Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
Using spatiotemporal analysis to identify potential sex trafficking victims in commercial sex advertisements
Nickolas Kirk Freeman, Shailesh Divey, Greg Bott, Burcu Keskin
The University of Alabama, United States of America
Counter-trafficking efforts are often conducted with a local scope. However, sex traffickers commonly move individuals geographically. Thus, there is a need for organizations in different geographical regions to collaborate in counter-trafficking efforts. This research leverages a large dataset of commercial sex ads and techniques from the areas of machine learning and network science to identify prominent geographical circuits and individuals operating on these circuits.
Breaking the vicious cycle of reincarceration: placement optimization with an MDP approach
Xiaoquan Gao, Pengyi Shi, Nan Kong
Purdue University, United States of America
Community corrections provide alternatives for incarcerations, which can reduce jail overcrowding and recidivism rate, particularly for individuals with substance use disorder. We study the placement decisions for community corrections and relevant capacity planning via an MDP model and prove structural properties for policy insights. To address the complex dependence between optimal placement and system congestion, we leverage a two-timescale approach to develop algorithmic solutions.
Reducing air pollution through machine learning
Leonard Boussioux, Boussioux Bertsimas, Cynthia Cynthia
MIT
This paper presents a data-driven approach to mitigate industrial plant air pollution on nearby cities by linking operational decisions with weather conditions. Our method combines predictive and prescriptive machine learning models to forecast short-term wind patterns and recommend production adjustments. Implemented at a chemical plant in Morocco, our algorithm improves weather forecasts by 40-50% and offers valuable trade-offs, reducing emissions by 33% and costs by 63%.
