Summary
Dániel Terbe is a researcher-developer with 10 years of experience at the intersection of physics, computer science and AI, currently working on computational optical sensing at MTA SZTAKI in Budapest. He specializes in machine learning for computer vision and image processing, with applied expertise in remote photoplethysmography (camera-based pulse estimation) and digital holographic microscopy, including real-time implementations for resource-constrained embedded systems and mobile apps. Trained originally as a physicist with an Info-Bionics MSc, he brings a strong quantitative background in statistical inference and biophysical neuron modeling that informs his data-driven approach to imaging problems. Dániel has a track record of translating academic literature into deployable algorithms and prototypes, and frequently bridges experimental optics with deep learning pipelines. Colleagues describe him as comfortable moving "from the external to the internal"—from physical measurement systems to the neural models that interpret them—making him adept at multidisciplinary, application-focused research engineering.
10 years of coding experience
5 years of employment as a software developer
Bachelor of physics, Physics, 4.5, Bachelor of physics, Physics, 4.5 at Eötvös Loránd University
Master's degree, Info-Bionics, Master's degree, Info-Bionics at Pázmány Péter Katolikus Egyetem
English, German