Can we Trust Data-Driven Scientific Discoveries?
From Belle Taylor
As more and more scientific domains are collecting vast troves of data, we rely on machine learning techniques to analyze the data and help make data-driven scientific discoveries. In this public lecture, Genevera Allen discusses how machine learning has been used to advance science and asks: "are these data-driven discoveries reproducible?" and "how can we use machine learning to draw reliable scientific conclusions?" Genevera discusses these questions using examples from her own research, including an extended example on clustering. Additionally, she outlines new research directions and offers practical advice for improving the reliability and reproducibility of data-driven discoveries.