John Wilson

Julian R. Rice, Karthik Balaguru, Fadia Ticona Rollano et al.

Storm surge is one of the deadliest hazards posed by tropical cyclones (TCs), yet assessing its current and future risk is difficult due to the phenomenon's rarity and physical complexity. Recent advances in artificial intelligence applications to natural hazard modeling suggest a new avenue for addressing this problem. We utilize a deep learning storm surge model to efficiently estimate coastal surge risk in the United States from 900,000 synthetic TC events, accounting for projected changes in TC behavior and sea levels. The derived historical 100-year surge (the event with a 1% yearly exceedance probability) agrees well with historical observations and other modeling techniques. When coupled with an inundation model, we find that heightened TC intensities and sea levels by the end of the century result in a 50% increase in population at risk. Key findings include markedly heightened risk in Florida, and critical thresholds identified in Georgia and South Carolina.

Adam Wang, Steve Chang, John Wilson

Mobile applications and on-body devices are becoming increasingly ubiquitous tools for physical activity tracking. We propose utilizing a self-tracker's habits to support continuous prediction of whether they will reach their daily step goal, thus enabling a variety of potential persuasive interventions. Our aim is to improve the prediction by leveraging historical data and other qualitative (motivation for using the systems, location, gender) and, quantitative (age) features. We have collected datasets from two activity tracking platforms (Moves and Fitbit) and aim to check if the model we derive from one is generalizable over the other. In the following paper we establish a pipeline for extracting the data and formatting it for modeling. We discuss the approach we took and our findings while selecting the features and classification models for the dataset. We further discuss the notion of generalizability of the model across different types of dataset and the probable inclusion of non standard features to further improve the model's accuracy.