I am excited to present our work “Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement” at #NeurIPS2021 this week with eytan bakshy and Max Balandat. Paper: arxiv.org/abs/2105.08195 Summary video: youtu.be/XOYo_TNo3Gw 1/n

Our paper on Bayesian optimization w/heterogenous & unknown evaluation costs, subject to a total budget: arxiv.org/pdf/2111.06537…. This is work led by Raul Astudillo, along w/@maxbalandat, eytan bakshy, Peter Frazier. Join us at #NeurIPS2021 poster session 4 (12/8, 4:30 PST)!

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/facebookresear… with Sait Cakmak, Max Balandat, M A Osborne, Enlu Zhou, and eytan bakshy #icml2022

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/ David Eriksson, Max Balandat, eytan bakshy Paper: arxiv.org/abs/2109.10964 #uai2022

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/s…)! We now have a simple tutorial in BoTorch! botorch.org/tutorials/robu… with Sait, Max Balandat, M A Osborne, Enlu, eytan bakshy

I am excited to share our new NeurIPS paper "Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization"! Many thanks to my collaborators Xingchen Wan, David Eriksson, Max Balandat, M A Osborne, eytan bakshy #NeurIPS2022 1\n

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/articl…

If you want to meet Max Balandat & David Eriksson 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

📢 Come to our NeurIPS Conference tutorial on Advances in Bayesian Optimization Dec 5, Mon, 11am to 1:30pm PST bayesopt-tutorial.github.io Speakers: Virginia Aglietti, Jake Gardner, me Panelists: Stefanie Jegelka, Jose Miguel Hernández-Lobato, Roman Garnett, eytan bakshy, Syrine Post your questions below.

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 Sterling G. Baird. Here is the first video in the series: youtu.be/Evua529dAgc new posts through Christmas ;)

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 Max Balandat and I tomorrow in our minisymposium on Bayesian optimization in the real-world.

High-Dimensional Combinatorial BO via Dictionary-based Embeddings (BODi) will be presented at AISTATS AISTATS Conference next week. Paper: arxiv.org/pdf/2303.01774… Code: github.com/aryandeshwal/B… w/ Aryan Deshwal, @SebastianAment, David Eriksson, Max Balandat, and eytan bakshy 1/N

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…

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): x.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: x.com/MaxBalandat/st…

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

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