Student Talks
Artificial Intelligence in Support of Emergency Care in the Field
Abstract: Medical emergencies demand rapid and accurate interventions to save lives. Severe injuries often require surgical care within the first 60 minutes when timely action significantly improves survival rates. However, limited resources, remote locations, and unpredictable conditions often obstruct access to advanced medical care during this critical period. This thesis focuses on developing a medical [...]
Efficient Synthetic Data Generation and Utilization for Action Recognition and Universal Avatar Generation
Abstract: Human-centered computer vision technology relies heavily on large, diverse datasets, but collecting data from human subjects is time-consuming, labor-intensive, and raises privacy concerns. To address these challenges, researchers are increasingly using synthetic data to augment real-world datasets. This thesis explores efficient methods for generating and utilizing synthetic data to train human-based computer vision models. [...]
Communication-Efficient Active Reconstruction using Self-Organizing Gaussian Mixture Models
Abstract: For the multi-robot active reconstruction task, this thesis proposes using Gaussian mixture models (GMMs) as the map representation that enables multiple downstream tasks: high-fidelity static scene reconstruction, communication-efficient map sharing, and safe informative planning. A new method called Self-Organizing Gaussian mixture modeling (SOGMM) is proposed that estimates the model complexity (i.e., number of Gaussian [...]