Jiyang Wang
Principal Applied Scientist at Oracle
Cambridge, Massachusetts, United States
Summary
👤
Senior🎓
Top SchoolJiyang Wang is a Principal Applied Scientist based in Cambridge, MA with 15 years of experience in data mining and modeling and six years focused on large language models and multimodal foundations. He blends a strong research background—a PhD in condensed matter physics with multiple high-profile publications—with hands-on ML engineering skills in Python, TensorFlow/PyTorch, AWS, Docker/Kubernetes and MLflow, enabling production-grade model development and deployment. At Amazon he worked on AGI-related fine-tuning and delivery services for Amazon Nova and on Alexa AI entity resolution for real-world dialog and edge applications, and he now brings that scale-up expertise to Oracle. His career spans self-driving, telematics, seismic geophysics and e-commerce, reflecting an ability to translate noisy, domain-specific data into robust predictive systems. Colleagues value him for leading small science teams and shipping pragmatic solutions that bridge research and product; less obvious is his track record of turning complex physics experiments into reproducible ML pipelines.
9 years of coding experience
12 years of employment as a software developer
Mianyang High SchoolBachelor of Science (BS) Physics, Bachelor of Science (BS) Physics at Fudan UniversityDoctor of Philosophy (Ph.D.) Condensed Matter Physics, Doctor of Philosophy (Ph.D.) Condensed Matter Physics at The University of Chicago
English, Chinese
Github Skills (1)
probabilistic-graphical-models8
Programming languages (1)
Github contributions (5)
2wavetech/Robotics---Computational-Motion-Planning
Nov 2016 - Aug 2017
Contributions:26 commits, 24 pushes, 1 branch in 9 months
2wavetech/Probabilistic-Graphical-Model
Nov 2016 - Aug 2017
This repository contains my implementation of the programming assignments of Probabilistic Graphical Models delivered by Stanford University on Coursera
Contributions:28 commits, 25 pushes, 1 branch in 9 months
probabilistic-graphical-models
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