Federica Milinanni
DOKTORAND
Kungliga Tekniska Högskolanhttps://www.kth.se/profile/fedmil
Researcher
Researcher ID
About me
I am a PhD student in Applied and Computational Mathematics. In my research I analyse and implement Markov Chain Monte Carlo methods (MCMC) for Uncertainty Quantification (UQ) of parameters in neuron models. My research is part of the Brain-IT project within the Swedish e-Science Research Center (SeRC). I am also part of the Science for Life Laboratory (SciLifeLab).
PUBLICATIONS & PREPRINTS
- Kramer, A., Milinanni, F., Nyquist, P., Jauhiainen, A., Eriksson, O. (2023).
UQSA -- An R-Package for Uncertainty Quantification and Sensitivity Analysis for Biochemical Reaction Network Models. - Milinanni, F., & Nyquist, P. (2023).
A large deviation principle for the empirical measures of Metropolis-Hastings chains. - Eriksson, O., Kramer, A., Milinanni, F., & Nyquist, P. (2021).
Sensitivity Approximation by the Peano-Baker Series.
THESES
- Milinanni, F. (2023). Markov Chain Monte Carlo Methods and Applications in Neuroscience. Licentiate Thesis.
CONFERENCES & WORKSHOPS
-
Math + Neuroscience: Strengthening the Interplay Between Theory and Mathematics (semester program at ICERM), 17/09/2023-05/11/2023, Providence (RI)
Talk: "Parameter estimation with uncertainty quantification via Markov Chain Monte Carlo methods" -
Licentiate Seminar, 14/09/2023, KTH,
- "Markov Chain Monte Carlo Methods and Applications in Neuroscience"
- Monte Carlo Methods and Applications, 26-30/06/2023, Paris (FRANCE)
Poster: "Large Deviation Principle for the Metropolis-Hastings algorithm" - EBRAINS RI Training: Tools for Molecular Simulation of Neuronal Signaling Cascades, 21-23/06/2023, Hybrid
Talk: "Subcellular toolset" - Human Brain Project Summit 2023, 28-31/03/2023, Marseille (FRANCE)
Poster: "Data-driven signaling pathway modelling" - Bayes Comp 2023, 15-17/03/2023, Levi (FINLAND)
Talk:"Large Deviation Principle for the Metropolis-Hastings algorithm" - Hausdorff School on Inverse Problems for Multi-Scale Models, 22-26/08/2022, Bonn (GERMANY)
Talk: "Uncertainty Quantification in Neuronal Signalling Pathways" - Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, 17-22/07/2022, Linz (AUSTRIA)
- Training Workshop on Tools for Molecular Simulation of Neuronal Signaling Cascades, 07-10/03/2022, Virtual
Talk: "Subcellular model building toolset" - YAMC2021: Conference Of Young Applied Mathematicians, 13-17/09/2021, Leuca, Lecce (ITALY)
Talk: "Approximating Fisher Information for efficient MCMC"
Poster: "Approximating Fisher Information for efficient MCMC" - INCOME2021 (Integrative pathway modeling in systems biology and systems medicine), 01-04/03/2021, Virtual
Poster: "Approximating Fisher Information for efficient MCMC"
Courses
Risk Management (SF2980), teacher | Course web