About
I am an expert in computational neuroscience, machine learning, computer science, and neuromorphic computing. Generally, my interests are in asynchronous and distributed systems, particularly in uncovering and extracting their core algorithms and data structures based on first principles, to understand how non-linear dynamics can foster stable computations, and to advance the mathematical understanding of how diverse systems -- from brain regions to computing networks -- manage information. To this end, I analyze neural population dynamics, emergent phenomena, self-organization, plasticity, and collective behaviors using a variety of tools from computer science, machine and deep learning, data science, mathematics, and statistics.
Research & Work
Currently, I explore neural representations and behaviors of rodents and the camouflage patterns of cuttlefish. I also study the emergence of computational complexity and spatio-temporal languages in evolutionary distributed systems. In past research, I focused specifically on neural computations that are critical for spatial navigation and perception, and on computer vision. I have also successfully applied my knowledge to practical robotics, leading the development of a perception framework for manipulation robotics and a publicly available sim2real rendering pipeline. My scientific and industrial contributions are documented in academic publications and patents.
In addition to my research, I teach, co-organize summer schools, and have initiated a progressive mentoring program that introduces reverse mentoring to executive leadership. I acquired expert knowledge in a multitude of programming languages, and technical and theoretical frameworks, e.g. C, C++, python, pytorch, to name just a few and those that I use most frequently at the moment. My technical proficiencies extend to distributed high-performance computing, GPGPU programming, database systems and SQL, and computer graphics, each significantly contributing to both the theoretical and practical aspects of my work.
Bio
I am a postdoc in Benjamin Dunn's (Neural) Data Science group at the Department of Mathematical Sciences in beautiful Trondheim, Norway, and also work with Yasser Roudi from King's College, London. Before, I spent almost three years as research scientist at the Bosch Center for Artificial Intelligence, obtained a PhD in Computer Science in Neural and Neuromorphic Computing while working in the Neuroscientific System Theory (NST) group at TU Munich, Germany, a Diploma in Computer Science from Ulm University, Germany, and became a fully-qualified professional software developer at Celos Computer GmbH, Germany.
Please contact me if you are interested in a detailed bio.
List of Publications
2024- R. Dietrich, N. Waniek, M. Stemmler, A. Knoll. Grid Codes versus Multi-Scale, Multi-Field Place Codes for Space. Front. Comput. Neurosci., 19 April 2024. DOI: 10.3389/fncom.2024.1276292
- Preprint: R. Dietrich, N. Waniek, M. Stemmler, A. Knoll. Grid Codes versus Multi-Scale, Multi-Field Place Codes for Space. 2023. DOI: 10.1101/2023.06.18.545252
- Preprint: N. Waniek. An introduction to the Transition Scale-Space model for grid cells. 2023.
- A. Kupcsik, M. Spies, A. Klein, M. Todescato, N. Waniek, P. Schillinger, M. Buerger. Supervised Training of Dense Object Nets Using Optimal Descriptors for Industrial Robotic Applications. AAAI 2021.
- L. Rozo , M. Guo , A. G. Kupcsik , M. Todescato, P. Schillinger, M. Giftthaler, M. Ochs, M. Spies, N. Waniek, P. Kesper, M. Burger. Learning and Sequencing of Object-Centric Manipulation Skills for Industrial Tasks. IROS'2020.
- N. Waniek. Transition scale-spaces: A computational theory for the discretized entorhinal cortex. Neural Computation 32(2):330-394, Feb 2020,. MIT Press. DOI: 10.1162/neco_a_01255.
- Leonel Rozo, Andras Kupcsik, Meng Guo, Marco Todescato, Philipp Schillinger, Nicolai Waniek, Markus Giftthaler, Mathias Bürger. Fast Learning and Sequencing of Object-centric Manipulation Skills. R:SS 2019, June 22 2019, Freiburg.
- M. Spies, M. Todescato, H. Becker, P. Kesper, N. Waniek, M. Guo. Bounded Suboptimal Search with Learned Heuristics for Multi-Agent Systems. Proceedings of the AAAI Conference on Artificial Intelligence, Volume 33, pages 2387-2394, 2019. DOI: 10.1609/aaai.v33i01.33012387
- N. Waniek. Locally distributed spatial navigation in a scale-space model for grid cells. PhD thesis, 2018.
- N. Waniek. Hexagonal Grid Fields Optimally Encode Transitions in Spatiotemporal Sequences. Neural Compututation 30(10):2691-2725, Oct 2018. MIT Press. DOI: 10.1162/neco_a_01122.
- Z. Tayeb, N. Waniek, J. Fedjaev, L. Rychly, N. Ghaboosi, C. Widderich, C. Richter, J. Braun, M. Saveriano, G. Cheng, and J. Conradt. gumpy: A Python Toolbox Suitable for Hybrid Brain-Computer Interfaces. Journal of Neural Engineering, 15(6), pp-065003, 2018. DOI: 0.1088/1741-2552/aae186.
- N. Waniek, and J. Conradt. Grid Cells as Transition Encoders. Poster presented at Bernstein Conference on Computational Neuroscience, Göttingen, 2017.
- Preprint: N. Waniek. Multi-Transition Systems: A theory for neural spatial navigation. 2017.
- Preprint: N. Waniek, J. von Stetten, and J. Conradt. Graph cuts for asynchronous event-based vision sensors. 2017.
- M. Mulas, N. Waniek, and J. Conradt. Hebbian plasticity realigns grid cell activity with external sensory cues in continuous attractor models. Front Comput Neurosci, 10:13, Feb 2016. DOI: 10.3389/fncom.2016.00013
- Preprint: N. Waniek, E. Berzs, and J. Conradt. Data Structures for Locally Distributed Routing. 2016.
- N. Waniek, J. von Stetten, and J. Conradt. Event-based graph cuts, 2016. Poster presented at Neurocomputing Systems Workshop, Frauenwörth, 2016.
- N. Waniek, J. Biedermann, and J. Conradt. Cooperative SLAM on small mobile robots. In 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO), pages 1810–1815, Dec 2015.
- M. Mulas, N. Waniek, and J. Conradt. Exploiting grid cell properties for robotic spatial navigation. Poster presented at BCCN Retreat, Tutzing, 2015.
- N. Waniek, M. Mulas, and J. Conradt. Self-organization of grid cell networks. Poster presented at Bernstein Conference on Computational Neuroscience, Heidelberg, 2015.
- N. Waniek, S. Bremer, and J. Conradt. Real-time anomaly detection with a growing neural gas. In Artificial Neural Networks and Machine Learning – ICANN 2014, volume 8681 of Lecture Notes in Computer Science, pages 97–104. Springer International Publishing, 2014.
- R. Araújo, N. Waniek, and J. Conradt. Development of a dynamically extendable spinnaker chip computing module. In Artificial Neural Networks and Machine Learning – ICANN 2014, volume 8681 of Lecture Notes in Computer Science, pages 821–828. Springer International Publishing, 2014.
- M. Mulas, N. Waniek, and J. Conradt. Neuromorphic architecture for robotic spatial navigation. Poster presented at Bernstein Conference on Computational Neuroscience, Göttingen, 2014.
- N. Waniek, M. Mulas, and J. Conradt. Grid cell realignment based on idiothetic head direction cues. Poster presented at Bernstein Conference on Computational Neuroscience, Göttingen, 2014.
- N. Waniek, C. Denk, and J. Conradt. GRIDMAP – from brains to technical implementations. Poster presented at Bernstein Conference on Computational Neuroscience, Tübingen, 2013.
- N. Waniek, C. Denk, and J. Conradt. GRIDMAP – from brains to technical implementations. Poster presented at Bernstein Conference on Computational Neuroscience, Tübingen, 2013.
- N. Waniek and J. Conradt. From brains to technical implementations. 2013. Poster presented at BCCN Sparks Workshop, Tutzing, 2013.
- N. Waniek. Biologically Inspired Model for Visually Driven Navigation. Diploma Thesis, 2012.