Jiahan Xie
PHD Student at University of California, Santa Cruz
City of Ithaca, New York, United States
Summary
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Rockstar🎓
Top SchoolJiahan Xie is a PhD student and machine learning engineer who blends research in multi-agent reinforcement learning for energy system control with hands-on experience building ML compilers and production data pipelines. Trained at Cornell and Zhejiang University, Jiahan has contributed to cross-framework tooling (implementing Array API-compliant elementwise ops for ivy supporting JAX, TensorFlow, PyTorch, and NumPy) and interned on ML compiler stacks at Cerebras. Their background spans academic research on accelerator compiler infrastructure, industry work delivering real-time electricity forecasting pipelines, and teaching roles—demonstrating an ability to move ideas from prototype to deployable systems. Known for bridging ML research and software engineering, Jiahan brings a systems-minded approach to applying learning algorithms in energy and hardware-aware contexts.
4 years of coding experience
4 years of employment as a software developer
Bachelor of Science - BS, Computer Science, Bachelor of Science - BS, Computer Science at Cornell UniversityBachelor of Science - BS, Environmental Engineering Technology/Environmental Technology, Bachelor of Science - BS, Environmental Engineering Technology/Environmental Technology at Zhejiang University
Stackoverflow
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Github Skills (8)
pytorch10
machine-learning10
api10
tensorflow10
python10
numpy10
jax9
deep-learning9
Programming languages (7)
Github contributions (5)
ivy-llc/ivy
Apr 2022 - Aug 2022
Convert Machine Learning Code Between Frameworks
Role in this project:
Backend Developer
Backend DeveloperContributions:25 reviews, 174 commits, 47 PRs in 3 months
Contributions summary:Jiahan contributed to the implementation of elementwise operations, particularly for the pow function. Their work involved adapting the functions to adhere to the Array API standard across different machine learning frameworks, including Jax, Tensorflow, PyTorch, and NumPy. They also made contributions to the backends by providing support for torch and tensorflow. The modifications include data type promotion and the handling of edge cases related to exponentiation, improving the library's compatibility and functionality.
pythontensorflowframework-learningtemplatedata-science
jiahanxie353/circt
Oct 2023 - Mar 2025
Circuit IR Compilers and Tools
Contributions:200 pushes, 115 branches in 1 year 6 months
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