Atte Aalto


Faculté ou Centre  Luxembourg Centre for Systems Biomedicine  
Department  Systems Control  
Adresse postale 
Université du Luxembourg 6, avenue du Swing L4367 Belvaux 

Bureau sur le campus  BioTech II, 0.06  
Langues parlées  English, Finnish  
Séjours de recherche en  France, Luxembourg, Finland  
Postdoc in the Systems Control group (Gonçalves Lab)
Background
 December 2016: Postdoc researcher at LCSB’s Systems Control group
 20152016: Postdoc researcher at INRIA Saclay ÎledeFrance research center
 2014: Doctor of Science, Department of Mathematics and Systems Analysis, Aalto University, Finland
 2009: Master of Science, Helsinki University of Technology, Finland
My background is in applied mathematics, and the area of my doctoral thesis was mathematical systems theory. In particular, I studied the effect of temporal discretization on Kalman filtering and the effect of spatial discretization on Kalman filtering for systems governed by partial differential equations (PDEs). During my first postdoctoral appointment, I studied state and parameter estimation problems for PDE systems.
Current research
Since December 2019, I have been the principal investigator in an FNR CORE junior project on modelling gene expression dynamics from singlecell data, that consist of gene expression measurements at singlecell resolution. One cell can be measured only once, and so the data consist of snapshots of cell ensembles at different times. Although singlecell experiments produce data that is far richer compared to traditional bulk experiments, it is not evident how such data can be used for modelling cell dynamics. Before focusing on singlecell data, I developed a method for gene regulatory network inference from short time series data with low sampling frequency, combining techniques from machine learning, systems theory, and statistics. In the method, gene expression is assumed to follow a nonlinear (stochastic) ordinary differential equation, where the dynamics function is modelled as a Gaussian process. This results in a stochastic process model of gene expression, whose properties depend on the underlying network. Network inference is then carried out by Markov chain Monte Carlo sampling.
Method development
BINGO: a method for gene regulatory network inference from time series data
Last updated on: jeudi 23 avril 2020
2021
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in Modelling COVID19 dynamics and potential for herd immunity by vaccination in Austria, Luxembourg and Sweden (2021)
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in PloS one (2021), 16(5), 0252019
2020
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in Nature Communications (2020), 11
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in Economics & Human Biology (2020), 43
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Eprint/Working paper (2020)
2019
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in Proceedings of the IEEE Conference on Decision and Control (2019, December)
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in Foundations of Systems Biology in Engineering (2019)
2018
in Mathematics of Control, Signals & Systems (2018), 30(3), 9
in IMA Journal of Mathematical Control and Information (2018), 35(suppl_1), 5172
in ESAIM: Control, Optimisation and Calculus of Variations (2018), 24(1), 265288
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in Acta Acustica United with Acustica (2018), 104(2), 323337
2016
in Systems & Control Letters (2016), 94
in International Journal of Control (2016), 89(4), 668679
2015
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in ESAIM: Control, Optimisation and Calculus of Variations (2015), 21(2), 324347
2013
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in Mathematical Control and Related Fields (2013), 3(1), 119
2011
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in Proceedings of the 24th Nordic Seminar on Computational Mechanics (2011)
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in Proceedings of the 18th World Congress of the IFAC (2011)
2009
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in Proceedings of the 6th International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (2009)
URL: https://wwwfr.uni.lu/lcsb/people/atte_aalto  Date: vendredi 22 octobre 2021 21:54:13 