Phillip M. Maffettone

According to our database, Phillip M. Maffettone authored at least 9 papers between 2021 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Facile Integration of Robots into Experimental Orchestration at Scientific User Facilities.

Chandima Fernando, Daniel Olds, Stuart I. Campbell, Phillip M. Maffettone

Proceedings of the IEEE International Conference on Robotics and Automation, 2024

2023

Inverse Protein Folding Using Deep Bayesian Optimization.

Natalie Maus, Yimeng Zeng, Daniel Allen Anderson, Phillip M. Maffettone, Aaron Solomon, Peyton Greenside, Osbert Bastani, Jacob R. Gardner

CoRR, 2023

Self-driving Multimodal Studies at User Facilities.

Phillip M. Maffettone, Daniel B. Allan, Stuart I. Campbell, Matthew R. Carbone, Thomas A. Caswell, Brian L. DeCost, Dmitri Gavrilov, Marcus D. Hanwell, Howie Joress, Joshua K. Lynch, Bruce Ravel, Stuart B. Wilkins, Jakub Wlodek, Daniel Olds

CoRR, 2023

2022

Machine learning enabling high-throughput and remote operations at large-scale user facilities.

Tatiana Konstantinova, Phillip M. Maffettone, Bruce Ravel, Stuart I. Campbell, Andi M. Barbour, Daniel Olds

CoRR, 2022

Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders.

Samuel Stanton, Wesley J. Maddox, Nate Gruver, Phillip M. Maffettone, Emily Delaney, Peyton Greenside, Andrew Gordon Wilson

Proceedings of the International Conference on Machine Learning, 2022

2021

Crystallography companion agent for high-throughput materials discovery.

Phillip M. Maffettone, Lars Banko, Peng Cui, Yury Lysogorskiy, Marc A. Little, Daniel Olds, Alfred Ludwig, Andrew I. Cooper

Nat. Comput. Sci., 2021

Gaming the beamlines - employing reinforcement learning to maximize scientific outcomes at large-scale user facilities.

Phillip M. Maffettone, Joshua K. Lynch, Thomas A. Caswell, Clara E. Cook, Stuart I. Campbell, Daniel Olds

Mach. Learn. Sci. Technol., 2021

Constrained non-negative matrix factorization enabling real-time insights of <i>in situ</i> and high-throughput experiments.

Phillip M. Maffettone, Aidan C. Daly, Daniel Olds

CoRR, 2021

Optimizing High- Throughput Capabilities by Leveraging Reinforcement Learning Methods with the Bluesky Suite.

Daniel Olds, Daniel B. Allan, Thomas A. Caswell, Joshua K. Lynch, Phillip M. Maffettone, Stuart I. Campbell

Proceedings of the 3rd Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing, 2021