Madeleine Shang
Co-Founder at Backflip Inc.
Old Toronto, Ontario, United States
Summary
🤩
Rockstar🎓
Top SchoolMadeleine Shang is a Co-Founder and Senior Research Engineer with a decade of experience at the intersection of AI, privacy-preserving ML, and startup building. She leads and contributes to open-source privacy tooling—most notably backend work on OpenMined’s PySyft for federated learning and secure model serving—and has steered data science initiatives for Women Who Code. A serial entrepreneur and mentor affiliated with The Next 36 and Ryerson DMZ, she blends technical depth (JWT/HSA/RSA security, model averaging, server integrations) with product and venture experience from multiple early-stage ventures. Based in Toronto, she is also a Google Women Techmakers ambassador and brings a futurist’s curiosity and hands-on engineering rigor to research-driven productization.
10 years of coding experience
2 years of employment as a software developer
Double Degree- BMath and BBA Mathematical Economics and Finance, Double Degree- BMath and BBA Mathematical Economics and Finance at Wilfrid Laurier UniversityDouble Degree- BMath and BBA, Double Degree- BMath and BBA at University of Waterloo
English, Chinese, French
Github Skills (16)
restful-api10
api-rest10
user-authentication10
rest-api10
authentication10
python10
federated-learning10
back-end-development10
api-design10
flask10
flask-ask10
sym10
jwt10
cryptography9
secure-computation9
Programming languages (7)
Github contributions (5)
OpenMined/PySyft
Feb 2020 - May 2020
Perform data science on data that remains in someone else's server
Role in this project:
Back-end Developer
Back-end DeveloperContributions:9 commits, 4 PRs, 3 pushes in 2 months
Contributions summary:Madeleine contributed to the backend functionality of the PySyft project, focusing on federated learning and model serving. Their commits include the implementation of endpoints for model downloading and reporting, along with authentication mechanisms. They worked on integrating server configuration parameters and averaging model plans. Additionally, the user has experience with security and authentication through JWT implementation (HSA/RSA).
pytorchcryptographyacquiringpythonscience
Prtfw/trainingPipeline_medical_imaging
Apr 2018 - May 2020
deep learning training and image processing pipeline for medical image segmentation (cardio dicoms)
Contributions:17 commits, 2 PRs, 15 pushes in 2 years 1 month
pipelinemedical-imagedeep-learningimage-segmentationprocessing-pipeline
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