Max Balandat

Proud to share the 1.0 release of Ax, our platform for adaptive experimentation platform. We've been using it for the past 6 years for optimizing everything from end-to-end AI systems, recommender systems, AR hardware, and material science applications. engineering.fb.com/2025/11/18/ope…

@ViktorStein2 Glad you enjoyed my talk, @ViktorStein2. You can find a pdf version of my slides here: drive.google.com/file/d/1wQsmDy…

Hi @MaxBalandat, I really enjoyed your talk at MSML'25! Would be open to sharing your slides with me? In my applications I also deal with large kernel matrices, so I'm interested in forming matrix-vector products efficiently.

Excited to share our ICML 2025 paper: "Scalable Gaussian Processes with Latent Kronecker Structure" We unlock efficient linear algebra for your kernel matrix which *almost* has Kronecker product structure. Check out our paper here: arxiv.org/abs/2506.06895

Compute is increasing much faster than data. How can we improve classical supervised learning long term (the underlying tech of most of GenAI)? Our ICML position paper's answer: simply train on a bunch of artificial data (noise) and only do inference on real-world data! 1/n

@louistiao @davidmeriksson @eytan We're very excited to have you! Looking forward to working together!

I'm particularly excited to be joining @davidmeriksson @MaxBalandat @eytan and the amazing Adaptive Experimentation (AE) team to continue advancing the frontiers of Bayesian optimization, Gaussian processes ...

🎉 I'm happy to share that I'm joining @Meta as a Research Scientist in New York City! 🇺🇸🗽

@b9dhsjk646 We currently don’t have a PostDoc opening - I’ll be sure to announce it here if we do.

@MaxBalandat Hello is there a Postdoc position? Really interested in this field.

2024 PhD Internship opportunity! Join me and my amazing colleagues on Meta's Adaptive Experimentation team to work on Bayesian optimization, probabilistic modeling / Gaussian Processes, and sample-efficient decision making: metacareers.com/jobs/905634110…

Heading to NOLA for #NeurIPS2023 - find me at our (spotlight!) poster on logEI (Poster Session 2 on Tue): twitter.com/SebastianAment… - or DM me if you want to chat about Bayesian Optimization on the side. P.S.: We're hiring PhD Research Interns for 2024: twitter.com/MaxBalandat/st…

Pleased to share that we have a post-doctoral position on the Adaptive Experimentation team at Meta. Please share this on with others interested in Bayesian optimization, active learning, or Bayesian deep learning! metacareers.com/v2/jobs/347448…

@JPDuerholt @felix_s_k @procrastiprof @leecronin @A_Aspuru_Guzik @felix_s_k What exactly are the shortcomings / issues you observed for batch acquisition functions and discrete spaces in BoTorch? Sequential greedy + conditioning using pending should still work? Or is the issue that there are too many pending points?

TLDR – a distributed self-driving lab for data-driven closed-loop discovery of new gain materials for organic solid-state lasers. More details in the thread 🧵⬇️

@icouckuy @GaussianProcess @eytan @Meta It was a pleasure to have you, @icouckuy! Thanks for presenting your work and participating in the many discussions.

A great experience to present our work at the Adaptive Experimentation (AE) workshop. Thanks to our colleagues and peers for the fruitful discussion on @GaussianProcess and much more! Kudos to @MaxBalandat @eytan and the AE team of @Meta for organizing.

High-Dimensional Combinatorial BO via Dictionary-based Embeddings (BODi) will be presented at AISTATS @aistats_conf next week. Paper: arxiv.org/pdf/2303.01774… Code: github.com/aryandeshwal/B… w/ @deshwal_aryan, @SebastianAment, @davidmeriksson, @MaxBalandat, and @eytan 1/N

Are you attending #SIAMCSE23 and want to learn about how Bayesian optimization can be used for problems such as peptide design, molecular design, and optimizing stellarator coils? Join @MaxBalandat and I tomorrow in our minisymposium on Bayesian optimization in the real-world.

@JPDuerholt @eytan Indeed! They’ve got fancier animations for their twitter posts though :) We do have an internal version of Ax as an RPC service as well; I wonder if there would be value in open sourcing this as well at some point.

On this darkest day of the year, allow me to share a bit of light in the form of a fantastic tutorial on adaptive experimentation put together by my student @SterlingBaird1. Here is the first video in the series: youtu.be/Evua529dAgc new posts through Christmas ;)

@PyTorch @jacobrgardner @gpleiss @SebastianAment Here is the video summary of the poster: youtube.com/watch?v=HE85_4…

I’m looking forward to presenting our poster on LinearOperator [github.com/cornellius-gp/…] - a library for structured linear algebra in @PyTorch - at this year's #PyTorchConference (co-located w/ NeurIPS).

@janadoppa @NeurIPSConf @VGAglietti @StefanieJegelka @jmhernandez233 @eytan @syrineblk Great tutorial, awesome to see BoTorch featured in it as well!

📢 Come to our @NeurIPSConf tutorial on Advances in Bayesian Optimization Dec 5, Mon, 11am to 1:30pm PST bayesopt-tutorial.github.io Speakers: @VGAglietti, Jake Gardner, me Panelists: @StefanieJegelka, @jmhernandez233, Roman Garnett, @eytan, @syrineblk Post your questions below.

If you want to meet @MaxBalandat & @davidmeriksson and learn more, register here for #PyTorchConference on December 2: 🤝In-Person in New Orleans Registration: cvent.me/WX4Yaa 📺Virtual Streaming Event RSVP: cvent.me/G4AzoW 4/4

Apply to attend in-person or watch the live stream of the conference on Dec. 2nd. [ pytorchconference22.splashthat.com]