Artificial intelligence (AI) is revolutionizing scientific discovery by enabling researchers to analyze large and complex datasets that are otherwise beyond the reach of traditional methods. However, AI is not without limitations—it can introduce systematic errors, make unacceptable mistakes, and often lacks the statistical guarantees that scientists require. This project aims to unlock the full potential of AI in scientific research by developing techniques that integrate imperfect AI models with human-in-the-loop feedback and rigorous statistical estimation. These methods will enable high-throughput, high-precision scientific measurements with quantified uncertainty from large-scale datasets. The work will be grounded in four high-impact applications spanning environmental science and sensing—using data from satellite imagery, radar, acoustics, and sonar. By systematically evaluating the approach in real-world settings, this project will help expand scientists’ ability to use AI responsibly and effectively for discovery and decision-making in areas such as environmental monitoring and disaster response. The project will support interdisciplinary training for PhD students and undergraduate researchers by incorporating its research themes into undergraduate and graduate courses, designing course projects that address real-world challenges in deploying AI, and organizing community workshops that amplify the impact of AI in scientific domains. The project will develop a new