Cezanne Camacho
AI & Machine Learning at Apple
United States
Summary
🤩
Rockstar🎓
Top SchoolCezanne Camacho is an AI and machine learning specialist with a decade of experience building educational ML and CV programs, currently developing ML courses at Apple. She previously led curriculum for Udacity’s Deep Learning and Computer Vision nanodegrees and has hands-on experience implementing projects from plagiarism detection to facial keypoint and SLAM exercises. With an M.S. from Stanford in electrical engineering and a background spanning synthetic biology, biomedical imaging, and teaching, she blends rigorous research foundations with practical pedagogy. Her GitHub contributions emphasize reproducible, notebook-driven learning artifacts and real-world feature engineering for classification and vision tasks. Passionate about inclusive STEM education, she pairs technical depth with a knack for turning complex topics into approachable learning experiences.
11 years of coding experience
4 years of employment as a software developer
Bachelor of Science (BS), Electrical and Electronics Engineering, Bachelor of Science (BS), Electrical and Electronics Engineering at University of WashingtonMaster of Science (M.S.), Electrical and Electronics Engineering, Master of Science (M.S.), Electrical and Electronics Engineering at Stanford University
Github Skills (22)
pytorch10
robotics10
python10
image-processing10
machine-learning10
feature-engineering10
3d-mapping10
data-preprocessing10
mask-rcnn10
data-loading10
computer-vision10
faster-rcnn10
jupyter-notebook10
slam10
data-structure9
Programming languages (3)
Github contributions (5)
udacity/CVND_Exercises
Apr 2018 - May 2018
Exercise notebooks for CVND.
Role in this project:
Data Scientist
Data ScientistContributions:57 commits, 51 pushes, 1 branch in 1 month
Contributions summary:Cezanne's commits primarily involve exercise notebooks related to computer vision and neural networks. They are contributing to notebooks focused on image representation, edge detection, Fourier transforms, contour detection, and K-means clustering, utilizing libraries like OpenCV and Matplotlib. Their work demonstrates a focus on understanding and implementing image processing techniques and applying these concepts to computer vision tasks. The commits show an engagement with introductory-level content, which suggests an educational or learning context.
pytorchcomputer-vision-nanodegreecvndcomputer-visionnotebooks
udacity/P1_Facial_Keypoints
Mar 2018 - Jul 2018
First project for CVND: facial keypoint detection.
Role in this project:ML Engineer
ML EngineerContributions:56 commits, 43 pushes, 1 branch in 3 months
Contributions summary:Cezanne primarily contributed to the data loading code within the facial keypoint detection project. Their work involved modifications to the dataset loading process, likely to improve data preparation for training. This suggests the user's focus was on the initial steps of the machine learning pipeline, ensuring that the model receives the necessary data for learning facial keypoints. The changes are focused on the architecture of the neural network for facial keypoint detection.
keypoint
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