Sören Becker

[Josh Pacini @joshpacini]

AI models that understand patient biology will be the most transformative medical breakthrough in history.

That’s what we are building: models that can simulate all patient workflows, from diagnostics to clinical trials.

We are growing our SF team to accelerate our progress.

[Valinor @valinordiscover]

Valinor is building momentum with a $13 million funding round to increase clinical trial success through our ML platform.

Thank you to our investors @CRV, @HarpoonVentures, @AminoCollective, and @Pelion_VP for backing our vision to help shape the future of faster, more efficient drug development.

This investment will fuel the expansion of our proprietary patient-derived datasets and grow our San Francisco-based team.

Our multimodal ML models are trained on matched multi-omic and clinical outcome datasets to better predict patient response — helping drug developers identify responders earlier, design smarter trials, and uncover new insights from real-world biology.

If you are working on clinical drug programs and want to learn more about Valinor's predictive ML models for patient response please reach out!

[Yuge Ji @_yji_]

Do you run functional assays? Wish you could get more results without having to scale?

If you’re not using Prophet, you’re leaving potential on the table.

(Warning: pitch not tweetorial)

[Luca Eyring @LucaEyring]

Dealing with undesired distribution shifts in unpaired translation tasks? Our #ICLR2024 paper shows how to mitigate them leveraging Unbalanced OT!
We propose a method to incorporate unbalancedness into any neural Monge map estimator and demonstrate the benefits of unbalancedness.

[Karin Hrovatin @KHrovatin]

Existing datasets can be combined and re-used to bring insights unattainable from individual datasets. To see how to achieve this, even in the presence of substantial batch effects, join my talk at M2D2 series hosted by @_portal_ on 2024/02/13 at 5pCET. https://t.co/14SRtyOf3q

[Danielle Maddix @danielle_maddix]

All are invited to submit to our first ICLR 2024 workshop on AI4DifferentialEquations in Science! We welcome submissions that push forward the use of AI for solving ODEs and PDEs with applications in earth sciences, weather, climate and beyond.

#ai4science #iclr2024

[Dominik Klein @Dominik1Klein]

Join us for a discussion on uncovering new biological insights through the application of Optimal Transport. We'll start with discrete OT and continue with novel Neural OT algorithms using Flow Matching! Can't wait? Feel free to check out https://t.co/pjkhrioy6P!

[IdoAmitLab @IdoAmitLab]

We are very excited to present the development of Zman-seq (“Zman”, Hebrew for “time”), the 1st technology that measures single-cell transcriptomes and physical time in vivo, led by @D_Birschenkaum, @CuriousKX, @FlorianIngelfi1, @AssafWeiner
https://t.co/pDk6ackAtV. (1/19)

[Laura Martens (@lauradmartens.bsky.social) @lauradmartens]

Is binarization of scATAC-seq data really necessary? The conclusion from our analysis is that a quantitative treatment is in fact beneficial. Now out in Nature Methods! @gagneurlab @fabian_theis https://t.co/UdFyU8fpYz

Many additions since the preprint 👇(1/n)

[Kirtan Padh | કીર્તન પાઢ @_kirtan_]

The AI Transparency Institute is hosting the AI governance forum virtually on 1 December. The speakers come from diverse backgrounds, including law, policy, computer science and philosophy! Do drop by!
Register: https://t.co/ku2bc8WHT6
Speakers: @xriskology @sayashk @ZennerBXL

[Sören Becker @SorenBecker]

Really enjoyed this collaboration!

[Stéphane d'Ascoli @stephanedascoli]

🚨 ODEFormer is on Arxiv! https://t.co/5zVwZpgFYH

We show that Transformers can recover the differential equations governing dynamical systems from noisy & irregularly sampled trajectories.

Very fun collaboration with @SorenBecker, @TrackingPlumes, @pschwllr & @k__niki!

🧵⤵️

[Sara Magliacane @saramagliacane]

Have you ever wondered where do #causal variables come from? 🤔

Join the discussion on #causal #representation #learning at our #CRL workshop @NeurIPSConf

Submission deadline: Sept 29
Workshop date: Dec 15/16

[Eva Holtkamp @Holtkamp_Eva]

1/n We're very excited to announce the release of #DeepRVAT, a deep neural network approach to learn burden scores from rare variants by integrating dozens of annotations in a data-driven manner.

[Dominik Klein @Dominik1Klein]

1/10 Looking for a tool to map cells across time and space? We introduce https://t.co/pjkhrioy6P, a scalable framework for #optimaltransport (#OT) applications in single-cell genomics! https://t.co/ywVsuUOyLH

[Fabian Theis @fabian_theis]

Glad to see our review on best practices for single-cell analysis across modalities out @NatureRevGenet! In a big team effort led by @LukasHeumos & @AnnaCSchaar, we recommend workflows based on benchmarks. Paper at https://t.co/dN1jwlmIZA & extension at https://t.co/NVurPpJaHH.

[Niki Kilbertus @k__niki]

The Munich Center for Machine Learning (MCML) invites applications for multiple roles:

PhD positions: https://t.co/awTNzMy8wf (let them know when you want to work with me)
Group Leader ML: https://t.co/TnWNN0IA3D
Group Leader Ethics of AI/ML: https://t.co/gSaPhUI7u3

[Luca Eyring @LucaEyring]

Very excited to be presenting our work later today at the @lmrl_bio workshop @NeurIPSConf as a contributed talk! For further discussion, join me at poster 43 during the first poster session.

[Causal view on Dynamic Systems Workshop @NeurIPS22 @causal_on]

We would like to thank everyone that followed our workshop, in person or remotely! It was a pleasure to meet all of you 🤗

Thanks to all the speakers for their insightful talks. Excited about further developments on the interaction between causality and dynamical systems! 🔄

[Joris Mooij @JorisMooij]

Delivered a presentation on Markov properties for continuous-time dynamical systems at #NeuRIPS22 workshop "Causal View on Dynamical Systems". In case you missed it, you can watch it here: https://t.co/VZ1HOiJEbG

[Lukas Heumos @LukasHeumos]

I'm very excited to finally announce our online book https://t.co/prWaNLxtbZ on single-cell analysis best practices. We build upon the excellent work of @MDLuecken and expand his tutorial to more analysis use-cases and all widely used modalities.