Summary
Ahmad Amirivojdan is an applied machine learning scientist and PhD candidate in Biosystems Engineering with over a decade of hands-on experience building computer vision, IoT, and embedded systems for agriculture and industrial automation. He has designed end-to-end research prototypes—customizing Mask R-CNN and U-Net for poultry weight/posture and flock mobility estimation, and creating a low-cost, real-time feed intake monitor using piezo sensor audio and VGG16-based event classification. Prior roles span full-stack product delivery (contributing to a horse-racing betting platform with 70k users and $32M annual revenue) to industrial R&D where he automated production feeders and implemented PLC control. Ahmad combines rigorous dataset curation and annotation practices with deployment-minded engineering, using tools like CVAT, Label-Studio, and Weights & Biases to bridge research and production. He brings uncommon breadth: from autonomous humanoid robotics and IMU firmware to cloud-backed data pipelines and real-world sensor systems. Based in Knoxville, he focuses on practical ML solutions that lower cost and scale monitoring in commercial agricultural settings.
10 years of coding experience
10 years of employment as a software developer
Master's degree, Computer Engineering - Artificial Intelligence, Master's degree, Computer Engineering - Artificial Intelligence at Islamic Azad University
Doctor of Philosophy - PhD, Biological/Biosystems Engineering, Doctor of Philosophy - PhD, Biological/Biosystems Engineering at University of Tennessee, Knoxville
English, French, Persian, Kurdish