(2026). "From geometry to dynamics: Learning overdamped Langevin dynamics from sparse observations with geometric constraints." Fourty-third International Conference on Machine Learning (ICML 2026)
Maoutsa, D.(2026). "Learning to learn dynamical associations with reward-gated local plasticity." International Conference for Learning Representations-New Frontiers in Associative Memories workshop.
Maoutsa, D.(2026). "Learning to learn dynamical associations with reward-gated local plasticity." International Conference for Learning Representations-Geometry-grounded Representation Learning and Generative Modeling workshop.
Maoutsa, D.(2025). "Discovering neo-Hebbian plasticity rules for reward-driven training of RNNs." NeurIPS-WiML workshop.
Maoutsa, D.(2025). "Meta-learning three-factor plasticity rules for structured credit assignment with sparse feedback." NeurIPS-NeurReps workshop.
Maoutsa, D; Gjorgjieva, J. (in preparation). "Identifying plasticity mechanisms underlying experience-driven adaptation in cortical circuits." (in preparation). .
Adell Segarra,R; Festa, D; Maoutsa, D; Gjorgjieva, J. (in preparation). "Plasticity-driven circuit self-organization on spiking stabilized supralinear networks." (in preparation).
Maoutsa, D. (2022). "Geometric constraints improve inference of sparsely observed stochastic dynamics." ICLR workshop Physics for Machine Learning.
Maoutsa, D. (2023). "Deterministic particle flows for stochastic nonlinear systems: Simulation, Control, and Inference." Technical University of Berlin. 1(3).
Maoutsa, D. (2022). "Geometric path augmentation for inference of sparsely observed stochastic nonlinear systems." NeurIPS workshop Machine Learning for the physical sciences. 4.4 (2022): 043035.
Maoutsa, D; Opper, M. (2022). "Deterministic particle flows for constraining stochastic nonlinear systems." Physical Review Research. 4.4 (2022): 043035.
Maoutsa, D; Opper, M. (2021). "Deterministic particle flows for constraining SDEs." NeurIPS workshop Machine Learning for the physical sciences. 1(3).
Rémi Gau*, Stephanie Noble*, Katja Heuer*, Katherine L Bottenhorn*, Isil P Bilgin*, Yu-Fang Yang*, Julia M Huntenburg*, Johanna MM Bayer*, Richard AI Bethlehem*, et al. (The Brainhack Community). (2021). "Brainhack: Developing a culture of open, inclusive, community-driven neuroscience." Neuron. 1(3).
Maoutsa, D; Reich, S; Opper, M. (2020). "Interacting particle solutions of Fokker–Planck equations through gradient–log–density estimation." Entropy. 22.8 (2020): 802.
Casadiego*, J; Maoutsa*, D; Timme, M. (2018). "Inferring network connectivity from event timing patterns." Physical Review Letters. 121.5 (2018): 054101.
Contirbuted talk at Junior Theoretical Neuroscientist’s Workshop, Flatiron Institute, New York, USA
Invited talk at Marine Biological Laboratory, University of Chicago, Woods Hole, Falmouth, USA
Invited talk at Institute for Adaptive and Neural Computation, University of Edinburgh, Edinburgh, UK
Invited talk at Center for Computational Neuroscience, Flatiron institute, New York, USA
Invited talk at Gjiorgjieva lab, School of Life Sciences, Technical University of Munich, Munich, Germany
Invited talk at Institute of Mathematics, University of Potsdam, Potsdam, Germany
Invited talk at Max Planck Institute for the Physics of Complex Systems, Dresden, Dresden, Germany