Yang Liu is a machine learning researcher anchored in a strong mathematical foundation with nine years of experience bridging academia and industry. He holds a Ph.D. in Computational and Applied Mathematics and specializes in machine learning, deep learning, data analysis, Python, Fortran, and high-performance computing (MPI, OpenMP). At JD Technology, he led federated learning initiatives for smart cities, developing eight core federated algorithms (e.g., Federated Forest, Federated Boosting) and guiding platform design, deployment, and privacy-preserving data sharing. Earlier at Southern Methodist University, he built distributed-memory HPC methods using MPI and Hypre, achieving robust parallel speedups and scalable performance. Based in Beijing's Chaoyang District, Yang combines rigorous research with practical product development and teamwork, and is open to connecting with researchers and engineers who value theory-driven, scalable ML in real-world contexts.
10 years of coding experience
6 years of employment as a software developer
Bachelor's degree, Applied Mathematics, 3.6, Bachelor's degree, Applied Mathematics, 3.6 at The University of Texas at Arlington
Nanodegree, Artificial Intelligence, Nanodegree, Artificial Intelligence at Udacity
Doctor of Philosophy (Ph.D.), Computational and Applied Mathematics, 3.7, Doctor of Philosophy (Ph.D.), Computational and Applied Mathematics, 3.7 at Southern Methodist University
Bachelor's degree, Applied Mathematics, Bachelor's degree, Applied Mathematics at University of Science and Technology Beijing
Contributions:47 commits, 42 pushes, 1 branch in 3 months
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