Tim Harris
Member Of Technical Staff at Varsity Publications Ltd
Cambridge, England, United Kingdom
Summary
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Rockstar🎓
Top SchoolTim Harris is a seasoned systems and performance engineer with over six years of industry experience and a long academic pedigree from the University of Cambridge (PhD). He blends deep research expertise in parallel programming, transactional memory, and runtime systems with hands-on production work at companies including AWS, Microsoft, and now OpenAI. Tim has led architecture and research teams, contributed influential papers and patents, and applied that expertise to real-world scale problems such as S3 performance and ONNX Runtime thread-pool optimization. He is also an affiliated lecturer at Cambridge and a director of Varsity Publications, reflecting a commitment to teaching and community service alongside engineering. Notably, his open-source contributions to the widely used microsoft/onnxruntime focus on subtle thread-synchronization and scheduling improvements that yield measurable inferencing performance gains. Comfortable moving between low-level concurrency primitives and cloud-scale storage architectures, he turns research insights into pragmatic, high-performance systems.
5 years of coding experience
21 years of employment as a software developer
Devonport High School for BoysPhD Computer Science, PhD Computer Science at University of Cambridge
Github Skills (11)
c-language10
eigen10
onnx10
cprogramming-language10
performance-optimization10
multithreading10
concurrency10
hardware-acceleration9
deep-learning8
machine-learning8
neural-network8
Programming languages (3)
Github contributions (5)
microsoft/onnxruntime
Jun 2020 - Aug 2021
ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
Role in this project:
Back-end Developer & Performance Engineer
Back-end Developer & Performance EngineerContributions:91 reviews, 87 commits, 44 PRs in 1 year 1 month
Contributions summary:Tim primarily focused on improving the performance of the ONNX Runtime's thread pool. Their work included implementing OpenMP-like synchronization patterns in the Eigen thread pool, which involved changes to work distribution and thread management. They also addressed compiler warnings and added tests for burst scheduling and thread pool creation/destruction to identify and fix potential issues. Furthermore, they refined the thread pool API and optimized barrier usage for better performance in parallel sections.
runtimetrainingtensorflowai-frameworkaccelerator
Nifury/onnxruntime
Oct 2020 - Nov 2020
ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
Contributions:26 commits in 20 days
pytorchdeep-learningruntimemachine-learningonnx
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