Max Balandat

Research Scientist Manager, Adaptive Experimentation team @Meta. Creator of BoTorch. Interested in Bayesian Optimization and the like. Scuba Instructor.

[eytan bakshy @eytan]

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.
https://t.co/19aLoJ6jJU

[Max Balandat @MaxBalandat]

@ViktorStein2 Glad you enjoyed my talk, @ViktorStein2. You can find a pdf version of my slides here: https://t.co/fvj1GCMXvN

[Jihao Andreas Lin @JihaoAndreasLin]

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:
https://t.co/wqq89CTrAb

[Samuel Müller @SamuelMullr]

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

[Max Balandat @MaxBalandat]

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

[Max Balandat @MaxBalandat]

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

[Max Balandat @MaxBalandat]

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: https://t.co/5uR0NNXLQU

[Max Balandat @MaxBalandat]

Heading to NOLA for #NeurIPS2023 - find me at our (spotlight!) poster on logEI (Poster Session 2 on Tue): https://t.co/56iWiFpTdz - or DM me if you want to chat about Bayesian Optimization on the side.

P.S.: We're hiring PhD Research Interns for 2024: https://t.co/VTjDPE8iU8

[Max Balandat @MaxBalandat]

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: https://t.co/5uR0NNXLQU

[eytan bakshy @eytan]

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! https://t.co/wmvyx6Q5zU

[Max Balandat @MaxBalandat]

@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?

[Max Balandat @MaxBalandat]

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

[Jana Doppa @janadoppa]

High-Dimensional Combinatorial BO via Dictionary-based Embeddings (BODi) will be presented at AISTATS @aistats_conf next week.

Paper: https://t.co/JJxlkTqGRn
Code: https://t.co/7N8jPs7T1B

w/ @deshwal_aryan, @SebastianAment, @davidmeriksson, @MaxBalandat, and @eytan

1/N

[David Eriksson @davidmeriksson]

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.

[Max Balandat @MaxBalandat]

@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.

[Taylor Sparks @taylordsparks]

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: https://t.co/PATRPZdiRM
new posts through Christmas ;)

[Max Balandat @MaxBalandat]

@PyTorch @jacobrgardner @gpleiss @SebastianAment Here is the video summary of the poster: https://t.co/4xFggXQ7Hr

[Max Balandat @MaxBalandat]

I’m looking forward to presenting our poster on LinearOperator [https://t.co/7NDWOssKpc] - a library for structured linear algebra in @PyTorch - at this year's #PyTorchConference (co-located w/ NeurIPS).

[Max Balandat @MaxBalandat]

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

[Jana Doppa @janadoppa]

📢 Come to our @NeurIPSConf tutorial on Advances in Bayesian Optimization

Dec 5, Mon, 11am to 1:30pm PST
https://t.co/xcYEOW1Pgl

Speakers: @VGAglietti, Jake Gardner, me
Panelists: @StefanieJegelka, @jmhernandez233, Roman Garnett, @eytan, @syrineblk

Post your questions below.

[PyTorch @PyTorch]

If you want to meet @MaxBalandat & @davidmeriksson and learn more, register here for #PyTorchConference on December 2:
🤝In-Person in New Orleans Registration: https://t.co/9awTFMcpus
📺Virtual Streaming Event RSVP: https://t.co/ZbZN8BTi0o

4/4

[Max Balandat @MaxBalandat]

Apply to attend in-person or watch the live stream of the conference on Dec. 2nd. [ https://t.co/0KYLqRjKfB]