Alex Park
Research Analyst at Cubist Systematic Strategies
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolAlex Park is a research-savvy software engineer and analyst with 12 years of experience applying machine learning to real-world systems across finance, hardware, and consumer products. He holds a PhD from MIT and has driven modeling and systems research at Google on wakeword detection and neural speech enhancement for billions of devices. Prior roles at Intel and Cerebras involved low-level ML engineering and performance work—he contributed to the Neon deep-learning framework with GPU activations, RMSprop, and batch-norm speedups—showing a blend of algorithmic depth and production optimization. Earlier in his career he built live quantitative trading systems at Tower Research, giving him rare experience bridging high-frequency decision systems and ML research. Now based in the San Francisco Bay Area at Cubist Systematic Strategies, he combines research rigor with pragmatic engineering to turn models into low-latency, high-reliability deployments. Colleagues would note his curiosity across signal processing, optimization, and hardware-aware ML as a distinguishing, non-obvious strength.
11 years of coding experience
18 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Electrical Engineering and Computer Science, Doctor of Philosophy (Ph.D.) Electrical Engineering and Computer Science at Massachusetts Institute of Technology
Github Skills (14)
algorithm10
machine-learning10
deep-learning10
rms10
batch-normalization10
optimisation10
python10
optimizers10
optimization10
cuda9
gpu-programming9
c-language8
testing8
cprogramming-language8
Programming languages (3)
Github contributions (5)
NervanaSystems/neon
May 2015 - Mar 2017
Intel® Nervana™ reference deep learning framework committed to best performance on all hardware
Role in this project:
Backend Developer & ML Engineer
Backend Developer & ML EngineerContributions:135 commits, 3 PRs, 3 branches in 1 year 10 months
Contributions summary:Alex made significant contributions to the `neon` deep learning framework, focused on improving its functionality and performance. Their work included implementing and refining features for integration testing, such as adding command-line options and adjusting YAML configurations. The user was also involved in adding new activation functions for the GPU backend and incorporating the RMSprop optimization algorithm. They also improved the batch norm computation times.
deep-learningbest-performanceintelmachine-learningperformance
apark263/tensorflow
May 2017 - Jun 2019
Computation using data flow graphs for scalable machine learning
Contributions:3 PRs, 27 pushes, 42 branches in 2 years
computationscalabledata-sciencemachine-learninggraphs
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