no code implementations • 8 Sep 2023 • Alexander E. Siemenn, Tonio Buonassisi
These long computing times are a result of the Gaussian process surrogate model having a polynomial time complexity with the number of experiments.
2 code implementations • 16 Mar 2023 • Alexander E. Siemenn, Eunice Aissi, Fang Sheng, Armi Tiihonen, Hamide Kavak, Basita Das, Tonio Buonassisi
High-throughput materials synthesis methods have risen in popularity due to their potential to accelerate the design and discovery of novel functional materials, such as solution-processed semiconductors.
no code implementations • 22 Nov 2022 • Vitali Petsiuk, Alexander E. Siemenn, Saisamrit Surbehera, Zad Chin, Keith Tyser, Gregory Hunter, Arvind Raghavan, Yann Hicke, Bryan A. Plummer, Ori Kerret, Tonio Buonassisi, Kate Saenko, Armando Solar-Lezama, Iddo Drori
For example, asking a model to generate a varying number of the same object to measure its ability to count or providing a text prompt with several objects that each have a different attribute to identify its ability to match objects and attributes correctly.
1 code implementation • 26 Aug 2022 • Alexander E. Siemenn, Zekun Ren, Qianxiao Li, Tonio Buonassisi
Needle-in-a-Haystack problems exist across a wide range of applications including rare disease prediction, ecological resource management, fraud detection, and material property optimization.
1 code implementation • 28 May 2021 • Alexander E. Siemenn, Evyatar Shaulsky, Matthew Beveridge, Tonio Buonassisi, Sara M. Hashmi, Iddo Drori
Generating droplets from a continuous stream of fluid requires precise tuning of a device to find optimized control parameter conditions.
no code implementations • 6 May 2021 • Alexander E. Siemenn, Matthew Beveridge, Tonio Buonassisi, Iddo Drori
Thus, in this work, we develop a computer vision-driven Bayesian optimization framework for optimizing the deposited droplet structures from an inkjet printer such that it is tuned to perform high-throughput experimentation on semiconductor materials.