Researches

3D Reconstruction and Neural Rendering Many real-world tasks require understanding scenes in 3D from sparse or unconstrained inputs. I work on reconstructing 3D scenes using COLMAP for camera pose estimation, followed by neural rendering methods such as NeRF and Gaussian Splatting for high-fidelity reconstruction and view synthesis.

Diffusion-Based Image Restoration Real-world imaging systems introduce complex degradations that standard diffusion models are not designed to handle. My work focuses on developing diffusion-based restoration methods that remain reliable under realistic sensor conditions, including spatially correlated noise, low resolution, and thermal imaging artifacts.

Robust Multimodal Learning Multimodal systems often struggle when one or more input sources are missing, corrupted, or imbalanced. My work develops learning strategies that preserve performance under these real-world conditions by enabling models to reason effectively with incomplete information.

Research My research spans computer vision, NLP, multimodal learning, and 3D reconstruction, with a focus on building reliable ML systems that operate robustly under real-world conditions. I develop diffusion-based methods for image restoration that handle real sensor noise, low-quality inputs, and cross-domain degradations.