Online Archive of University of Virginia Scholarship
The Aggression Pattern: Evaluating Signals from Wearable Devices for Agitation Detection; Beyond the Bottleneck: Data Analytics & Cloud Platforms in Automotive Supply Chain Optimization197 views
Author
Molakalapalli, Govind, School of Engineering and Applied Science, University of Virginia
Advisors
Doryab, Afsaneh, EN-SIE, University of Virginia
Clark, Matthew, EN-Comp Science Dept, University of Virginia
Winkler, Antonia, EN-SIE, University of Virginia
Seabrook, Bryn, EN-Engineering and Society, University of Virginia
Abstract
Data science and analytics is a field that opens doors to critical insights. In my portfolio I approach two novel problems using the same core discipline of data science. Motivating both projects was the fact this discipline has many solutions to complex problems. Gun violence is largely caused by reactionary aggression, and identifying key signals that contribute to such responses is critical to future research, especially in the development of wearable devices with early-warning detection for signs of agitation response. Building a tool that synchronously logs wearable data and response data allowed us to identify critical insights into how an individual may respond to agitating stimuli. Meanwhile, the field of data science technology is making progress to improve complex business systems, such as the automotive supply chain, and identifying current gaps and opportunities to innovate allows for money saved. Therefore, these two projects encapsulate the diverse applicability of systems engineering itself, due to its applications to fields in research-based studies, and in industry optimization.
Degree
BS (Bachelor of Science)
Keywords
Manifestations of Agitation; Physiological Signals; Machine Learning; Automotive Supply Chain; Cloud Tools; AI; Data Science; Data Analysis
Molakalapalli, Govind. The Aggression Pattern: Evaluating Signals from Wearable Devices for Agitation Detection; Beyond the Bottleneck: Data Analytics & Cloud Platforms in Automotive Supply Chain Optimization. University of Virginia, School of Engineering and Applied Science, BS (Bachelor of Science), 2026-04-30, https://doi.org/10.18130/jpmx-h962.
