Postdoc, Aalborg University · Spatio-temporal Modelling & Probabilistic Machine Learning
I'm a postdoc at Aalborg University, working on landscape modeling and uncertainty quantification as part of the DK-Future project.
Previously, I was at the IMAGE section at DIKU, working on explainable AI and medical image segmentation.
I did my PhD in Biophysics at the Niels Bohr Institute as part of the Kirkegaard Lab. My dissertation, Mind the Gradient, explored differentiable methods for studying microorganism behavior and optimality in biological systems.
I also build open-source tools, mostly in JAX. Always open to ideas and collaborations.
Google Scholar | GitHub | Looking for Lonso, Alberta? Here you go.
Biological organisms have developed extraordinary capabilities to fix broken structures. We exploit that and construct a cellular-automata network to fix common issues on pixel-wise segmentation masks, resulting in a surprisingly effective method that efficiently repairs topological artifacts in medical segmentation models.
We introduce a new explainability mask where a closed contour 'relaxes' on top of the object that the Neural Network is basing its decision on. The cool part is that we move the contour by propagating the gradients through the network and the masking process. Very simple and elegant.
When the hydrodynamic graph model accounts for the energy cost of delivery from node to area, we can apply automatic differentiation to study optimal node positions. Curiously, when the domain is irregular (as in leaves), nodes distribute themselves to maximize efficiency.
Tiny cells have a hard time sensing their environment due to physical limits. We present a theoretical model exploring how receptors should be placed for optimal information processing. Results show clustering in high-curvature membrane regions, aligning with real-cell observations.
Decision making is hard for microscopic cells, yet essential for survival. We present a minimal model providing quantitative understanding of how cells use pseudopod splitting to achieve high-performance chemotaxis with minimal regulation—mechanical intelligence.
Two main chemotaxis strategies exist in nature: temporal (for small cells) and spatial (for larger cells). We show the transition is continuous and a combined strategy outperforms constrained variants.
An end-to-end deep learning approach for extracting precise shape trajectories of motile, overlapping slender bodies in high-density microscopy, applied to swimming nematodes.
See my GitHub for more details.