Marius Yamakou

Prof. Dr. Marius Yamakou

Interim Professor

Faculty of Sciences
Department of Data Science (DDS)

Room: Room 02.343
Cauerstr. 11
91058 Erlangen

Assistance

Andrea Hoppe

Cauerstr. 11
91058 Erlangen
More › Details for Andrea Hoppe

Office hours: By appointment via e-mail or on Fridays, 11:30 am – 12:00 pm, Room: 02.343

  • 10/2024 – present: Interim Professor, Department of Data Science, University of Erlangen-Nürnberg, Germany
  • 04/2021 – 10/2024: Researcher and Lecturer, Department of Data Science, University of Erlangen-Nürnberg, Germany
  • 11/2019 – 03/2021: Postdoc, Department of Mathematics, University of Erlangen-Nürnberg, Germany
  • 02/2018 – 10/2019: Postdoc, Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany

  • 18/08/2024 – 27/08/2024: Centre for Mathematical Sciences, Lund University, Sweden
  • 28/05/2023 – 03/06/2023: Centre for Mathematical Sciences, Lund University, Sweden
  • 12/02/2023 – 23/02/2023: Perimeter Institute for Theoretical Physics, Waterloo, Canada
  • 05/12/2022 – 18/12/2022: Department of Mathematics, Brandeis University, Massachusetts, USA
  • 01/10/2022 – 30/10/2022: INRIA Research Centre, Université Côte d’Azur, France
  • 01/02/2019 – 30/04/2019: Department of Applied Mathematics, Technical University of Denmark, Denmark

  • 10/2014 – 02/2018: IMPRS Scholarship, Max Planck Institute for Mathematics in the Sciences, Leipzig
  • 04/2021 – 03/2023: DFG Research Grant as Principal Investigator (Project No. 456989199),  ∼ € 200K

  • 04/2020 – present: Departments of Mathematics and Data Science, University of Erlangen-Nürnberg
    • Lecture: Selected Topics in Mathematics of Learning, WS 24/25
    • Lecture and exercise classes: Dynamical System Theory for Data Scientists, WS 24/25
    • Lecture and exercise classes: Theory of Neural Dynamics and Applications to Machine Learning, SS 24
    • Lecture and exercise classes: Dynamical System Theory for Data Scientists, WS 23/24
    • Lecture and exercise classes: Theory of Neural Dynamics and Applications to Machine Learning, SS 23
    • Exercise classes: Mathematics for Engineers: Stochastics, SS 20
  • 02/2019 – 04/2019: Department of Applied Mathematics, Technical University of Denmark
    • Lecture and exercise classes: Dynamical Systems 2 (M.Sc. course)
  • 10/2016 – 10/2017: Max Planck Institute for Mathematics in the Sciences, Leipzig
    • Seminar lecture series: 5 lectures on Stochastic Neural Dynamics
  • 10/2012 – 06/2013: Department of Physics, University of Buea
    • Exercise classes: PHY 202: Newtonian Mechanics
    • Exercise classes: PHY 207: Mathematical Methods for Physics 1
    • Exercise classes: PHY 301: Analytical Mechanics
    • Exercise classes: PHY 306: Mathematical Methods for Physics 2

I am pleased to discuss possible bachelor’s & master’s thesis topics at the intersection of dynamical systems theory, machine learning, computational neuroscience, and data science.

  • 2024 – present: Dua Kasvi, Predicting bursting dynamics in Morris-Lecar neurons with reservoir computing
  • 2024 – present: Alina Schlabritz, Optimization of noise-induced resonances on simplicial complexes: Application in computational neuroscience
  • 2024 – present: Divyesh Savaliya,  Self-induced stochastic resonance problem: A physics-informed neural network approach
  • 2024 – present: Behnam Babaeian, Chaotic dynamics, synchronization, and Hamiltonian analysis of neurons using physics-informed machine learning
  • 11/2024 – present: Pooya Khalaji, Chaos-induced inhibition of spiking activity in adaptive neural networks
  • 03/2024 – 08/2024: Jay Asodariya, Motion estimation and magnification using swin V2 image transformer and deep convolutional neural networks
  • 10/2023 – 05/2024: Jan Kobiolka, Reduced-order adaptive synchronization in a chaotic neural network with parameter uncertainty: A dynamical system vs. machine learning approach
  • 04/2020 – 04/2021: Florian Bönsel, Control of noise-induced coherent oscillations in three-neuron motifs
  • 03/2016 – 02/2017: Gregor Schuldt, Analyse des Morris-Lecar-Neuronenmodells mit stochastischen Störungen

  • 10/2024 – present: Jonas Sengfelder, Chaotic synchronization and coherence resonance in non-identical neural networks

  • 03/2025: Lecture at the FAU Research Training Group FRASCAL on Machine Learning & Neural Networks, FAU Erlangen
  • 10/2024: Biointerfaces Seminar,  Laboratory of Biointerfaces, Dept. of Chemistry and Pharmacy, FAU, Erlangen
  • 08/2024: International Workshop on Network Dynamics, Centre for Mathematical Sciences, Lund University, Sweden
  • 07/2024: International Conference – XLIV Dynamics Days Europe, Bremen, Germany
  • 06/2024: International Conference on Mathematical Neuroscience, University College Dublin, Ireland
  • 11/2023: Workshop: Dynamics in Coupled Networks, Weierstrass Institute for Applied Analysis & Stochastics, Berlin
  • 10/2023: Computational Neuroscience Seminar: University College London (UCL), London, United Kingdom
  • 07/2023: 32nd Annual Meeting, Organization for Computational Neurosciences, Leipzig, Germany
  • 06/2023: Dynamical Systems Seminar, Centre for Mathematical Sciences, Lund University, Sweden
  • 05/2023: Seminar of Statistical Learning and Dynamical Systems, ScaDS.AI Institute, Uni. of Leipzig, Germany
  • 05/2023: SIAM Conference on Applications of Dynamical Systems, Portland, Oregon, USA
  • 03/2023: Dynamical Systems Seminar, Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany
  • 02/2023: Dynamical systems seminar, Department of Applied Mathematics, University of Waterloo, Canada
  • 02/2023: 14th Conference on Dynamical Systems Applied to Biology and Natural Sciences, Bilbao, Spain
  • 12/2023: Dynamical Systems in Neuroscience seminar, Brandeis University, Waltham, Massachusetts, USA
  • 11/2022: International Conference  Control of Self-Organizing Nonlinear Systems, Potsdam, Germany
  • 10/2022: Conference on Complex Systems, Palma de Mallorca, Spain
  • 09/2022: Workshop on Control of Self-Organizing Nonlinear Systems, Wittenberg, Germany
  • 06/2022: 7th International Conference on Random Dynamical Systems, Hanoi, Vietnam
  • 05/2022: Nonlinear Dynamics Seminar,  Weierstrass Institute for Applied Analysis and Stochastics, Berlin, Germany
  • 09/2021: International Conference on Stochastic Resonance, Perugia, Italy
  • 05/2021: SIAM Conference on Applications of Dynamical Systems, Portland, USA
  • 11/2020: Mini-workshop on Neuronal Dynamics, University of Erlangen-Nürnberg, Germany
  • 08/2020: XL. Dynamics Days Europe, Nice, France
  • 02/2020: Oberseminar: Dynamics, Department of Mathematics, Technical University of Munich, Germany
  • 09/2019: 15th Seminar on Stochastic and Collective Effects in Neural Systems, University of Granada, Spain
  • 07/2019: Workshop on Oscillations, Transients, and Fluctuations in Complex Networks, University of Copenhagen
  • 09/2018: Bernstein Conference on Computational Neuroscience, Technical University of Berlin, Germany
  • 07/2018: 27th Annual Meeting, Organization for Computational Neurosciences, Seattle, USA
  • 05/2018: Seminar: Dynamics and Control of Complex Networks, Inst. of Theoretical Physics, Technical University of Berlin
  • 06/2018: 4th International Conference on Mathematical Neuroscience, Juan-les-Pins, France
  • 03/2018: Deutsche Physikalische Gesellschaft (DPG) Spring Meeting, Berlin, Germany
  • 09/2017: Bernstein Conference on Computational Neuroscience, University of Göttingen, Germany
  • 05/2017: SIAM Conference on Applications of Dynamical Systems, Snowbird, Utah, USA
  • 11/2016: 3rd Dresden-Leipzig Dynamics Day, Technical University of Dresden, Dresden, Germany
  • 06/2015: Workshop: Dynamics of Multi-Level Systems, Max Planck Institute for Physics of Complex Systems, Dresden

My research lies at the intersection of applied mathematics, theoretical physics, complex systems, computational neuroscience, data science, and machine learning. I focus on bridging the gap between mathematical theory and practical application, leveraging machine learning to create innovative tools for analyzing and controlling complex dynamical systems. I aim to advance data-driven modeling and uncover governing equations that describe these systems, combining modern data science techniques with the rigor of traditional dynamical systems theory. For example, I explore the fundamental principles underlying the computational capabilities of biological neurons, which, in turn, inform the development of neuroscience-inspired and physics-informed machine learning algorithms and neural computing methods.

  • Dynamical system theory and nonlinear dynamics
  • Mathematical neuroscience and theory of neuronal dynamics
  • Statistical physics and stochastic dynamics
  • Geometric singular perturbation theory
  • Complex systems theory and adaptive network dynamics
  • Chaos- and noise-induced resonance and synchronization phenomena
  • Self-organization and critical dynamics in complex adaptive systems
  • Inference in dynamical systems
  • Data-driven methods in dynamical systems and neurodynamics
  • Bayesian methodology for high-dimensional and complex data
  • Computation through neural population dynamics
  • Statistical and computational methods in neuroscience and neural data (EEG, MEG) analysis
  • Spiking neural networks and their applications
  • Deep convolutional neural networks, recurrent neural networks, long short-term memory network
  • Physics-informed machine learning and constrained optimization
  • Neuroscience-inspired machine learning: liquid-state machines & echo-state networks
  • In particular, the interfaces between the above fields!

  • 06/2025: Co-organizer of the Conference on Women in Data Science, FAU, Erlangen, Germany
  • 04/2024: Co-organizer of the Conference on Women in Data Science, FAU, Erlangen, Germany
  • 04/2023: Co-organizer of the Conference on Women in Data Science, FAU, Erlangen, Germany
  • 03/2020 – 07/2021: Organizer of the Online Seminar Series on Dynamics and Control (93 talks), FAU
  • 11/2020: Organizer of the Mini-workshop on Neuronal Dynamics (3 talks), FAU
  • 11/2020: Organizer of the Mini-workshop on Robots Learning, Optimization & Control (4 talks), FAU
  • 10/2020: Organizer of the Mini-workshop on Hyperbolic Problems (5 talks), FAU

  • 06/2016: Science Communicator at the  Lange Nacht der Wissenschaften 2016, Inselstrasse 22, Leipzig, Germany
  • 03/2025: Invited Lecture at the FAU Research Training Group FRASCAL (fracture across scales) on machine learning, recurrent neural networks, and physics-informed neural networks

  • AIP Advances
  • Applied Physics and Engineering
  • Applied Physics Letters
  • Applications of Mathematics
  • Brain Sciences
  • Chaos: An Interdisciplinary Journal of Nonlinear Science
  • Cognitive Neurodynamics
  • Communications in Nonlinear Science and Numerical Simulations
  • Discrete Dynamics in Nature and Society
  • European Physical Journal Plus
  • Fluctuation and Noise Letters
  • Frontiers in Computational Neuroscience
  • Journal of Computational and Nonlinear Dynamics
  • Journal of Mathematical Biology
  • International Journal of Bifurcation and Chaos
  • Mathematics (ISSN 2227-7390)
  • Mathematical Methods in the Applied Sciences
  • Neural Processing Letters
  • Neural Networks
  • Nonlinear Dynamics: An International Journal of Nonlinear Dynamics and Chaos
  • Physica A: Statistical Mechanics and its Applications
  • Physica D: Nonlinear Phenomena
  • Physical Review E
  • Physics Letters A
  • Reviews of Modern Physics