I'm a postdoc at Aalborg University (2026–present), working on landscape modelling and uncertainty quantification as part of the DK-Future project.
Previously, I was at the IMAGE section at DIKU (2025–2026), working on explainable AI and medical image segmentation.
I did my PhD in Biophysics at the Niels Bohr Institute (2022–2025) as part of the Kirkegaard Lab. My dissertation, Mind the Gradient, explored differentiable methods for studying microorganism behaviour and optimality in biological systems.
I am a co-founder of phaige (2026–present), a startup working on optimising phage isolation to make it a practical alternative to antibiotics.
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.
Research
Biophysics & Statistical Physics
Computer Vision
Probabilistic Modelling
Peer-reviewed Papers (Journals & Conferences)
Energetic costs of reaching the physical limits of sensing
Sensing a chemical gradient means absorbing molecules, and absorbing molecules costs energy. So how much must a cell burn to sense well? We derive nice closed-form relations tying gradient-sensing accuracy to entropy production, and find a happy surprise: cells can get most of the way to peak chemotaxis while paying only a tiny fraction of the full diffusion-limited cost. I guess precision is cheap, but perfection is not.
Limits of cell sensing in observer-absorber assemblies
A lonely cell senses better by absorbing molecules, which kills off rebinding correlations. That we know. But in a colony, every molecule you absorb is one your neighbour can't. We introduce an observer-absorber model that smoothly interpolates between Berg-Purcell monitoring and perfect absorption, and show the best strategy depends on geometry with Pareto fronts emerging from the tug-of-war.
rNCA: Self-repairing segmentation masks
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.
Extremal contours: Gradient-driven contours for compact visual attribution
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.
Spline refinement with differentiable rendering
Sometimes predicted centerlines look slightly off. We introduce a training-free differentiable rendering approach to spline refinement, achieving both high reliability and sub-pixel accuracy. It serves as a drop-in replacement for the popular active contour model.
Adaptive node positioning in biological transport networks
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 maximise efficiency.
Local clustering and global spreading of receptors for optimal spatial gradient sensing
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.
Persistent pseudopod splitting is an effective chemotaxis strategy in shallow gradients
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.
Irreversibility in non-reciprocal chaotic systems
A stochastic-thermodynamic framework analysing the relationship between irreversibility and dynamical behaviour in high-dimensional chaotic systems.
Learning optimal integration of spatial and temporal information in noisy chemotaxis
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.
Fast detection of slender bodies in high-density microscopy data
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.
PhD Thesis
Mind the Gradient: Differentiable computational methods in microorganism behaviour studies
Uses differentiable programming techniques to develop computational methods and mathematical models exploring navigation, sensory integration, and behavioural adaptations under physical constraints of the microscopic scale.
Software
- Bayex – Bayesian Optimisation in JAX
- PCAx – Differentiable PCA in JAX
- t-SNEx – Minimal t-SNE in JAX
- BoundVor – Bounded Voronoi Tessellation
- Notata – Scientific Logging
See my GitHub for more details.