Visiting PHD Student at Baylor College of Medicine
Menlo Park, California, United States
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Summary
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Senior
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Top School
Zhuokun Ding is a computational neuroscientist and founding machine learning research scientist with 8 years of experience applying ML, statistical modeling, and scalable systems to understand and emulate neural circuits. He builds and validates brain “digital twins” using state-of-the-art deep architectures and distributed pipelines, and has driven novel GLMM-based inference that links neuronal tuning to anatomical connectivity. His work spans academic (Nature first-author publication, SfN/COSYNE presentations) and industry impact—designing LLM adaptation pipelines at Amazon that let compact models outperform much larger baselines on domain tasks. Based in Menlo Park, he combines hands-on engineering (Kubernetes/GPU clusters, MySQL, Python/R) with theoretical insight, and has a knack for translating biologically inspired principles into robust AI systems. An indicator of his experimental rigor is a twofold increase in neuronal tuning extraction yield from his validation frameworks, highlighting emphasis on reproducibility as well as performance.
8 years of coding experience
Bachelor's degree Biology/Biological Sciences General, Bachelor's degree Biology/Biological Sciences General at Fudan University
Doctor of Philosophy - PhD Neuroscience, Doctor of Philosophy - PhD Neuroscience at Baylor College of Medicine
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Zhuokun Ding - Visiting PHD Student at Baylor College of Medicine