CV

Education

• Ph.D in Version Control Theory, GitHub University, 2018 (expected)
• M.S. in Jekyll, GitHub University, 2014
• B.S. in GitHub, GitHub University, 2012

Work experience

• Spring 2024: Academic Pages Collaborator
  • Github University
  • Duties includes: Updates and improvements to template
  • Supervisor: The Users
• Fall 2015: Research Assistant
  • Github University
  • Duties included: Merging pull requests
  • Supervisor: Professor Hub
• Summer 2015: Research Assistant
  • Github University
  • Duties included: Tagging issues
  • Supervisor: Professor Git

Skills

• Skill 1
• Skill 2
  • Sub-skill 2.1
  • Sub-skill 2.2
  • Sub-skill 2.3
• Skill 3

Publications

Identifying plasticity mechanisms underlying experience-driven adaptation in cortical circuits

Maoutsa, D; Gjorgjieva, J. (in preparation). "Identifying plasticity mechanisms underlying experience-driven adaptation in cortical circuits." (in preparation). .

Plasticity-driven circuit self-organization on spiking stabilized supralinear networks

Adell Segarra,R; Festa, D; Maoutsa, D; Gjorgjieva, J. (in preparation). "Plasticity-driven circuit self-organization on spiking stabilized supralinear networks." (in preparation).

Geometric constraints improve inference of sparsely observed stochastic dynamics

Maoutsa, D. (2022). "Geometric constraints improve inference of sparsely observed stochastic dynamics." ICLR workshop Physics for Machine Learning.

Geometric path augmentation for inference of sparsely observed stochastic nonlinear systems

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.

Deterministic particle flows for constraining stochastic nonlinear systems

Maoutsa, D; Opper, M. (2022). "Deterministic particle flows for constraining stochastic nonlinear systems." Physical Review Research. 4.4 (2022): 043035.

Deterministic particle flows for constraining SDEs

Maoutsa, D; Opper, M. (2021). "Deterministic particle flows for constraining SDEs." NeurIPS workshop Machine Learning for the physical sciences. 1(3).

Brainhack: Developing a culture of open, inclusive, community-driven neuroscience

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).

Interacting particle solutions of Fokker–Planck equations through gradient–log–density estimation

Maoutsa, D; Reich, S; Opper, M. (2020). "Interacting particle solutions of Fokker–Planck equations through gradient–log–density estimation." Entropy. 22.8 (2020): 802.

Inferring network connectivity from event timing patterns

Casadiego*, J; Maoutsa*, D; Timme, M. (2018). "Inferring network connectivity from event timing patterns." Physical Review Letters. 121.5 (2018): 054101.

Talks

Learning latent low-dimensional dynamics from neural population responses: a stochastic control approach

May 06, 2024

Invited talk at Institute for Adaptive and Neural Computation, University of Edinburgh, Edinburgh, UK

Learning latent low-dimensional dynamics from neural population responses: a stochastic control approach

April 24, 2024

Invited talk at Center for Computational Neuroscience, Flatiron institute, New York, USA

Geometric methods for inference of nonlinear systems

April 19, 2023

Invited talk at Gjiorgjieva lab, School of Life Sciences, Technical University of Munich, Munich, Germany

Deterministic particle solutions to Fokker-Planck equations

September 15, 2020

Invited talk at Institute of Mathematics, University of Potsdam, Potsdam, Germany

Revealing network physical interactions from event timing patterns: A model-free approach

October 23, 2018

Invited talk at Max Planck Institute for the Physics of Complex Systems, Dresden, Dresden, Germany

Teaching

Mathematical Models of Neural Systems and Cognitive Functions – Tutorials (M.Sc.)

Introduction to Computational Neuroscience – Research Projects (M.Sc.)

Introduction to Computational Neuroscience – Tutorials (M.Sc.)

Introduction to Data Science (M.Sc./Ph.D.)

Network Dynamics & Complex Systems – Theoretical and Computational Tools (M.Sc.)

Service and leadership

• Currently signed in to 43 different slack teams