Yannick Assogba
Senior Research Scientist at Apple
Cambridge, Massachusetts, United States
Summary
🤩
Rockstar🎓
Top SchoolYannick Assogba is a Senior Research Scientist based in Cambridge with 15 years of experience building data-driven products at the intersection of design, visualization, and machine learning. He blends hands-on software engineering with user-centered visual design, having shipped production systems at Apple and Google and led data viz projects at Bocoup and IBM. Yannick is an active open-source contributor—he extended the popular Voyager visualization tool to be embeddable as a library and has contributed ML examples and core ops to the TensorFlow.js ecosystem. His background from the MIT Media Lab and work on creative coding platforms like p5.js reflect a rare mix of research rigour, product focus, and artistic sensitivity. Colleagues know him for turning complex data workflows into intuitive interfaces while keeping a strong engineering discipline around testing and state serialization.
15 years of coding experience
17 years of employment as a software developer
SM, MIT Media Lab, SM, MIT Media Lab at Massachusetts Institute of TechnologyBSc, Computer Science, Computation Art, BSc, Computer Science, Computation Art at Concordia University
English, French
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (36)
webgl10
javascript10
canvas10
testing10
tensorflowjs10
typescript10
machine-learning10
p5js10
graphic10
redux10
jscript10
regression10
coding10
filter10
typescript-types10
Programming languages (5)
Github contributions (5)
tensorflow/tfjs-examples
Mar 2018 - Jan 2021
Examples built with TensorFlow.js
Role in this project:
ML Engineer
ML EngineerContributions:1 review, 23 commits, 40 PRs in 2 years 10 months
Contributions summary:Yannick contributed a polynomial regression example utilizing the core API of TensorFlow.js, demonstrating a hands-on approach to model creation. The changes involved adding a new file for the example, implementing the necessary functions for prediction, loss calculation, and training. Furthermore, the user included a UI to visualize the data, initial predictions, and the results after model training, showcasing a comprehensive understanding of machine learning model development and deployment within the TensorFlow.js framework.
tensorflow-jsjavascripttensorflow
tensorflow/tfjs-models
Sep 2018 - Jan 2021
Pretrained models for TensorFlow.js
Role in this project:ML Engineer
ML EngineerContributions:17 reviews, 40 commits, 73 PRs in 2 years 4 months
Contributions summary:Yannick primarily contributed to the TensorFlow.js models repository by updating existing models and dependencies to newer versions of TensorFlow.js, specifically version 1.0 and 3.0. They addressed publishing errors and fixed bugs in the KNN-classifier, ensuring correct tensor disposal. Additionally, the user updated model URLs and configurations for deeplab, and incorporated handpose and facemesh into the CI environment.
javascriptmachine-learningpretrained-modelstensorflowtensorflow-js
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