From Sensing to Insight: Data-Heavy Supercomputing

 Abstract With advances in sensing technology, the field of imaging has seen an exponential growth in data rates. The belief is that high-quality, high-volume signals encode answers to groundbreaking scientific questions. However, the promise of those discoveries is hampered by our ability to effectively extract insights from the overwhelming amount of data. To unlock the full potential of experimental information, we need new large-scale computational frameworks that decode it and relate it to prior knowledge. In this presentation, various approaches towards this goal with a focus on their application in biomedicine will be explored. From large-scale image registration and stitching to machine learning-based image segmentation, morphometrics, and computational solid and fluid mechanics, Dr. Rossinelli will discuss the challenges and opportunities encountered in processing large volumes of experimental data. 

 Short Bio Diego Rossinelli graduated from ETH Zurich in 2006, where he obtained the doctoral title in 2011. He received the ETH Medal, the Euro-Par Distinguished Paper Award, the ABB Research Prize, and the ETH nomination for the ACM Doctoral Dissertation Award. He was the recipient of the APS Milton van Dyke Award in 2012, the ACM Gordon Bell Prize in 2013, and finalist of the ACM Gordon Bell Prize in 2015. In 2016, Dr. Rossinelli left academia for a multi-year industry experience, and in 2018 was appointed as CTO of Lucid Concepts AG. In 2021, he returned to academia as a supercomputing specialist for Kantonsspital Aarau and the Faculty of Medicine at the University of Zurich.