine-Grained Zero-Shot Learning with DNA as Side Information
Fine-grained zero-shot learning task requires some form of side-information to transfer discriminative information from seen to unseen classes.
Explaining crime diversity with Google street view
Crime diversity is a measure of the variety of criminal offenses in a local environment, similar to ecological diversity.
Repurposing recidivism models for forecasting police officer use of force
We review several concepts and modeling techniques from statistical and machine learning that have been developed to forecast recidivism.
Assessing GAN-based approaches for generative modeling of crime text reports
Analysis and modeling of crime text report data has important applications, including refinement of crime classifications, clustering of documents, and feature extraction for spatio-temporal forecasts.
Point Process Modeling of Drug Overdoses with Heterogeneous and Missing Data
We apply the algorithm to drug overdose data from Indianapolis, showing that the point process defined on the integrated data outperforms point processes that use only homogeneous EMS (AUC improvement . 72 to . 8) or coroner data (AUC improvement . 81 to . 85). We also investigate the extent to which overdoses are contagious, as a function of the type of overdose, while controlling for exogenous fluctuations in the background rate that might also contribute to clustering.
Hawkes Process Multi-armed Bandits for Disaster Search and Rescue
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.
Redditors in Recovery: Text Mining Reddit to Investigate Transitions into Drug Addiction
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.
Coupled IGMM-GANs for deep multimodal anomaly detection in human mobility data
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 and 2) the dependence of existing methods on significant pre-processing and feature engineering.
Learning convolution filters for inverse covariance estimation of neural network connectivity
In this paper we show how inverse covariance estimation can be dramatically improved using a simple convolution filter prior to applying sample covariance.
