Cezanne 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.
10 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 Washington
Master of Science (M.S.), Electrical and Electronics Engineering, Master of Science (M.S.), Electrical and Electronics Engineering at Stanford University
Case studies, examples, and exercises for learning to deploy ML models using AWS SageMaker.
Role in this project:
Data Scientist
Contributions:34 commits, 31 pushes, 1 branch in 27 days
Contributions summary:Cezanne's commits focus on initiating and modifying a Jupyter Notebook file (`Project_Plagiarism_Detection/2_Plagiarism_Feature_Engineering.ipynb`) for a plagiarism detection project. The code changes indicate involvement in feature engineering, cleaning, and preprocessing data from text files for a binary classification task. This involves reading data, and likely implementing methods for comparing text similarity, a key component of plagiarism detection.
Contributions: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.
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