iiinova — where your digital self keeps evolving

Reposted by Machine Learning in Science

Matthijs Pals @matthijspals.bsky.social · 28/09/2026

What are the dynamics underlying sequence working memory? To found out, we develop and analyse RNNs fitted to multi-session single-unit data of macaques. See my talk Wed 12:00, at @bernsteinneuro.bsky.social. With @jakhmack.bsky.social @mackelab.bsky.social Data from Chen et al., Neuron 2024.

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Machine Learning in Science @mackelab.bsky.social · 28/09/2026

🪧 Poster IV-99 | Wed 14:00 @byoungsookim.bsky.social: "Towards 3D eye connectome-constrained models of the fly optic lobe to study LPT neurons in optomotor behavior" (8/8)

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Machine Learning in Science @mackelab.bsky.social · 28/09/2026

🪧 Poster III-55 | Wed 12:30 Isaac Omolayo: "Multi-task training of connectome-constrained models of the full fly optic lobe" (7/8)

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Machine Learning in Science @mackelab.bsky.social · 28/09/2026

🪧 Poster II-62 | Tue 18:00 Maren Eberle: "Embodied connectome-constrained inference of Drosophila neural activity from behavior" (6/8)

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Machine Learning in Science @mackelab.bsky.social · 28/09/2026

🪧Poster II-59 | Tue 18:00 @lulmer.bsky.social: "Neural activity constraints improve connectome- and task-constrained models of the fruit fly optic lobe" (5/8)

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Machine Learning in Science @mackelab.bsky.social · 28/09/2026

🪧Poster I-69 | Tue 16:30 Zinovia Stefanidi: "Connectome, transcriptome and task constrained models of the entire fly optic lobe" (4/8)

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Machine Learning in Science @mackelab.bsky.social · 28/09/2026

🎤 Tue 10:30 | Satellite workshop "Advances in optimization of biologically constrained models and how to use them" @jakhmack.bsky.social: "Simulation-based inference: Progress, promise, open problems" (3/8)

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Machine Learning in Science @mackelab.bsky.social · 28/09/2026

🎤 Tue 8:30 | Satellite workshop "Reconciling biology and function in large-scale brain models" @jakhmack.bsky.social: "Learning mechanistic models from neurons to networks to computations" (2/8)

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Machine Learning in Science @mackelab.bsky.social · 28/09/2026

The Macke lab is back at @bernsteinneuro.bsky.social in Frankfurt! 🧠🪰 This year with 2 satellite workshop talks + 5 posters on mechanistic, connectome-constrained models of the fruit fly. All posters are joint work with @srinituraga.bsky.social. Come and say hi! 👋 Details 👇 (1/8)

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Reposted by Machine Learning in Science

Jakob Macke @jakhmack.bsky.social · 14/07/2026

We are excited to announce up to 3 PhD positions in a new project on using ML for "Extracting Probabilistic Representations in Exponential Quantum Spaces" (EXPRESSO) with @philipphennig.bsky.social , @mariokrenn.bsky.social, Igor Lesanovsky, @gmartius.bsky.social in the @ml4science.bsky.social.

quantiki.org

PhD Positions in Quantum Physics and Machine Learning | Quantiki

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Machine Learning in Science @mackelab.bsky.social · 06/07/2026

ModelSMC: we frame LLM-based scientific model discovery as Bayesian inference. Sequential Monte Carlo over executable model structures, with the LLM as a probabilistic proposal mechanism. 📍 Wed Jul 8, at 2:30–4:15 PM KST in Hall A #3512 📄 arxiv.org/abs/2602.18266 🧵 bsky.app/profile/mack...

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Machine Learning in Science @mackelab.bsky.social · 06/07/2026

PRISM: Model inference at scale. We infer joint posteriors over model structure & parameters, with test-time control of complexity via a tunable prior. 📄 arxiv.org/abs/2603.15292 by Manuel Gloeckler, @josepemanzano.bsky.social, @mristam.bsky.social, @coschroeder.bsky.social, @jakhmack.bsky.social

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Machine Learning in Science @mackelab.bsky.social · 06/07/2026

Mackelab is at @icmlconf.bsky.social in Seoul 🇰🇷 Two papers on simulation-based and model-based scientific discovery this year. Come say hi!

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Machine Learning in Science @mackelab.bsky.social · 02/07/2026

We congratulate @matthijspals.bsky.social on his successful PhD defense! During his time in the Mackelab, he used RNNs to link neural activity with underlying mechanisms. Now he moved on to a Postdoc position in @durstewitzlab.bsky.social at the ZI Mannheim and the University of Heidelberg.

Left to right: Philipp Berens, Anna Levina, Matthijs Pals, Peter Dayan, Jakob Macke.

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Machine Learning in Science @mackelab.bsky.social · 26/06/2026

Casting discovery as inference over programs lets scientists: • weigh competing mechanistic hypotheses • quantify uncertainty and non-identifiability • reuse established SMC methods for principled posterior exploration • unify proposal, refinement, and selection.

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Machine Learning in Science @mackelab.bsky.social · 26/06/2026

A unique benefit of our framework? We can analyse the model posterior! We semantically cluster the generated HH models by their added ion-channel mechanisms. Low-weight clusters flag structural mismatch scientists can discard; high-weight clusters point to the mechanisms actually worth pursuing.

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Machine Learning in Science @mackelab.bsky.social · 26/06/2026

We treat Hodgkin-Huxley models as a structural problem: infer missing ion-channel mechanism. ModelSMC uncovers mechanistically meaningful extensions, exposing structural ambiguities and improving fit, yielding a posterior over plausible models rather than a single best model.

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Machine Learning in Science @mackelab.bsky.social · 26/06/2026

In a pharmacological kidney model, ModelSMC recovers meaningful structure from real-data. Even with highly limited data, the posterior concentrates on a small class of aldosterone feedback laws.

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Machine Learning in Science @mackelab.bsky.social · 26/06/2026

As one concrete instantiation of this framework, we introduce ModelSMC: Sequential Monte Carlo directly in model space. We keep a population of candidate programs as particles, propose refinements via the LLM, weight by likelihood, and resample.

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Machine Learning in Science @mackelab.bsky.social · 26/06/2026

Concretely: we approximate a posterior over executable models m that replicate the data x: p(m|x) ∝ p(x|m) p(m) The LLM acts as a probabilistic proposal mechanism inside this inference problem.

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Machine Learning in Science @mackelab.bsky.social · 26/06/2026

Most LLM discovery systems are agentic loops: propose, evaluate, keep the best one. This gives a single point estimate, discarding the probabilistic structure scientists rely on to know what to rule out, what is uncertain, and where more data would help. Instead, we frame discovery as inference.

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Machine Learning in Science @mackelab.bsky.social · 26/06/2026

New paper: We recast automated scientific model discovery with LLMs as Bayesian inference! LLMs write code and carry domain knowledge, great for proposing models. The key idea: discovery is inference, not just generation. What distribution of models explains the data? 🧵 arxiv.org/abs/2602.18266

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Machine Learning in Science @mackelab.bsky.social · 30/03/2026

Come and work with us! We have a PostDoc position at the intersection of ML and Biogeoscience within the TERRA excellence cluster @terra-cluster.org, w/ Senckenberg. Be part of a great ML and Geo community and use ML to investigate fire and its impact on global vegetation🔥 🌱🌳 www.mackelab.org/jobs/

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Machine Learning in Science @mackelab.bsky.social · 11/03/2026

Friday, 13:15 (Poster 2-116): Maren Eberle presents “Objective functions and task complexity in connectome-constrained Spiking Neural Networks” (work in the NYU Neuroinformatics lab)

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Machine Learning in Science @mackelab.bsky.social · 11/03/2026

Friday, 13:15 (Poster 2-040): @lulmer.bsky.social presents “Neural activity constraints improve task-optimized connectome-constrained models” (joint work with @srinituraga.bsky.social).

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Machine Learning in Science @mackelab.bsky.social · 11/03/2026

@mackelab.bsky.social is at @cosynemeeting.bsky.social #cosyne2026 in Lisbon with two posters presented by PhD students from the lab. Thread below on the projects 👇

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Machine Learning in Science @mackelab.bsky.social · 16/02/2026

Auguste developed ML methods for linking neural activity and behavior, ranging from classic discriminative models to deep generative models such as VAEs and DDPMs e.g. doi.org/10.52202/079... or doi.org/10.1016/j.ce....

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Machine Learning in Science @mackelab.bsky.social · 16/02/2026

Janne developed deep mechanistic networks to study when and how detailed measurements of brain wiring (connectomes) can enable accurate, neuron-level predictions of neural dynamics across the brain (www.nature.com/articles/s41...).

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Machine Learning in Science @mackelab.bsky.social · 16/02/2026

The PhD graduation streak continues with Dr. Janne Lappalainen (@lappalainenjk.bsky.social) and Dr. Auguste Schulz (@auschulz.bsky.social) successfully defending in January and February. Congratulations!

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Machine Learning in Science @mackelab.bsky.social · 13/01/2026

Guy Moss (@gmoss13.bsky.social) developed and applied simulation-based inference methods to solve inference problems in glaciology, in collaboration with @geophys-tuebingen.bsky.social. E.g., openreview.net/forum?id=yB5... 3/3

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Machine Learning in Science @mackelab.bsky.social · 13/01/2026

Julius Vetter (@vetterj.bsky.social) worked on deep generative modeling and simulation-based Bayesian inference, with applications to (physiological) time series data. E.g., openreview.net/forum?id=kN0... 2/3

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Machine Learning in Science @mackelab.bsky.social · 13/01/2026

Happy 2026 everyone! Two freshly minted PhDs 🧑‍🎓emerged from our lab at the end of last year. We congratulate Dr Julius Vetter (@vetterj.bsky.social) and Dr Guy Moss (@gmoss13.bsky.social)! Here seen celebrating with the lab 🎳. 1/3

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Machine Learning in Science @mackelab.bsky.social · 01/12/2025

AutoSBI Poster: Tuesday 2 Dec 10:30am at the Amortized ProbML workshop, Copenhagen 11/11

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Machine Learning in Science @mackelab.bsky.social · 01/12/2025

Fifth, we bring AutoML to SBI pipelines with a practical performance metric that does not require ground-truth posteriors, improving inference quality on the SBI benchmark! By @swagatam.bsky.social, @gmoss13.bsky.social, @keggensperger.bsky.social, @jakhmack.bsky.social 10/11

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Machine Learning in Science @mackelab.bsky.social · 01/12/2025

Fourth, in collaboration with Ian C Tanoh and Scott Linderman, we used the Jaxley toolbox and extended Kalman filters to estimate the marginal log-likelihood of a biophysical neuron model. We showed that this enables identifying biophysical parameters given extracellular recordings. 8/11

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Machine Learning in Science @mackelab.bsky.social · 01/12/2025

Third, in collaboration with @kyrakadhim.bsky.social, @philipp.hertie.ai, and others, we built a task- and data-constrained biophysical network of the outer plexiform layer of the mouse retina. To optimize this model, we built it on top of our Jaxley toolbox for differentiable simulation. 6/11

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Machine Learning in Science @mackelab.bsky.social · 01/12/2025

Second, come by to check out NPE-PFN: We leverage the power of tabular foundation models for training-free and simulation-efficient SBI. SBI has never been so effortless! By @vetterj.bsky.social, Manuel Gloeckler, @danielged.bsky.social, @jakhmack.bsky.social 4/11

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Machine Learning in Science @mackelab.bsky.social · 01/12/2025

First, we introduce FNOPE, a new simulation-based inference approach for efficiently and flexibly inferring function-valued parameters. By @gmoss13.bsky.social, @leahsmuhle.bsky.social, Reinhard Drews, @jakhmack.bsky.social and @coschroeder.bsky.social 2/11

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Machine Learning in Science @mackelab.bsky.social · 01/12/2025

Our group is at NeurIPS and EurIPS this year with four papers and one workshop poster. If you are either curious about SBI with autoML, with foundation models, or on function spaces or about differentiable simulators with Jaxley, have a look below 👇 1/11

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Reposted by Machine Learning in Science

Guy Moss @gmoss13.bsky.social · 01/12/2025

I’m super excited to present our new work in #Eurips2025 and #Neurips2025! We developed FNOPE: a new simulation-based inference (SBI) method which excels at inferring function-valued parameters! Paper: openreview.net/forum?id=yB5... Code: github.com/mackelab/fnope (1/9)

openreview.net

FNOPE: Simulation-based inference on function spaces with Fourier...

Simulation-based inference (SBI) is an established approach for performing Bayesian inference on scientific simulators. SBI so far works best on low-dimensional parametric models. However, it is...

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Machine Learning in Science @mackelab.bsky.social · 28/11/2025

We are looking for a Research Engineer (E13 TV-L) to work at the intersection of #ML and #compneuro! 🤖🧠 Help us build large-scale bio-inspired neural networks, write high-quality research code, and contribute to open-source tools like jaxley, sbi, and flyvis 🪰. More info: www.mackelab.org/jobs/

mackelab.org

Jobs - mackelab

The MackeLab is a research group at the Excellence Cluster Machine Learning at Tübingen University!

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Machine Learning in Science @mackelab.bsky.social · 28/11/2025

Maren joined the lab as PhD student in October to work on connectome-constrained models of neural activity and behavior in the fruit fly. She holds an MSc Computational Neuroscience from the BCCN Berlin. 6/7

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Machine Learning in Science @mackelab.bsky.social · 28/11/2025

Byoungsoo (@byoungsookim.bsky.social) joined as a research scientist in October, upon finishing his Master's in Computational Neuroscience in the lab. He is working on modeling optomotor response circuits with a 3D compound eye model of the fruit fly. 5/7

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Machine Learning in Science @mackelab.bsky.social · 28/11/2025

Isaac joined as a master’s thesis student working on representation learning for connectome-constrained models. Now, as a PhD student since July, he’s applying this to models of the fruitfly. He previously did an MSc at AIMS South Africa. 4/7

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