Justus F. Huebotter
Curriculum vitae
Education
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June 2021 – present
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Sep 2019 – July 2021
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Sep 2017 – Sep 2019
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Oct 2013 – Sep 2017
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Sep 2005 – Jun 2013
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PhD Artificial Intelligence
Donders Institute, Radboud University, Nijmegen, Netherlands -
MSc Artificial Intelligence
Vrije Universiteit, Amsterdam, Netherlands -
MSc Neuroscience
Vrije Universiteit, Amsterdam, Netherlands -
BSc Biomimicry
City University of Applied Sciences, Bremen, Germany -
High School
Ökumenisches Gymnasium, Bremen, Germany
Work experience
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June 2021 – present
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Apr 2020 – June 2021
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Nov 2019 – Feb 2020
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Feb 2018 – Aug 2018
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Feb 2017 – Aug 2017
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Sep 2015 – Jan 2016
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Teacher’s assistant
Donders Institute, Radboud University, Nijmegen, Netherlands -
Teacher’s assistant
Vrije Universiteit, Amsterdam, Netherlands -
Student assistant
Vrije Universiteit, Amsterdam, Netherlands -
Research internship & master thesis
Amsterdam UMC – Neuroscience, Amsterdam, Netherlands -
Student assistant & bachelor thesis
German Research Center for AI (DFKI), Bremen, Germany -
Research internship
Institute for Neurotechnology, National University of Singapore
Publications
J. F. Hübotter, M. van Gerven, S. Thill, and N. Ahmad: Revisiting Noise Perturbation and Decorrelation Learning in Spiking Neural Networks. In preparation
J. F. Hübotter, P. Lanillos, S. Thill, and M. van Gerven: Training Spiking Neural Networks for Continuous Control with Surrogate Gradients. In preparation
J. F. Hübotter, S. Thill, M. van Gerven, and P. Lanillos (2023): Learning Policies for Continuous Control via Transition Models. Communications in Computer and Information Science (CCIS, volume 1721)
J. F. Hübotter, P. Lanillos, and J. M. Tomczak (2021): Training Deep Spiking Auto-encoders without Bursting or Dying Neurons through Regularization. Preprint
J. F. Hübotter, T. Maaiveld, and S. Wijtsma (2020): Evolutionary Generation of Music with Geometry. Preprint
F. Sorgini, R. Ghosh, J. F. Hübotter, R. Caliò, C. Galassi, C. M. Oddo, and S. L. Kukreja (2016): Design and preliminary evaluation of haptic devices for upper limb stimulation and integration within a virtual reality cave. 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob)
Talks & Posters
J. F. Hübotter, P. Lanillos, S. Thill, and M. van Gerven (2023): Training Spiking Neural Networks for Continuous Control with Surrogate Gradients. Poster @ Neuro-AI-Talks, Talk @ SNUFA
J. F. Hübotter, S. Thill, M. van Gerven, and P. Lanillos (2022): Learning Policies for Continuous Control via Transition Models. Poster @ International Workshop on Active Inference
J. F. Hübotter, P. Lanillos, and J. M. Tomczak (2020): Training Deep Spiking Auto-encoders without Bursting or Dying Neurons through Regularization. Poster @ Donders Poster Session
J. F. Hübotter, J. Tung, N. V. Thakor, H. Yu, and S. L. Kukreja (2016): A Novel High Density Haptic Glove for Vibrotactile Feedback. Poster @ IEEE Life Sciences Grand Challenges Conference (LSGCC)
News
Research talk at SNUFA 2023
I have been researching spiking neural networks for continuous control problems for the past two years of my PhD…
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Audiovisual performance at Kunstnacht 2023
Over the last few months, I had the great opportunity to work together with the fantastic electronic music artists…
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We are in the 2022 AI Song Contest Finale
Together with some colleagues from the Donders Institute AI department and singer-songwriter Maya Shanti, we participated in the 2022…
Read More“If a technological feat is possible, man will do it. Almost as if it’s wired into the core of our being.”
– Major Motoko Kusanagi – Ghost in the Shell (1995)