Hi! I am Thorir, and I am currently a Ph.D. student at ETH Zurich under the supervision of Prof. Dr. Luca Benini. My research interest is applying Robust and Practical Machine Learning approaches, focusing on a bio-signal analysis taking into account the characteristics and constraints of wearable edge devices and IoT units. A use case that I am currently researching is the usage of bio-signals to detect and forecast seizures, where the bio-signals come from wearable devices and are classified with ML and DL algorithms on the same wearable edge devices.
Ph.D. in Electrical Engineering and Information Technology
M.Sc. in Electrical Engineering and Information Technology, 2020
B.Sc. in Electrical and Computer Engineering, 2018
University of Iceland
In this paper, we present implementations of energy-efficient artifact detection algorithms on a parallel ultra-low power platform.
In this paper, we present Clustering-Based REDuction (C-BRED), a new technique to reduce the size of NAS search spaces.
In this paper, we present implementations of seizure detection algorithms on a parallel ultra-low power platform.
In this paper, we propose ECG-TCN, a novel temporal convolutional network (TCN) that achieves outstanding accuracy while requiring few trainable parameters.