Sr. Software Engineer (Machine Learning/Computer Vision) at National Institutes of Health
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Passionate computer science and machine learning specialist. Started my career focusing on the engineering side of computer vision. Nowadays, I split my time between research and engineering to build deep-learning powered e-health systems. • Practical knowledge and skills in machine learning and software development. • Adequate experience in medical image analysis. • Programming with Python (Keras, PyTorch, Pandas), Java, Matlab, and C/C++. • Strong communication skills and good team player.
University of Missouri-Columbia
Master of Science - MS, Electrical and Computer Engineering
January 1, 2012 – January 1, 2015
Henan University of Technology
Bachelor of Science - BS, Electrical, Electronics and Communications Engineering
January 1, 2008 – January 1, 2012
The National Institutes of Health
Sr. Software Engineer - Computer Vision
December 1, 2020 – Present
The National Institutes of Health
Image Processing Engineer
December 1, 2015 – November 1, 2020
CIVA lab
Research Assistant
August 1, 2014 – November 1, 2015
Columbia, Missouri Area
Mobile Image Analysis for Plant Phenotyping
February 1, 2015 – Present
I developed a fully-automated mobile system for plant phenotyping. The system takes a leaf image as input, and measures the length of the major veins by tracking their trajectories.
Robot solving Rubik's Cube
December 1, 2013 – February 1, 2014
Two Puma 260 robots cooperate under Linux system to solve a scrambled Rubik's cube
Android Application
August 1, 2013 – November 1, 2013
Gyroscope, accelerometer, and magnetic field sensor were combined in this app to measure the orientation of the cell phone
Neural Networks and Deep Learning
DeepLearning.AI
June 24, 2026 – Present
Convolutional Neural Networks
DeepLearning.AI
June 24, 2026 – Present
Supervised Machine Learning: Regression and Classification
DeepLearning.AI, Stanford University
June 24, 2026 – Present
Advanced Learning Algorithms
DeepLearning.AI, Stanford University
June 24, 2026 – Present
Generative AI with Large Language Models
DeepLearning.AI, Amazon Web Services
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's experience is heavily skewed towards computer vision, image processing, and machine learning, primarily in research and development settings. While these skills are valuable, the target role is 'Data Analyst'. The projects and professional experience do not explicitly demonstrate core data analysis skills such as advanced SQL, data warehousing, business intelligence tools, statistical analysis beyond ML models, or dashboarding. This indicates a potential mismatch with the typical responsibilities of a Data Analyst role, which often focuses on extracting insights from structured data for business decision-making rather than developing ML models for image analysis. The certifications in deep learning are relevant to AI/ML but less directly to traditional data analysis.
Soft Skills & Operational Fit
The candidate's experience in R&D and deploying research code to production indicates a practical, results-oriented approach. Involvement in field studies suggests adaptability and problem-solving skills in real-world scenarios. The project descriptions, while brief, imply an ability to work on complex, multi-faceted technical challenges.