David Chen - Scientific Advisor at Ambio Life Sciences

David Chen

Scientific Advisor at Ambio Life Sciences

Toronto, Ontario, Canada

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Summary

👤

Senior

🎓

Top School

David Chen is a scientific advisor and entrepreneur with 12 years of experience at the intersection of computing, AI and neuroscience, blending deep academic training (PhD in Neuroscience, University of Toronto) with hands-on startup leadership as co-founder and CTO/CINO of BenchSci. He has applied advanced diffusion MRI analysis and machine learning in both clinical research and product settings, including population tractography studies of trigeminal neuralgia and cranial nerve reconstruction. As an open-source contributor to the prominent dipy medical-imaging library, he refactored and validated a core anisotropic power map function, demonstrating attention to reproducibility and test-driven development. Based in Toronto, he now advises life-sciences ventures while continuing to translate neuroimaging methods into practical AI-driven tools—an unusual mix of deep domain science, software engineering, and product-facing leadership.

12 years of coding experience

14 years of employment as a software developer

Danforth

Doctor of Philosophy (PhD), Neuroscience, Doctor of Philosophy (PhD), Neuroscience at University of Toronto

Hon. BASc, Computer Science, Software Engineering Specialist, Hon. BASc, Computer Science, Software Engineering Specialist at University of Toronto - University College

English, Chinese, Japanese

Github Skills (8)

machine-learning10

diffusion-mri10

python10

neuroimaging10

numpy9

medical-imaging9

denoising8

tractography8

Programming languages (2)

JavaScriptPython

Github contributions (5)

dipy/dipy

Jul 2015 - Sep 2015

DIPY is the paragon 3D/4D+ medical imaging library in Python. Contains generic methods for spatial normalization, signal processing, machine learning, statistical analysis and visualization of medical images. Additionally, it contains specialized methods for computational anatomy including diffusion, perfusion and structural imaging.

Role in this project:

Data Scientist

Contributions:6 commits, 1 PR, 14 comments in 2 months

Contributions summary:David primarily focused on implementing and refining the `sh_to_ap` method, a core function within the `dipy/dipy` library for generating anisotropic power maps. This involved refactoring existing code, correcting errors, adding descriptive documentation, and incorporating the necessary references. The user also added tests for the implemented method, demonstrating a commitment to code quality and validation.

3dimagingmachine-learningpythonsignal-processing

sinkpoint/neuro-scripts

Jan 2014 - Feb 2016

Scripts for processing neuroimages

Contributions:30 commits, 16 pushes, 1 branch in 2 years 1 month

image-processing

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