tianqiyang
VA
Summary
👤
SeniorBruce Yang is a senior software engineer with nine years of experience bridging physical sciences and full-stack software development, currently building web applications and ML-driven features at Mastercard Data & Services. Trained as an optics and physical engineer and holding a CS master’s from University of Rochester, he pairs rigorous algorithm and data-structure skills with practical product delivery across React/TypeScript front ends and Python-based deep learning back ends. He has shipped production systems at Amazon and Mastercard, built physics-aware generative models and optical-DNN hybrids in research, and implemented Monte Carlo Tree Search and perception modules for the widely used aima-python repository under Peter Norvig’s mentorship. Comfortable across AWS, CI tooling, and model training, Bruce focuses on turning research-grade neural models into deployable, test-covered components for real-world problems. A detail-oriented problem solver, he brings a rare mix of optics, simulation experience, and hands-on ML engineering to full-stack product work.
9 years of coding experience
Github Skills (20)
python10
machine-learning10
deep-learning10
neural-network10
edge-detection10
reinforcement-learning9
artificial-intelligence9
tensorflow9
keras9
faster-rcnn8
computer-vision8
mask-rcnn8
rnn-model8
n8
algorithm7
Programming languages (3)
Github contributions (5)
aimacode/aima-python
Jun 2019 - Jun 2020
Python implementation of algorithms from Russell And Norvig's "Artificial Intelligence - A Modern Approach"
Role in this project:
ML Engineer
ML EngineerContributions:11 commits, 21 PRs, 1 comment in 1 year
Contributions summary:Tianqiyang implemented and tested Monte Carlo Tree Search (MCTS) algorithms for the game environment. They added code for games specifically designed to work with MCTS and added comments and tests to the implementation. The user also integrated perception and implemented edge detection algorithms, including gradient, Gaussian derivative, and Laplacian edge detectors, to support machine learning tasks. Further, they worked with deep neural networks, including implementation of common loss functions, optimization algorithms (Adam, SGD), and different neural network architectures such as Dense, Conv1D and RNN to enhance the project.
artificial-intelligencepython
bruceyoungsysu/bruceyoungsysu.github.io
Mar 2018 - Aug 2020
personal web page
Contributions:179 commits, 167 pushes, 1 branch in 2 years 6 months
personal-web-pagereactweb-pageweb-personal
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