Thesis/Project Final Defense Schedule
Join us as the School of STEM master’s degree candidates present their culminating thesis and project work. The schedule is updated throughout the quarter, check back for new defenses.
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Master of Science in Computer Science & Software Engineering
AUTUMN 2025
Thursday, November 20
JAMES TRUONG
Chair: Dr. Kelvin Sung
Candidate: Master of Science in Computer Science & Software Engineering
11:00 A.M.; Join James Truong’s Online Defense
Project: Realistic Illumination in AR
Augmented Reality (AR) creates a unique user experience by superimposing digital content onto real-world environments. With the addition of applying realistic visual cues, AR objects can be further involved and properly integrated into a user’s real-world environment to achieve higher immersion and believability. Unfortunately, accurately applying realistic illumination and spatial conditions remains a technically challenging task. Diverse environments and exposure to real-world phenomena often vary in complexity and visual acuity.
Two primary approaches exist to address this challenge: machine learning and image-based methods. Machine learning based methods can simulate real-world illumination on AR objects by recognizing illumination patterns and features in images, but are reliant on the quality, quantity, and diversity of their training data. In contrast, image-based approaches offer advantages in flexibility and usability where a balance in image quality and hardware performance can still provide a satisfactory result. Unfortunately, image-based approaches remain sensitive to hardware limitations and image quality which can significantly affect the performance and outcome.
This project focuses on the latter, emphasizing the development of a practical and accessible solution for realistic AR lighting on common consumer devices. The implemented system utilizes two mobile devices: one device gathers real-world environmental data, and the other establishes and renders the AR object. This configuration allows realistic illumination of AR objects in near real-time under dynamic indoor lighting conditions. The resulting system provides a practical and accessible solution for integrating digital objects into real indoor environments allowing them to appear consistent with real physical objects in terms of lighting, shadow, and reflection. However, the performance is limited in complex or rapidly changing environments where noise is more prominent.
Friday, November 21
SIQIAO YE
Chair: Dr. Michael Stiber
Candidate: Master of Science in Computer Science & Software Engineering
3:30 P.M.; Join Siqiao Ye’s Online Defense
Project: Incident Classification of Emergency Services Communication System (ESCS)
In the context of next-generation 911 (NG911), Emergency Service Communications Systems (ESCS) are becoming increasingly complex, requiring operators to answer calls and then categorize events based on the content of the conversation. This study explores metadata-based event classification methods and how their performance depends on the spatiotemporal structure of incoming calls, providing operators with an initial pre-classification before answering. We synthesized 911-like datasets using a graph-based clustering point process simulator, built a dual-input Long Short-Term Memory (LSTM) network classifier, and compared it with classic non-sequential baseline models. We generated three spatiotemporal separation modes: temporally overlapping but spatially separable; spatially overlapping but temporally separable; and simultaneously spatiotemporally overlapping. The results reveal clear patterns, especially when spatial cues are ambiguous, where temporal memory becomes crucial, and the LSTM model performs better. On a representative test set, the overall accuracy and macro F1 score typically exceed 85%, demonstrating robust performance and highlighting the necessity and option of sequence modeling in metadata-only ESCS classification.