Research Scientist Manager, Adaptive Experimentation team @Meta. Creator of BoTorch. Interested in Bayesian Optimization and the like. Scuba Instructor.
San Francisco Bay Area
Joined September 2018
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Max Balandat retweeted
This Thursday at #ICML LILO will be presented during the morning poster session (10:30am - 12:15pm, Hall A #3612).
Come by to chat with @MaxBalandat and @ItsMrLin and learn more about our work.
Check out our work on LILO led by @kasia_kobalczyk and @ItsMrLin. LILO allows users to perform black box optimization with natural language feedback.
Hope to see you at the poster at #ICML2026
Paper: arxiv.org/pdf/2510.17671
Max Balandat retweeted
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/2025/11/1…
Max Balandat retweeted
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
Max Balandat retweeted
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
Heading to NOLA for #NeurIPS2023 - find me at our (spotlight!) poster on logEI (Poster Session 2 on Tue): xitter.gq/SebastianAment/status/… - or DM me if you want to chat about Bayesian Optimization on the side.
P.S.: We're hiring PhD Research Interns for 2024: xitter.gq/MaxBalandat/status/172…
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/9056341…
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/9056341…
Max Balandat retweeted
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/3474…
Max Balandat retweeted
High-Dimensional Combinatorial BO via Dictionary-based Embeddings (BODi) will be presented at AISTATS @aistats_conf next week.
Paper: arxiv.org/pdf/2303.01774.pdf
Code: github.com/aryandeshwal/BODi
w/ @deshwal_aryan, @SebastianAment, @davidmeriksson, @MaxBalandat, and @eytan
1/N
Max Balandat retweeted
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 retweeted
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 ;)
I’m looking forward to presenting our poster on LinearOperator [github.com/cornellius-gp/lin…] - a library for structured linear algebra in @PyTorch - at this year's #PyTorchConference (co-located w/ NeurIPS).
Max Balandat retweeted
📢 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.
Max Balandat retweeted
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.splashth…]
Came across this paper bringing together two of my passions: The ocean and sample-efficient optimization (the paper uses BoTorch for multi-objective optimization of a catamaran hull design via CFD simulations): sciencedirect.com/science/ar…
Max Balandat retweeted
I am excited to share our new NeurIPS paper "Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization"! Many thanks to my collaborators @wanxingchen_, @davidmeriksson, @MaxBalandat, @maosbot, @eytan
#NeurIPS2022
1\n
Max Balandat retweeted
Come by our spotlight talk today (535p ET) and poster session (630-830p ET) at #ICML2022 on robust multi-objective Bayesian optimization (icml.cc/virtual/2022/spotlig…)!
We now have a simple tutorial in BoTorch! botorch.org/tutorials/robust…
with Sait, @MaxBalandat, @maosbot, Enlu, @eytan
Excited to share that our paper "Robust Multi-Objective Bayesian Optimization under Input Noise" has been accepted at ICML!
Preprint: arxiv.org/abs/2202.07549
Code: github.com/facebookresearch/…
with Sait Cakmak, @MaxBalandat, @maosbot, Enlu Zhou, and @eytan
#icml2022
Max Balandat retweeted
I am excited to share that our paper “Multi-Objective Bayesian Optimization over High-Dimensional Search Spaces” has been accepted for an oral presentation at UAI 2022!
w/ @davidmeriksson, @MaxBalandat, @eytan
Paper: arxiv.org/abs/2109.10964
#uai2022
