Friedemann Zenke, PhD
Axel Laborieux, PhD — Post-doc (since 2021)
PhD in Neuromorphic Computing at Paris-Saclay University.
I am interested in understanding the link between cortical circuit motifs and their associated functional roles for efficient information processing in spiking neural networks.
Mattias Nilsson, PhD — Post-doc (since 2023)
Ph.D. in Neuromorphic Computing and M.Sc. in Engineering Physics from Luleå University of Technology, Sweden. I am interested in spiking neural networks and their application on emerging neuromorphic hardware for efficient computation, such as for edge AI applications.
Peter Buttaroni — PhD student (since 2022)
MSc in Quantitative Finance at Luiss Guido Carli (IT) and MSc in AI from the Università della Svizzera Italiana (CH). I am interested in Reinforcement Learning algorithms and how biological principles can be used to improve artificial neural networks.
Julia Gygax— PhD student (since 2022)
BSc in Electrical Engineering and Information Technology, ETHZ. MSc in Biomedical Engineering from ETHZ. I am interested in how concepts of biological neural networks can be applied to artificial neural networks and in training of spiking neural networks.
Manu Srinath Halvagal — PhD student (since 2020)
M.Sc, Microengineering with a minor in Computational Neuroscience, EPFL and B.Tech, Electrical Engineering, IIT Madras. I am interested in the mechanisms of unsupervised and self-supervised perceptual learning in networks of biological neurons, and the role of top-down connections therein. I am also interested in exploring whether we could use these principles to improve machine learning algorithms.
Ashena Gorgan Mohammadi — PhD student (since 2023)
MSc in Computer Science from University of Tehran. My main interest is to develop bio-plausible intelligent systems in broad terms. More specifically, I am interested in how we can simulate predictive and continual learning in a spiking model of columnar architecture of the neocortex.
Julian Rossbroich — PhD student (since 2020)
Joint MSc in Neuroscience from Université Laval and Université de Bordeaux. My main interest concerns the neuronal signatures of learning and memory. Specifically, I am interested in how cortical circuits compute predictions of sensory inputs, and how such predictive processing is involved in learning and plasticity.
Jeremias Seitz — PhD student (since 2022)
MSc in Mechanical Engineering with a focus on Robotics and Control Engineering, ETHZ. My interest lies in optimization and control theory. I apply these ideas to learning strategies in neural networks.
Tengjun Liu — Visitor (starting 2023)
B.Sc. in Physics, Umeå University, Sweden, and B.Eng. in Electronic Science and Technology, Shenzhen University, China. Currently pursuing a Ph.D. in Biomedical Engineering at Zhejiang University, China. My primary interest lies in neuroAI, specifically, how neuroscience can inspire better AI and how AI can help in developing better neural engineering solutions and understanding neuroscience.
Collaborators and co-advised students
Claire Meissner-Bernard, Post-doc
with Rainer Friedrich, FMI.
Simon Narduzzi, PhD student
co-advised by Shih-Chii Liu, Institute of Neuroinformatics (UZH/ETHZ) and L. Andrea Dunbar, CSEM.
MSc in computer science and computational neuroscience (EPFL). My research focuses on developing efficient neural networks for ultra-low power neuromorphic hardware.
Former students and collaborators
- Bastian Eichenberger, PhD, FMI, PhD student (2020–2023), co-advised with Jeff Chao and Guillaume Diss
- Manvi Agarwal (2021–2022)
- Sebastian Billaudelle, PhD, University of Heidelberg, PhD student (2021–2022), co-advised with Johannes Schemmel
- Benjamin Cramer, PhD, University of Heidelberg, PhD student (2019–2022), co-advised with Johannes Schemmel
- Yue “Kris” Wu (2019–2022)
- Tianlin Liu (2019–2020)
- Siegfried Schwartz (2022)
- Peter Buttaroni (2022)
- Nikolaos Papanikolaou (2021–2022)
- Guillermo Martin Sanchez (2021–2022)
- Julia Gygax (2021)
- Matthias Depoortere (2020)