Xinya L
Quantitative Technologist
Central & Western District, Hong Kong SAR
Summary
👤
Senior🎓
Top SchoolXinya L is a quantitative technologist with nine years of experience building ML-driven systems across healthcare, finance, and customer experience platforms. Trained at Carnegie Mellon in Language Technologies and Statistics & Machine Learning, she has moved models from research to production—applying computer vision and xgboost at CHOP, speech and NLP models (including wav2vec2 and transformer-based approaches) at Voci/Medallia, and quantitative research roles at DRW and Qube. She blends full-stack engineering skills (React frontends, SQL orchestration, Docker, C++, Python, PyTorch/TensorFlow) with rigorous evaluation practices like A/B testing to connect clinicians, researchers, and traders to reliable insights. Notably, her background includes speech likability and low-resource voice synthesis research, reflecting a long-standing interest in practical, data-driven language solutions.
9 years of coding experience
6 years of employment as a software developer
Master of Science - MS Language Technologies, Master of Science - MS Language Technologies at Carnegie Mellon University
Github Skills (6)
spring-boot2
spring2
machine-learning1
nlp1
natural-language-processing1
java1
Programming languages (1)
Github contributions (5)
xinyal/Gan-Speech-Synthesis-Research
Sep 2016 - Sep 2016
Contributions:3 commits, 2 pushes, 1 branch in 1 day
xinyal/nn4nlp-code
Feb 2019 - Feb 2019
Code Samples from Neural Networks for NLP
Contributions:2 PRs, 1 push in 1 day
nlpnatural-language-processingneural-networksmachine-learning
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