dr. Borbála HunyadiAssistant Professor
Expertise: Biomedical signal processing
|Group:||Circuits and Systems (CAS)|
|Department of Microelectronics|
|Phone:||+31 15 278 1254|
Borbála (Bori) Hunyadi was born in Budapest, Hungary. She received a MSc degree in electrical and computer engineering from the Pazmany Peter Catholic University in 2009. In the same year she joined Stadius, Department of Electrical Engineering at KU Leuven, where she worked in close collaboration with the Laboratory for Epilepsy Research, and she obtained her PhD degree in 2014. She continued working in Stadius as a postdoctoral researcher on the ERC advanced grant Biotensors, and she served as the research lead on the Imec-ICON project SeizeIT. Between February and May 2016 she was a visiting researcher at the University of Oxford, and in May 2017 she visited the Christian Albrechts University of Kiel. In 2018 she was awarded one of the “Delft Technology Fellowships” for outstanding female academic researchers. In October 2018 she joined the Circuits and Systems group at TU Delft as an assistant professor.
Her research interests include biomedical signal processing and machine learning for biomedical pattern recognition. More specifically, she is interested in multimodal signal processing and fusion, blind source separation, tensor decompositions and wearable signal processing to better understand healthy and pathological physiology, in particular brain activity in epilepsy.
EE-BD Biomedical Devices (BD) profile
A profile in our MSc-EE program dedicated to biomedical devices. Biomedical devices are used for medical diagnosis, monitoring and treatment. They can be fixed, portable, wearable, implantable and injectable. They are active and thus embed electronics, computing and software
EE2S31 Signal processing
Digital signal processing; stochastic processes
EE4530 Applied convex optimization
Applied convex optimization: role of convexity in optimization, convex sets and functions, Canonical convex problems (SDP, LP, QP), second-order methods, first-order methods for large-scale problems.
Prostate cancer detection using ultrasound
Tensor techniques to improve the analysis of (3D+time) ultrasound images
Delft Tensor AI Lab
Tensor-based AI methods for biomedical signals
Multimodal, multiresolution brain imaging
Developing a novel brain imaging paradigm combining functional ultrasound and EEG
Medical Delta Cardiac Arrhythmia Lab
Part of a larger program (with Erasmus MC) to unravel and target electropathology related to atrial arrhythmia
Last updated: 16 Aug 2021