Summary
Alireza Zaeemzadeh is an RF machine learning engineer with a decade of experience applying advanced ML and signal-processing research to real-world systems, currently building real-time RF classification pipelines and edge deployments at Dedrone. He holds a PhD in Electrical Engineering and has moved between theory and practice—publishing novel information-theoretic and optimization results in academia while shipping embedded deep-learning models on NVIDIA Jetson for high-sampling-rate IQ data. His background spans embodied AI, ROS-based robotics, OOD detection, and provably convergent distributed optimization, giving him a rare blend of theoretical rigor and production-focused engineering. Notably, he cut exponential causal-computation costs to linear time in dynamical systems analysis and has a track record of reducing model size and inference costs without sacrificing accuracy. Based in the Washington DC–Baltimore area, he excels at end-to-end ML pipelines from data collection and auto-annotation to model augmentation and edge deployment.
10 years of coding experience
12 years of employment as a software developer
Bachelor's degree, Electrical, Electronics and Communications Engineering, Bachelor's degree, Electrical, Electronics and Communications Engineering at University of Tehran
Doctor of Philosophy - PhD, Electrical Engineering, Doctor of Philosophy - PhD, Electrical Engineering at University of Central Florida
English, Persian