Electronic Instrumentation (EI), Department of Microelectronics
Expertise: Digital IC design, neuromorphic engineering and spiking neural networks, neuroscience-inspired machine learning, hardware-algorithm co-design.Themes: Autonomous sensor systems, Health and Wellbeing
Charlotte Frenkel received the M.Sc. degree (summa cum laude) in Electromechanical Engineering and the Ph.D. degree in Engineering Science from Université catholique de Louvain (UCLouvain), Louvain-la-Neuve, Belgium, in 2015 and 2020, respectively. In February 2020, she joined the Institute of Neuroinformatics, University of Zurich and ETH Zurich, Switzerland, as a postdoctoral researcher. In July 2022, she started as an assistant professor in the Electronic Instrumentation group at Delft University of Technology.
Her current research aims at bridging the bottom-up (bio-inspired) and top-down (engineering-driven) design approaches toward neuromorphic intelligence, with a focus on digital spiking neural network processor design, embedded machine learning, and on-chip training algorithms. Direct applications cover adaptive internet of things (IoT) devices, autonomous robotic agents, and biosignal processing.
Dr. Frenkel received a best paper award at the IEEE International Symposium on Circuits and Systems (ISCAS) 2020 conference and her Ph.D. thesis was awarded the Nokia Bell Labs Scientific Award 2021, the IBM Innovation Award 2021 and the UCLouvain/ICTEAM Best Thesis Award 2021. In 2023, she received a prestigious AiNed Fellowship Grant from the Dutch Research Council (NWO). She serves as a TPC member for the tinyML Research Symposium 2022, the IEEE European Solid-State Circuits Conference (ESSCIRC) 2022, the ACM/IEEE International Symposium on Low-Power Electronics (ISLPED) 2022, as a member of the neuromorphic systems and architecture technical committee of the IEEE CAS society since 2021, and as a reviewer for various conferences and journals, including the IEEE Trans. on Neural Networks and Learning Syst., IEEE Trans. on Circuits and Syst. I/II, IEEE Trans. on Biomed. Circuits and Syst., Nature, Nature Electronics, and Nature Machine Intelligence. She presented several invited talks, including three keynotes at the tinyML EMEA technical forum 2021, at the Neuro-Inspired Computational Elements (NICE) neuromorphic conference 2021, and at Neuromorphic Computing Netherlands (NCN) 2022. She is the chair of the tinyML initiative on neuromorphic engineering, is a program co-chair of the NICE conference 2023, and serves as an associate editor for the IEEE Transactions on Biomedical Circuits and Systems journal.
Prospective self-motivated BSc/MSc/PhD students are always welcome to send an e-mail to discuss research opportunities in my lab, both for their main academic curriculum and for open curiosity-driven side projects.
For more information on the CogSys research team, please follow this link.
Last updated: 19 Jun 2023
MSc project proposals
-  Sparse spiking neural networks for radar-based human activity recognition
-  Meta-learning for low-cost adaptive neuromorphic hardware
-  Mixed-signal neuromorphic hardware for sub-10µW on-chip online learning at the edge
-  A hardware architecture based on the least-control principle for bio-plausible local online learning
-  Monostable multivibrator networks for low-cost spike-based computing
-  Dendritic circuits in neuromorphic hardware