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Richard Michael

@rdmichael

PhD student at University of Copenhagen, @DIKU, @KU_BioML group, DDSA fellow.

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23.11.2024
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Latest posts by Richard Michael @rdmichael

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The most important aspect when facing data shift is the type of shift present in the data. I will give below a few examples of shifts and some existing methods to compensate for it.🧡1/6

01.07.2025 09:38 πŸ‘ 30 πŸ” 16 πŸ’¬ 2 πŸ“Œ 1
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End-to-end differentiable homology search for protein fitness prediction.

@yaringal.bsky.social @deboramarks.bsky.social @pascalnotin.bsky.social

arxiv.org/abs/2506.089...

11.06.2025 19:00 πŸ‘ 32 πŸ” 9 πŸ’¬ 0 πŸ“Œ 0
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Spotted Oosterkade in Utrecht

11.03.2025 13:35 πŸ‘ 0 πŸ” 0 πŸ’¬ 0 πŸ“Œ 0
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GitHub - patrick-kidger/esm2quinox: An implementation of ESM2 in Equinox+JAX An implementation of ESM2 in Equinox+JAX. Contribute to patrick-kidger/esm2quinox development by creating an account on GitHub.

πŸš€ It's time for a new JAX ecosystem library!

This time quite a small one: ESM2quinox. A #JAX + Equinox implementation of the ESM2 protein language model.

GitHub: github.com/patrick-kidg...

SciML is obviously my whole jam. My open source has largely focused on the differential equations ...

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11.02.2025 14:40 πŸ‘ 32 πŸ” 8 πŸ’¬ 2 πŸ“Œ 1
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πŸ”₯ Benchmark Alert! MotifBench sets a new standard for evaluating protein design methods in motif scaffolding.
Why does this matter? Reproducibility & fair comparison have been lackingβ€”until now.
Paper: arxiv.org/abs/2502.12479 | Repo: github.com/blt2114/Moti...
A thread ⬇️

19.02.2025 20:49 πŸ‘ 41 πŸ” 17 πŸ’¬ 1 πŸ“Œ 5

The BioEmu-1 model and inference code are now public under MIT license!!!

Please go ahead, play with it and let us know if there are issues.

github.com/microsoft/bi...

19.02.2025 20:17 πŸ‘ 103 πŸ” 39 πŸ’¬ 2 πŸ“Œ 2
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The slides for my lectures on (Bayesian) Active Learning, Information Theory, and Uncertainty are online now πŸ₯³ They cover quite a bit from basic information theory to some recent papers:

blackhc.github.io/balitu/

and I'll try to add proper course notes over time πŸ€—

17.12.2024 06:50 πŸ‘ 177 πŸ” 28 πŸ’¬ 3 πŸ“Œ 0

I'm very grateful for the work w/ Simon Bartels and @miguelgondu.bsky.social, who did the heavy lifting on the libraries, CI/CD, testing, PR reviews, up-to-date docs, and much more.

Thanks to the generous support and funding by the MLLS center and the #DDSA!

12.12.2024 16:15 πŸ‘ 0 πŸ” 0 πŸ’¬ 0 πŸ“Œ 0
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GitHub - MachineLearningLifeScience/poli-baselines: A collection of objective functions and black box optimization algorithms related to proteins and small molecules A collection of objective functions and black box optimization algorithms related to proteins and small molecules - MachineLearningLifeScience/poli-baselines

With the tasks in place, we can run and assess different optimizers.

A range of solvers is available in the `poli-baselines` package,
here: github.com/MachineLearn....

The docs on how to get set up and contribute, are here: machinelearninglifescience.github.io/poli-docs/

12.12.2024 15:56 πŸ‘ 2 πŸ” 0 πŸ’¬ 0 πŸ“Œ 0
GitHub - MachineLearningLifeScience/poli: A library of discrete objectives A library of discrete objectives. Contribute to MachineLearningLifeScience/poli development by creating an account on GitHub.

We developed the `poli` package for easy access to (bio-chemical) black-box functions (cf. TDC, protein stability, ...) and synthetic tasks, with under-the-hood isolation.

Repo: github.com/MachineLearn...
License: MIT (unless a blackbox says otherwise)
A quick install away: `pip install poli-core`

12.12.2024 15:50 πŸ‘ 2 πŸ” 0 πŸ’¬ 0 πŸ“Œ 0
NeurIPS Poster A survey and benchmark of high-dimensional Bayesian optimization of discrete sequencesNeurIPS 2024

Working on (high-dimensional) Bayesian optimization and care about reproducible, robust comparisons?

Check out our poster presented by @miguelgondu.bsky.social : neurips.cc/virtual/2024... at #NeurIPS2024

Paper: arxiv.org/abs/2406.04739
Site: machinelearninglifescience.github.io/hdbo_benchma...
🧡

12.12.2024 15:44 πŸ‘ 5 πŸ” 0 πŸ’¬ 3 πŸ“Œ 2
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Traveling to Copenhagen. Tomorrow, I will give a talk at the DeLTA lab seminar sites.google.com/diku.edu/del...

I will talk about our joint work with Geoffrey Wolfer on the estimation of the average mixing time for Markov chains + consequences for machine learning. Link:
arxiv.org/abs/2402.10506

01.12.2024 12:57 πŸ‘ 17 πŸ” 5 πŸ’¬ 1 πŸ“Œ 0