Wen-ting Wang is a machine learning engineer and collaborative research scholar with over nine years of experience applying deep learning, spatiotemporal modeling, and LLMs to production-scale problems. She has built end-to-end systems—from automated computer vision pipelines for event detection to multi-queue retrieval and large-scale recommender infrastructures—emphasizing computational efficiency and deployability. Her research background (Ph.D. in Statistics) informs novel methodology work on generative models and diffusion enhancements at Academia Sinica, bridging rigorous theory with practical engineering. Wen-ting has led cross-functional teams, taught data science at scale, and grown engineering capability while delivering measurable business impact such as a 50% lift in conversion for a deployed ML product. She is an advocate of turning messy, massive data into intelligent systems and contributes to open-source and model optimization efforts. Based in Taiwan, she combines academic depth with hands-on production experience, often surfacing statistical insights that improve real-world ML reliability.
10 years of coding experience
15 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Statistics, Doctor of Philosophy (Ph.D.), Statistics at National Chiao Tung University
Bachelor’s Degree, Statistics, Bachelor’s Degree, Statistics at National Taipei University
Contributions:55 pushes, 1 branch in 5 years 11 months
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