Welcome to my personal website!
I am open to new opportunities, please do get in touch!
Experience
I develop algorithms and computational methods based on machine learning, information theory and statistical inference to generate quantitative insight from real data. I am an experienced programmer who has implemented computational models and applied stastistical inference methods to physical and biological systems. I have worked on research projects involving data from genomics, neuroscience and astrophysics. Currently, I write code in Python and I am learning Rust. Moreover, I have experience with Java and FORTRAN.
Inference:
-
Inference of Neuronal Connectivity: I have used inference methods based on information theory to study the functional connectivity of living neuronal networks. These models can contribute to better understand the computational capabilities of nervous systems and artificial networks. See [2] and my Projects.
-
Causal Inference and Omics: My research with Tom Michoel has focused on causal inference methods and their applications on genomic and multi-omic data. This work has implications on the study of diseases and healthy phenotypes, and finding targets for therapeutic interventions. See [1] and my Projects.
Modeling:
-
Simulating Regulatory Networks: I have worked on models of gene regulatory networks, and I have studied integrate and fire models of neuronal dynamics and the processes generating connections between neurons [2], and the stochastic kinetics of a chemical network controlling quorum sensing in bacteria.
-
Simulating Networks of Living Cells: I have worked with models of gene regulatory networks. Previously, I have studied integrate and fire models of neuronal dynamics and the processes generating connections between neurons [2], and the stochastic chemical kinetics of a chemical network controlling quorum sensing in bacteria.
-
Detectability of Gravitational Effects: I have implemented code in Java to show that the quadrupolar light deflection by Jupiter, as predicted by General Relativity, is detectable by the Gaia space mission of ESA [3].
References
-
Comparison between instrumental variable and mediation-based methods for reconstructing causal gene networks in yeast. Ludl and Michoel (2021) Mol. Omics, 17, 241-251. doi:10.1039/d0mo00140f – PMID:33438713 – arXiv:2010.07417 – github –
-
Impact of Physical Obstacles on the Structural and Effective Connectivity of in silico Neuronal Circuits. Ludl and Soriano (2020). Front. Comput. Neurosci., doi:10.3389/fncom.2020.00077 – PMID:32982710
-
Astrometic Detection of Gravitational Light Deflection by Jupiter with Gaia Data. Ludl (2011) Master’s thesis
Content available under CC BY-SA 4.0