Haichuan Zhang

Biography

I am a computer vision and imaging scientist with a Ph.D. in Electrical Engineering from Penn State. I combine imaging physics, optimization, and deep learning to build interpretable, efficient methods for image restoration and sensor-data recovery. My doctoral advisor was Prof. Vishal Monga at the Information Processing and Algorithms Laboratory (iPAL).

My industry experience spans event-based tracking and on-chip vision at OMNIVISION, event-guided motion deblurring at Dolby Laboratories, and diffusion-based fiber-sensor restoration at NEC Laboratories America. At Penn State, my research includes convergent unrolled networks, phase-informed deblurring, ultrasound phase correction, and NTIRE dehazing and bokeh challenge solutions. I work across mathematical modeling, model implementation, and experimental evaluation under practical sensing and compute constraints.

Earlier at Xi’an Jiaotong University, I developed computational pathology software and renal cancer analysis models, including a whole-slide image annotation framework used in the AIPath Dataset. This work led to a U.S. patent on collaborative pathology annotation, granted in 2022.

Expertise
Image restoration · Computational imaging · Event-based vision · Optimization & algorithm unrolling · Generative models · Medical image analysis
Core tools: Python · PyTorch · C++ · OpenCV · MATLAB