A new study shows our tongues are as unique as fingerprints. AI & 3D tech reveal individual taste and touch patterns, promising advances in personalized nutrition and health. neurosciencenews.com/tongue-ai-neur… 1/2

Our paper covered by Indian news channel NDTV ! Rayna Andreeva @Sarkar_Lab ndtv.com/food/tongues-h…

Our paper on #MachineLearning and #tda to analyse human Papillae covered in The Guardian theguardian.com/science/2023/d…

Machine learning and topological data analysis identify unique features of human papillae in 3D scans Rayna Andreeva @Sarkar_Lab Rik Sarkar nature.com/articles/s4159…

Exciting article about our work with Rik Sarkar School of Informatics, The University of Edinburgh in collaboration with @Sarkar_Lab Food Science and Nutrition at Leeds ! UKRI Centre for Doctoral Training in Biomedical AI The University of Edinburgh 📜: nature.com/articles/s4159…

Un grupo de investigadores aplicó aprendizaje automático para elaborar un mapa de las papilas gustativas sobre la lengua. trib.al/fz3WpP2

Il y a une "empreinte de langue" comme il y a des empreintes digitales 👉 l.sciencesetavenir.fr/rPf

I was interviewed for an article in December by BBC News Mundo BBC News Mundo about our recent work with @Sarkar_Lab (Food Science and Nutrition at Leeds) and Rik Sarkar (School of Informatics, The University of Edinburgh) about using AI and TDA to study the surface of the human tongue. The article is out now (in Spanish) ⬇️ UKRI Centre for Doctoral Training in Biomedical AI

We position #TopologicalDeepLearning as an exciting research frontier, unveil open problems with a potential to transform #AI / #ML landscape, especially for irregular domains. I warmly invite the research community to join our quest: arxiv.org/abs/2402.08871.

Excited to share #assemblyQC, our bash pipeline for benchmarking genome assemblies! 🧬✨Integrating QUAST, BUSCO, Merqury and Liftoff, it looks into assembly metrics, completeness, and gene positioning accuracy. Explore it now!🔬🚀#genomics #Bioinformatics github.com/LiaOb21/assemb…

Katharina Limbeck talking about the role of metric space magnitude in evaluating the diversity of latent-space models at the Helmholtz Munich | @HelmholtzMunich Foundation Model Symposium. Relevant preprint: arxiv.org/abs/2311.16054

New preprint out!🚨We performed a large scale analysis of physicochemical features extracted from over half a million AlphaFold structural models, and using data-driven methods we showed a link between these features and in vivo behaviour of proteins. Find out more below⬇️1/9

Uniform Generalization Bounds on Data-Dependent Hypothesis Sets via PAC-Bayesian Theory on Random Sets ift.tt/XyaBQPW

☀️I'm recruiting 2+3 year postdoc School of Informatics, The University of Edinburgh, part of the #CYBER consortium led by Thomas Gorochowski cyber-mission.github.io Join us in wonderful #Edinburgh and build #AI & #machinelearning models for #syntheticbiology More info linkedin.com/posts/diego-oy… Apply here elxw.fa.em3.oraclecloud.com/hcmUI/Candidat…

Do you want to know how to reuse neural components without knowing the transformations relating their latent spaces? 👀 Stop by our iclrconf poster #239 at 10:45 to find out more! #ICLR2024

At #ICLR2024 looking for some topology to brighten your day? Come find me at poster 204 for some topology inspired subsampling!

My latest PhD Project is now up on medRxiv: medrxiv.org/content/10.110… ! Here, we looked at the association between antidepressant exposure and DNA methylation🧬in 16,536 individuals in Generation Scotland. A 🧵 of our main findings:

Over the past years, we've been exploring topological complexity measures of generalization in deep nets. It is time to announce our recent work, where we propose a novel set of rigorous and efficient topology-based bounds for the generalization gap: arxiv.org/abs/2407.08723. 👇

Friends, I am beyond happy! I'm starting a new position as Full Professor of #MachineLearning at the University of Fribourg Uni Fribourg 🇨🇭! With #SwissAI and many other initiatives, I am taking my research at the intersection of #geometry, #topology, and #MachineLearning to a new