Scientific discoveries have created solutions to global problems and fundamentally improved our world. With rapidly increasing amounts of experimental data, machine learning becomes more and more crucial for automating scientific data analysis. However, many real-world workflows demand expert-in-the-loop attention and require models that not only interface with data, but also with experts such as behavioral neuroscientists and medical doctors. My research centers around scientist-in-the-loop frameworks that bridge machine learning and real-world use cases, to enable scalable interpretation of scientific insight from data. These models must accommodate vastly varying data, analysis tasks, and existing knowledge inherent in scientific domains, while optimizing expert effort in the workflow. My approach is to understand the interface between data, models, and scientists across domains, and apply this understanding to develop more efficient methods, such as for discovering semantically meaningful structure from data and integrating domain knowledge with learning. I collaborate closely with scientists to integrate these methods in practice to accelerate scientific progress.

Speaker: Jennifer Sun (Caltech)