Automating Analysis in Biology Using AI, From Data to Discovery

Time and Place: Davis Auditorium, 11:40am, Tuesday April 9

Abstract: Thanks to improved sensors and decreasing data acquisition and storage costs, biologists are increasingly able to collect more and higher quality data. How can we harness the expanding capabilities of GPUs at lower costs and fast-improving AI algorithms to effectively handle the rapid influx of data and extract scientific insights with manageable human effort? My work focuses on integrating machine learning into biology and medicine with three core goals: reducing human effort in data annotation, mitigating human bias in annotations, and uncovering concealed patterns within biomedical data through data-driven approaches.

This talk will focus on tackling these challenges, removing human effort and bias step-by-step. I will elucidate this approach with recent work on behavioral and cellular data analysis, starting with the application of machine learning to quantify animal behavior automatically in neuroscience experiments. I will then present our recent efforts to develop foundational models for scientific applications, showcased by a cellular segmentation model that generalizes across a wide range of cell types. Furthermore, I will show how we can move beyond human-generated labels and discover features directly from the data using self-supervision and experimental observations. Finally, I will outline how these technologies can be combined to accelerate analysis and facilitate discovery for scientific experiments.

Bio: Markus is a postdoc at Caltech working in the computer vision group with Pietro Perona. He received his Ph.D. at the Institute for Neuroinformatics at ETH Zurich. Currently, Markus focuses on developing machine learning algorithms to enhance scientific discovery in biology and medicine, collaborating closely with domain experts. Markus organized the interdisciplinary MABe workshop in 2023 with Jennifer Sun from Cornell and the Kennedy lab at Northwestern, aiming to bring together people and perspectives from different fields working on interacting agents.