Justus F. Huebotter

PhD candidate & AI researcher

Curriculum vitae

Education

Work experience

Teaching assistant
Donders Institute, Radboud University, Nijmegen, NL
Teaching assistant
Vrije Universiteit, Amsterdam, NL
Research internship & master thesis
Amsterdam UMC – Neuroscience, Amsterdam, NL
Research assistant & bachelor thesis
German Research Center for AI (DFKI), Bremen, DE
Research internship
National University of Singapore, Institute for Neurotechnology, Singapore

Publications

J. F. Hübotter, A. Urbano, P. Agliati, P. Lanillos, S. Thill, M. van Gerven, and S. Keemink: Spiking Control – A Review. In preparation

J. F. Hübotter and N. Ahmad: Mixed Gradient Descent – A Systematic Reevaluation of Noise-Based Gradient Estimation in Neural Networks. In preparation

J. F. Hübotter, P. Lanillos, S. Thill, and M. van Gerven (2025): Spiking Neural Networks for Continuous Control via End-to-End Model-Based Learning. Under review

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

justus@universe:~$ How does work?

“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)