Robert Guthrie
Software Engineer at Citadel Securities
Chicago, Illinois, United States
Summary
🤩
Rockstar🎓
Top SchoolRobert Guthrie is a software engineer based in Chicago with 11 years of experience building low-latency, performance-critical systems, currently focusing on options trading at Citadel Securities. A Georgia Tech double major in Computer Science and Applied Mathematics, he combines deep modern C++ expertise with a strong mathematical foundation to optimize numerical and trading code. His background spans hand-optimized SIMD and assembly work (NEON, AVX) for libraries like Torch7 and Yeppp!, delivering measurable speedups in tensor and vector operations. He also brings research-grade experience in NLP from Georgia Tech, co-authoring EMNLP papers and developing Bi-LSTM CRF models and PyTorch tutorials used in coursework. Equally comfortable in production finance systems and academic ML, he has a track record of translating low-level optimization into practical gains for high-throughput workloads.
11 years of coding experience
3 years of employment as a software developer
Bachelor of Science (BS), Computer Science and Applied Mathematics (Double Major), Bachelor of Science (BS), Computer Science and Applied Mathematics (Double Major) at Georgia Institute of Technology
Spanish
Github Skills (11)
c1710
pytorch10
lstm10
nlp10
c1110
crf10
deeplearning-ai9
neural-network9
deep-learning9
performance-tuning9
user-manual8
Programming languages (3)
Github contributions (5)
rguthrie3/DeepLearningForNLPInPytorch
Mar 2017 - Nov 2017
An IPython Notebook tutorial on deep learning for natural language processing, including structure prediction.
Role in this project:
ML Engineer
ML EngineerContributions:32 commits, 28 pushes, 1 branch in 8 months
Contributions summary:Robert contributed significantly to the development of deep learning models for Natural Language Processing, specifically focusing on implementing a Bi-LSTM CRF. They added code examples demonstrating various concepts, including bag-of-words classifiers, sequence models with LSTMs, and the forward and Viterbi algorithms for CRF. Their work focused on building and explaining Pytorch models, with efforts on code commenting and documentation.
nlppytorchpredictiondeep-learninglstm
torch/torch7
Aug 2016 - Sep 2016
http://torch.ch
Role in this project:Back-end Developer
Back-end DeveloperContributions:5 commits, 1 PR, 7 comments in 1 month
Contributions summary:Robert focused on optimizing the performance of numerical operations within the Torch7 library. Their contributions involved implementing and refining low-level routines using assembly language (NEON for ARM and AVX for x86) to accelerate tensor operations like add, mul, and scale. The user also established a dynamic dispatch framework to choose the most performant implementation based on the host CPU's capabilities, improving the library's overall efficiency. These changes directly impact the speed of computationally intensive tasks within Torch7.
libtorchtorchc-plus-plustensorautograd
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