Sarah McGough is a Principal Data Scientist at Genentech in South San Francisco, where she leads multiple industry-academia collaborations with the Alan Turing Institute and Stanford University in the space of advanced analytics and personalized healthcare. Her research leverages machine learning and large volumes of real-world health data to generate new molecule and disease insights, as well as to develop solutions for working with "big data" in healthcare.  

Bastian Rieck, M.Sc., Ph.D. is the Principal Investigator of the AIDOS Lab at the Institute of AI for Health at Helmholtz Munich, focusing on topology-driven machine learning methods in biomedicine. Bastian is also a faculty member of TUM, the Technical University of Munich, and a member of ELLIS, the European Laboratory for Learning and Intelligent Systems. Wearing yet another hat, he serves as the co-director of the Applied Algebraic Topology Research Network. Bastian received his M.Sc. degree in mathematics, as well as his Ph.D. in computer science, from Heidelberg University in Germany. He is a big proponent of scientific outreach and enjoys blogging about his research, academia in general, and software development. 

Dr. Denis Engemann is a senior scientist at Roche Pharma Research & Early Development (pRED), Department of Neuroscience & Rare Diseases, Basel, Switzerland. In his current role, Denis works on assessing the impact of novel compounds on brain dynamics and clinical outcomes by applying knowledge, tools and resources resulting from his research. has a broad interdisciplinary research experience bridging machine learning and neuroscience. Before joining Roche Pharma he worked from 2017-2021 at the French National Institute for Research in the Digital Sciences (Inria) in Paris as a researcher, developing novel machine learning models and conducting empirical studies on brain health using large population datasets. During his postdoc in clinical neuroscience at the Paris Brain Insititute / Neurospin (2014-2016) , he developed EEG-based machine learning models for tackling diagnosis in disorders of consciousness. His initial background is in neuroscience and experimental psychology (PhD, University of Cologne; Diploma, 2010, University of Giessen). Since 2013 he has been among the core developers of the MNE-Python software for processing M/EEG signals.