Erik Fredin is a PhD researcher at the University of Toronto with eight years of experience combining medical robotics, machine learning, and real-time control systems. He develops and deploys deep learning models (PyTorch) for 3D pose estimation and control of a magnetic surgical robot, contributing to publications in Science Robotics, ICRA and IROS. His background spans applied autonomy and simulation—C++ controllers for Volvo’s Esmini traffic simulator and ROS-based systems for a driverless Formula Student car—bridging academic rigor with industry-grade software. He has a 4.0 GPA, C$23,000 in scholarships, and a track record of turning research prototypes into reproducible systems used by collaborators. Less obvious: he pairs mechanical-mechatronics intuition from Lund with production-focused tooling and data engineering experience, enabling end-to-end solutions from CAD and MATLAB to deep learning and real-time embedded control.
8 years of coding experience
2 years of employment as a software developer
Doctor of Philosophy - PhD, Medical Robotics and Machine Learning, Doctor of Philosophy - PhD, Medical Robotics and Machine Learning at University of Toronto
Master of Science - MS, Mechanical Engineering specializing in Mechatronics, Master of Science - MS, Mechanical Engineering specializing in Mechatronics at Lund University
Exchange Programme, Exchange Programme at Queen's University
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Erik Fredin - PHD Researcher at University of Toronto