PhD Thesis Proposal
PhD Student
Robotics Institute,
Carnegie Mellon University

Spatial Reasoning and Semantic Representations for Intelligent Multi-Robot Exploration and Navigation

NSH 4305

Abstract: Autonomous robot exploration is widely applied in areas such as search and rescue, environmental monitoring, and structural inspection. Multi-robot exploration has garnered significant attention in the robotics research community, as it enables faster task completion and greater coverage than a single robot can achieve. However, it presents unique challenges: behavior coordination is complex, communication [...]

PhD Thesis Proposal
PhD Student
Robotics Institute,
Carnegie Mellon University

Leveraging Sense of Agency to Improve the Experience of Control Over Assistive Robots

GHC 6121

Abstract: In an age of autonomous driving and robotics, we are increasingly engaging with robots that deploy autonomous assistance. Cognitive science and human-computer interaction literature tells us that, when we apply autonomy in assistive settings, we are often augmenting the user's sense of agency over the system. Sense of agency is a phenomenon from cognitive [...]

PhD Thesis Proposal
PhD Student
Robotics Institute,
Carnegie Mellon University

Artificial Intelligence in Support of Emergency Care in the Field

GHC 6115

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 [...]

PhD Thesis Proposal
PhD Student
Robotics Institute,
Carnegie Mellon University

Efficient Synthetic Data Generation and Utilization for Action Recognition and Universal Avatar Generation

NSH 3305

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. [...]