Renwen Cui
Computer Vision Engineer @ Aemass, Inc.
About
I am analytical and dedicated individual with hands-on experience in optical engineering, machine learning and signal processing. I am capable of researching and designing color image processing, computer vision, video quality assessment, and cutting-edge AI-powered computational photography algorithms as well as performing characterization, analysis, modeling, and simulation of imaging systems. Skilled in developing encoder-decoder models with skip-connections, performing image augmentation, implementing error metrics and computed models, eliminating gaussian noise from noisy fluorescent video, and devising ultrafast optical imaging systems. In-depth knowledge of machine learning concepts and algorithms, image quality tuning, color imaging, and experiment process documentation. I tackle life and career management scenarios with meticulous strategies based on my expertise in the following areas: ♦ Denoising & Deblurring ♦ Monocular Depth Model ♦ Optical Flow Processes ♦ Image Quality & Processing ♦ Computational Optics & Imaging ♦ Computer Vision ♦ Digital Signal Processing ♦ Artificial Neural Network ♦ Ultrafast Optical Imaging System ♦ New Imaging Experimental Layout ♦ Lens Distortion & Binocular Camera Calibration Connect with me today to find out how I’ll make your mission my mission, to help bring ALL of your business objectives into focus!
United States
San Diego
Computer Software
Python (Programming Language), MATLAB, C++, C (Programming Language), Machine Learning, PyTorch, TensorFlow, Image Quality & Processing, Computer Vision, Artificial Neural Networks, Ultrafast Optical Imaging System , Optimization, Monocular Depth Model , Denoising & Deblurring
Experience

Project Lead (Monocular Depth Estimation)
Madison, Wisconsin, United States
● Developed encoder-decoder model with skip-connections using Utilized PyTorch and TensorFlow for depth estimation given single RGB image, which enhanced accuracy to 94%. ● Increased estimated accuracy by designing and implementing SSIM loss instead of pixel-wise L1 loss. ● Leveraged color inpainting algorithm and performed image augmentation for resolving 60K+ images for indoor and outdoor scenes with invalid pixel values. ● Assessed and managed monocular depth model performance by utilizing error metrics and computed model complexity and computation time for training.

Project Lead (Ultrafast Optical Imaging System)
Wuhan, Hubei, China
● Built and executed ultrafast optical imaging system with high temporal resolution of 48fs and sensitivity of 10-5. ● Predicted intensity decay of imaging system through the development and launch of regression model. ● Created and implemented new experimental layout to optimize SNR of ultrafast optical imaging system.
Renwen Cui's Contact Information
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