---
title: "Adaptive Experimental Design to Accelerate Scientific Discovery and Engineering Design"
authors:
  - "Aryan Deshwal"
doi: "10.24963/ijcai.2026/904"
url: "https://ijcai.org/proceedings/2026/904"
pdf: "https://ijcai.org/proceedings/2026/0904.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "8150-8154"
track: "Early Career Spotlight"
keywords:
  - "AI: Planning and Scheduling"
  - "AI: Search"
  - "AI: Uncertainty in AI"
  - "AI: Machine Learning"
---

# Adaptive Experimental Design to Accelerate Scientific Discovery and Engineering Design

Aryan Deshwal

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 8150-8154. DOI: [10.24963/ijcai.2026/904](https://doi.org/10.24963/ijcai.2026/904)

## 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 designs or hypotheses using expensive experiments in a resource-efficient manner. This paper surveys novel adaptive experimental design methods to address this broad challenge. Specifically, we discuss new probabilistic modeling and decision-making techniques that are applicable in small data settings. These approaches have shown substantial improvements in sample-efficiency, particularly for black-box optimization over high-dimensional combinatorial spaces (e.g., sequences and graphs) and a variety of goals ranging from multiobjective to multi-fidelity optimization. This paper outlines key methods and their real-world sustainability applications in areas such as nanoporous materials discovery, hardware design, surfactant design, and additive manufacturing.