Feng Wang
Algorithm Engineer at TuSimple
Beijing, China
Summary
👤
Senior🎓
Top SchoolFeng Wang is an algorithm engineer at TuSimple with 12 years of experience building machine learning and computer vision systems, grounded in a PhD from the University of Electronic Science and Technology and a visiting stint at Johns Hopkins. He combines research rigor with production focus—publishing work indexed on Google Scholar while contributing practical fixes to core open-source projects like dmlc-core to improve Windows compatibility. Feng has hands-on expertise in face verification and feature modeling, achieving strong results (e.g., 96.8% on LFW) through implementations such as Joint Bayesian and careful dataset engineering. Based in Beijing, he bridges academic methods and engineering realities to ship robust ML components for autonomous driving and vision applications.
12 years of coding experience
Johns Hopkins University
Github Skills (13)
windows10
compatibility10
computer-vision10
machine-learning10
c-language10
cprogramming-language10
python10
concurrency10
face-recognition10
datasets10
data-set10
svm9
matlab8
Programming languages (7)
Github contributions (5)
happynear/FaceVerification
Jun 2015 - Jan 2018
An Experimental Implementation of Face Verification, 96.8% on LFW.
Role in this project:
Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:72 commits, 66 pushes, 1 branch in 2 years 7 months
Contributions summary:Feng appears to be working on a face verification project, with primary focus on implementing and refining face verification algorithms. Their contributions include implementing the Joint Bayesian method for feature extraction and comparison, and creating scripts for generating and evaluating training and testing datasets. Further commits involve integrating and testing pre-trained models, implying a focus on applying machine learning techniques to achieve the face verification task.
face-verificationface-recognitionlfwbiometricsface-detection
dmlc/dmlc-core
Sep 2015 - Oct 2015
A common bricks library for building scalable and portable distributed machine learning.
Role in this project:Backend Developer
Backend DeveloperContributions:5 commits, 2 PRs, 2 comments in 11 days
Contributions summary:Feng focused on improving the codebase's Windows compatibility. Their commits addressed compiler issues, specifically related to `vs2015 vsnprintf` conflicts and the use of `std::atomic_flag`. They also made minor code style improvements related to indentation. The changes primarily involved adjustments to the `include/dmlc` header files, demonstrating their work in core library functionality.
scalablebricksmachine-learningportabledistributed-machine-learning
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.