@maxeggl.bsky.social

Happy to announce a new preprint with @violapriesemann.bsky.social ! We introduce an optimization framework to balance infection costs against mitigation costs during epidemics and pandemics, deriving optimal mitigation strategies rather than fixing policies ad hoc. arxiv.org/abs/2512.11454

Optimizing infectious disease mitigation under dynamic conditions

Mitigation measures are essential for controlling the spread of infectious diseases during pandemics and epidemics, but they impose considerable societal, individual, and economic costs. We developed ...

arxiv.org

With a bit of delay (it’s taken a while to process), I am happy to announce that I was awarded a #RyC2024 fellowship this year! One of the most prestigious research fellowships in Spain, I have been working towards this goal for a long time (with many rejections along the way)!

🧵1/Just published: SpyDen, developed with @tatjanat.bsky.social @surbhitwagle.bsky.social, J. Filling, T. Chater & Y. Goda — an open-source Python tool for analyzing 2D microscopy time-series of neurons. GUI-based, robust, and validated by experts. 🔗 shorturl.at/IVmyM Read on for more: 👇

SpyDen: Simplifying molecular and structural analysis across spines and dendrites

AbstractMotivation. Investigating the molecular composition of different neural compartments such as axons, dendrites, or synapses, is critical for underst

shorturl.at

Exciting news! We got funding to organize "Bio-inspired Deep Learning" workshop near Mainz. This time on the topic of dimensionality reduction techniques and led by Angus Chadwick. Applications are now open for participants from both experimental/ computational backgrounds.

Second highlight of the lab: our first work combining the magic of AI with the power of dw-MRI. Full thread below and on @maxeggl.bsky.social page! 🤖🧲🧠

@maxeggl.bsky.social · 2y ago

Also - because we are really proud of this preprint with @desantislab.bsky.social! We applied simulation-based inference to diffusion-weighted MRI (dw-MRI) achieving up to 90% reduction in acquisition time while maintaining high accuracy and robustness. (1/n)

Also - because we are really proud of this preprint with @desantislab.bsky.social! We applied simulation-based inference to diffusion-weighted MRI (dw-MRI) achieving up to 90% reduction in acquisition time while maintaining high accuracy and robustness. (1/n)