Systems Researcher with 5+ years of experience in IoT, low-power wireless communication, and embedded machine learning.
I like to design embedded AI/ML and TinyML solutions for constrained platforms using Tensorflow Lite for Microcontrollers and CMSIS-NN.
I also build distributed algorithms and network protocols for IEEE 802.15.4, Bluetooth Low Energy and Mesh.
Proficient in Python, C, Tensorflow, and embedded operating systems (Zephyr RTOS, Contiki-NG).
Team-oriented, I supervised 10+ theses and projects during my PhD.
Erasmus alumnus, I studied and worked in four countries.
I will defend my PhD in September 2022, looking for my next challenges in embedded AI/ML, IoT, or edge computing.
How can we adaptively control the latency-accuracy trade-off on embedded devices with < 100 MHz CPU and < 500 KB RAM?
Environment detection and classification on embedded devices solely from Bluetooth Low Energy advertisement packets.
Paper accepted at DAC 2022.
Adaptive channel management for Bluetooth Low Energy and Zephyr OS
Paper under submission
Embedded reinforcement learning and self-learned transmission policies to avoid interference in low-power IoT
Published at IEEE ICDCS 2021
[Code],
[Paper],
[Video]
Decentralized coordination for connected cars at intersections
Published at the ITCVT Workshop at NOMS 2020
[Code],
[Paper]
Fault-tolerant, network-wide consensus protocol for low-power IoT
Published at EWSN 2019, Best paper runner-up
[Code],
[Paper],
[Slides]