I am a PhD student in the Computer Science Department at the University of Maryland, College Park. I am working with Prof. Yiannis Aloimonos and Cornelia Fermüller at Perception and Robotics Group. I also work closely with Prof. Christopher Metzler. During my master’s study I worked with Prof. Pratap Tokekar on using Reinforcement Learning in Multi-agent System research.
My research focuses on Video and Image Generative Models, Computational Photography, and 3D Vision for Robotics.
This work studies controlled video generation by composing content from multiple reference images. We propose a novel and efficient formulation that leverages the in-context capabilities of pre-trained video generation models.
Diffusion-based Image Layer Decomposition with a unified token-to-token model. Work done during my summer internship at Amazon Prime Video team.
In this work, we adapt pre-trained video diffusion models trained on internet-scale datasets to solve the specialized real-world video task of event-based video interpolation.
ConVRT is an efficient INR framework for video-based turbulence mitigation that operates in test-time optimization manner
We leverage radiance fields to imagine different human views to find the best drone pose for aerial cinematography.
Inspired by microsaccades, we designed an event-based perception system capable of simultaneously maintaining low reaction time and stable texture.
CodedEvents is a novel method for optimal point-spread-function engineering for 3D-tracking with event cameras.
We present a self-supervised occupancy prediction technique, ProxMaP, to predict the occupancy within the proximity of the robot to enable faster navigation.
We present a Multi-Agent Reinforcement Learning (MARL) algorithm for the Visibility-based Persistent Monitoring (VPM) problem.
Template from Keunhong Park