NFL All-22 player labeling
The project was to identify players, formations, and outcome of certain plays
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I am a computer science graduate student with a strong focus on artificial intelligence and over a decade of experience building and operating large-scale production systems. My background includes designing and developing end-to-end machine learning pipelines for domains such as computer vision and signal processing, where I have managed all stages of data collection, preprocessing, feature extraction, and annotation. Notably, I have engineered automated data labeling solutions for projects like NFL play classification using deep learning models (YOLO, EfficientNet) and advanced segmentation techniques, as well as unsupervised anomaly detection in radio spectrograms for SETI research. My expertise spans Python, data pipelines, distributed systems, and cloud-native architectures, and I am adept at leveraging both manual and automated methods to ensure high-quality training data for AI applications. I am passionate about applying my skills to create robust, scalable AI systems and deliver reliable, labeled datasets for real-world machine learning challenges.
The project was to identify players, formations, and outcome of certain plays
Masters of Science, Computer Science
Master of Science, Computer Science
Network Deployment Engineer
Deployment Engineer