Casadiego*, J.; Maoutsa*, D.; Timme, M.
Inferring network connectivity from event timing patterns.
Physical Review Letters, 2018.

Maoutsa, Dimitra; Reich, Sebastian; Opper, Manfred.
Interacting particle solutions of Fokker–Planck equations through gradient–log–density estimation.
Entropy, 2020. (Editor's choice)

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)
Brainhack: Developing a culture of open, inclusive, community-driven neuroscience.
Neuron, 2021.

Maoutsa, Dimitra; Opper, Manfred.
Deterministic particle flows for constraining SDEs.
NeurIPS workshop Machine Learning for the physical sciences, 2021.

Maoutsa, Dimitra; Opper, Manfred.
Deterministic particle flows for constraining stochastic nonlinear systems.
Physical Review Research, 2022.

Maoutsa, Dimitra.
Geometric path augmentation for inference of sparsely observed stochastic nonlinear systems.
NeurIPS workshop Machine Learning for the physical sciences, 2022.

Maoutsa, Dimitra.
Geometric constraints improve inference of sparsely observed stochastic dynamics.
ICLR workshop Physics for Machine Learning, 2023.

Maoutsa, Dimitra.
"Deterministic particle flows for stochastic nonlinear systems: Simulation, Control, and Inference"
PhD Thesis - TU Berlin ( summa cum laude ), (awaiting publication), 2023