On February 10-11, we launched the first cohort of the SMART Master Lab program, equipping master’s students with SmallData research skills through hands-on training and expert mentorship.
The event opened with CRC Speaker Harald Binder introducing the fundamentals of SmallData, followed by SMART PI Nadine Binder and SMART Coordinator Iván Acevedo, who outlined the SMART Master Lab goals and its role in research innovation.
A big thanks goes to several of our doctoral researchers, who played a key role by presenting their research projects and providing interactive activities on their particular methods for the students. Sessions covered:
– Time-to-event models for healthcare pathways (project A02)
– Learning of a partial differential equation (project B02)
– Ordinary differential equation system parameter tuning (project B03)
– Generative models (project B05)
– Hidden Markov models and their applications (project C01)
– Reinforcement learning (project C04)
– End-to-end modeling with two-photon imaging data (project F)
– Autoencoder latent space reduction (project F)
A highlight of the kick-off was the SmallData Scavenger Hunt across the campus, promoting teamwork and problem-solving, both important aspects of collaborative research.
With the kick-off complete, students now begin their 10-week research projects, applying these methods to real-world challenges under doctoral mentorship. SMART Master Lab continues to drive innovation in SmallData methodologies, shaping the next generation of researchers.
We give our sincere thanks and gratitude to The University of Freiburg for the funding of our SMART Master Lab project.
Institute of Medical Biometry and Statistics,
Faculty of Medicine and Medical Center –
University of Freiburg
