I'm a passionate working at the intersection of physics, computer vision, and perception,
developing efficient and interpretable deep models for image reconstruction and super-resolution.

My current work explores:

• 🌀 Diffusion models and optimal transport (OT) for generative learning
• 🌈 Spectral–spatial transformers for hyperspectral image reconstruction (HSI)
• 🌊 Wavelet-based sparse representations and learnable thresholding
• 🧩 Low-rank approximation and structured attention for efficient architectures
• ⚡ Integration of physical priors into data-driven deep learning
• 🌀 Riemannian Geometry · Geometric analysis of manifolds, curvature, and metric learning in physical and perceptual spaces

• Physics-Inspired Deep Learning
• Diffusion & Optimal Transport for visual perception
• Riemannian Geometry
• Wavelet-Based Neural Architectures
• Sparse & Low-Rank Representations
• Hyperspectral Image Reconstruction (HSI)
• Super-Resolution and Denoising