Avi Shah
Email: avishah704@gmail.com
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Effxcts LLC
About Me
I’m a senior undergraduate student at the University of Florida with a strong focus in computer graphics. I began teaching myself visual effects at 14 using After Effects and Blender, which eventually led to founding Effxcts LLC— a sports technology company with 50M+ views and clients including ESPN, NHL, Red Bull, the Warriors, and more.
In college, my interests expanded to coding, image processing, machine learning, and the broader field of visual computing. In summer of 2024, I interned as a Machine Learning Researcher at the Children’s Hospital of Philadelphia (CHOP), publishing research in machine learning and image processing to predict surgical necessity from kidney scans.
Currently, I am growing Effxcts LLC with new computer graphics projects and am researching at the Indie Lab.
Current Interests: 4D Reconstruction, Pose Estimation, Diffusion Models, Gaussian Splatting
Hobbies: Basketball, Flag Football, Skiing
Effxcts LLC
See more work on our website
Past Projects
4D NBA Reconstruction from Monocular Video (2025)
Developed a semi-automated pipeline to reconstruct NBA plays from a single video input.
Pose Estimation for Automatic Video Creation (2024)
Built a program that automatically creates an animation from a dataset of images and specified 3D pose.
Deep Learning and Image Processing in Medicine (2024)
During my CHOP internship, I developed an image processing pipeline to extract quantitative data from hundreds of kidney scans. I trained and validated machine learning models, including a convolutional neural network with long short-term memory (CNN-LSTM), to predict surgical necessity in affected patients. To facilitate clinical use, I created two web-based tools using REST APIs. The abstract was accepted and presented at the 2025 AUA conference.
Basketball Shot Tracker - ML & Computer Vision (2023)
I developed a real-time basketball shot tracker by training a YOLOv8 deep learning model and implementing data-cleaning algorithms. The project achieved 95% score detection accuracy and 97% shot attempt accuracy.
3D Depth Screen Illusion - Computer Vision (2023)
I created an algorithm that adjusts 2D layers based on real-time eye tracking data to simulate 3D depth on a 2D screen.
NBA Blender 3D Data Visualization (2023)
I developed a GUI to visualize NBA player shooting heat maps using data from the NBA API. I then extended this into a 3D visualization, scripting 500 models to follow physics-based shooting arcs in Blender. This project reached over 4 million views on TikTok and Instagram.
