Senior Research Scientist, Samsung Research America
PhD, Computer Engineering, Arizona State University
I build foundation-model-based imaging systems for smartphone cameras, with particular emphasis on real-world single-image super-resolution, hallucination control, feature-conditioned diffusion, and efficient deployment-aware restoration.
At Samsung Research America, I work on diffusion-based and feature-driven models for camera quality enhancement within the Mobile Processor Innovation Lab, including work such as F2IDiff. My doctoral research at Arizona State University focused on atmospheric turbulence mitigation, long-range imaging, and physically grounded simulation, including projects such as DAATSim.
Career
Senior Research Scientist (Computer Vision)
Developing foundation-model-based imaging systems for Samsung smartphone cameras with emphasis on faithful, mobile-friendly super-resolution.
Summer Internship
Applied computer vision and deep learning to degraded long-range video across ground, aerial, handheld, and satellite platforms.
Research Assistant
Research in computational imaging, atmospheric turbulence restoration, astrophotography, and perceptual quality under real-world degradation.
What I Do
Building robust vision systems for degraded, long-range, and real-world imaging scenarios.
Designing and training deep architectures grounded in physics and domain priors.
Advancing camera pipelines from RAW processing to super-resolution and astrophotography.
Applying AI to biomedical sensing, environmental monitoring, and long-range observation.
End-to-end research from novel algorithm design through publication and deployment.
Scaling computation with GPU acceleration, parallelism, and cloud infrastructure.
Research & Projects
Tools & Technologies
Research Output
arXiv 2025
Devendra K. Jangid, Ripon Kumar Saha, Dilshan Godaliyadda, Jing Li, Seok-Jun Lee, and Hamid R. Sheikh
Feature-conditioned diffusion for high-fidelity smartphone super-resolution with stronger control and reduced hallucination.
Pacific Graphics 2025
Ripon Kumar Saha, Yufan Zhang, Jinwei Ye, and Suren Jayasuriya
A depth-aware, physically grounded simulator for spatially varying atmospheric turbulence and temporally coherent rendering.
CVPR 2024
Ripon Kumar Saha, Qin D., Ye J., Li N., and Jayasuriya S.
A dynamic-scene turbulence restoration pipeline that couples segmentation with restoration to preserve motion and detail.
WACV 2025
Ripon Kumar Saha, Mccloskey S, and Jayasuriya S
A multimodal framework that combines meteorological and visual cues to estimate atmospheric image degradation more accurately and robustly.
Thoughts & Stories
Exploring the intersection of optics and AI-based image processing, from DIY pinhole cameras to astrophotography and cutting-edge research
Our breakthrough research on AI-powered dry eye syndrome diagnosis receives coverage in one of Korea's prominent news outlets
Recognition
Arizona State University ECEE
Research Excellence Award ($12,000)
Arizona State University
Winner in Information Sciences (PhD Category)
BuildwithAI Hackathon
Out of 4,000 participants from over 70 countries
