Bridging the gap between
I'm a Master's student in CS at CU Boulder, advised by Prof. Bradley Hayes and Prof. Alessandro Roncone. I build Vision-Language frameworks that align human gaze to enable adaptive robotic task planning in shared autonomy settings.
Previously a Research Assistant at IISc Bangalore (Prof. Pradipta Biswas), where I developed gaze-tracking systems for automotive HUDs and applied Inverse RL for human intent prediction. Published at ACM THRI · ACM IUI · IEEE ICRA.
Explores how Inverse Reinforcement Learning can improve system understanding of human intent during rapid task execution in both virtual and collaborative robotic settings.
Evaluates efficiency of object detection computer vision pipelines on limited datasets for deploying mixed-reality manual assembly assistants on head-mounted displays.
Combines explicit gaze cues with implicit behavioral signals to improve prediction accuracy and enable intuitive collaboration in rapid pick-and-place tasks.
Demonstrates how learning from human demonstrations via Deep Inverse Reinforcement Learning enhances robot understanding of human preferences.
Explores generative adversarial networks to synthesize artificial data bridging the reality gap and addressing scarcity in domains underrepresenting certain scenarios.
Fuses contextual augmentations with computer vision inferences to provide interactive spatial anchoring guides during complex industrial manual assembly workflows.
Evaluates how appearance-based gaze tracking can enable safer and more intuitive control of HUD elements while minimizing driver distraction.
When I'm not writing papers or debugging ROS nodes, I spend my time exploring soundscapes, live music, and the Rocky Mountains.
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