Speakers
Keynote
Live-cell microscopy asks us to trade off resolution, speed, and how much light a sample can tolerate. Our group develops computational methods that ease this compromise, so that biologists can watch molecular processes for longer and with less damage. I will describe super-resolution approaches such as SRRF and eSRRF that recover fine detail from standard fluorescence data, and the open tools we build around them, including NanoPyx, ZeroCostDL4Mic, and DL4MicEverywhere, which make these analyses reproducible and available to non-specialists. I will also discuss our work on self-driving microscopes that adjust acquisition in real time, and how careful image analysis lets us combine and compare observations made with different imaging methods. Throughout, I will highlight the value of open, well-documented software for early-career researchers, and some pitfalls to keep in mind when interpreting AI-processed images.