Adaptive Experimental Design to Accelerate Scientific Discovery and Engineering Design
Abstract
Artificial Intelligence (AI) and Machine Learning hold immense potential to accelerate scientific discovery and engineering design. A fundamental challenge in these domains involves efficiently exploring a large space of hypotheses using expensive experiments in a resource-efficient manner. My research focuses on developing novel adaptive experimental design methods to address this broad challenge. Specifically, I develop new probabilistic modeling and decision making tools that operate in small data settings. These approaches have yielded substantial improvements in sample-efficiency, particularly for black-box optimization over high-dimensional combinatorial spaces (e.g., sequences and graphs). This cover letter outlines key methods I have developed and their real-world sustainability applications in areas such as nano-porous materials discovery, hardware design, and additive manufacturing. Additionally, I highlight my initiatives to foster collaboration between Science/Engineering and AI communities.
BibTeX
@article{Deshwal_2025, title={Adaptive Experimental Design to Accelerate Scientific Discovery and Engineering Design}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/35104}, DOI={10.1609/aaai.v39i27.35104}, abstractNote={Artificial Intelligence (AI) and Machine Learning hold immense potential to accelerate scientific discovery and engineering design. A fundamental challenge in these domains involves efficiently exploring a large space of hypotheses using expensive experiments in a resource-efficient manner. My research focuses on developing novel adaptive experimental design methods to address this broad challenge. Specifically, I develop new probabilistic modeling and decision making tools that operate in small data settings. These approaches have yielded substantial improvements in sample-efficiency, particularly for black-box optimization over high-dimensional combinatorial spaces (e.g., sequences and graphs). This cover letter outlines key methods I have developed and their real-world sustainability applications in areas such as nano-porous materials discovery, hardware design, and additive manufacturing. Additionally, I highlight my initiatives to foster collaboration between Science/Engineering and AI communities.}, number={27}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Deshwal, Aryan}, year={2025}, month={Apr.}, pages={28709-28709} }