UC Berkeley AI Hackathon
Developed an AI-powered mobile app for clothing recommendations.
Developed an AI-powered mobile app for clothing recommendations.
Research abstract: Regular physical exercise is crucial for health and wellness, but performing exercise with incorrect form can cause injuries. This paper presents a machine learning-based method to classify the quality of squats. Using an iPhone to record exercise, the videos are processed using Google’s Mediapipe, which extracts x, y, z coordinates of body joints. These coordinates are then converted into joint angles, in particular, the knees and hips. A deep neural network analyzes these angles to classify and predict the quality of the squat form, offering a solution to improve exercise safety and effectiveness. 
Given Facebook marketplace data, I used supervised machine learning to predict the prices of cars in the Bay Area, as a function of features such as: mileage, condition, and seller rating. 
Published in Journal 1, 2009
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1).
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Published in Journal 1, 2010
This paper is about the number 2. The number 3 is left for future work.
Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2).
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Published in Journal 1, 2015
This paper is about the number 3. The number 4 is left for future work.
Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3).
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Published in GitHub Journal of Bugs, 2024
This paper is about fixing template issue #693.
Recommended citation: Your Name, You. (2024). "Paper Title Number 3." GitHub Journal of Bugs. 1(3).
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Published:
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Published:
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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