About this Seminar
Biography:
Professor Garnett's primary research interest is Bayesian active learning, with a focus on applications in the natural sciences and engineering. A major theme in his research is automating scientific discovery, broadly interpreted to include both theory and practice and both policy design and modeling. He has collaborated in multiple domains across the natural sciences, including astronomy, drug discovery, materials science, surface science, personalized medicine, and animal behavior. His long-term research vision is building fully automated, robust systems for active learning, to democratize machine learning and transform scientific practice in the 21st century.
