Susanne Still
Short Bio

Susanne Still is Professor of Physics in the Department of Physics and Astronomy, at the University of Hawai`i at Mānoa, where she leads the Physics of Information and Machine Learning Lab. She is a member of the Foundational Questions Institute, from which she has received several research grants, and a Fellow of the European Center for Living Technology. Prof. Still serves as cooperating graduate faculty in the Department of Information and Computer Sciences, where she developed the machine learning curriculum (ICS 235, 435, 635, 636).

Her main research interests are centered around discovering how fundamental physical limits can give rise to constrictive rules for information processing, particularly inference and learning. Her work has advanced the physics of information processing, in particular the thermodynamic analysis of learning (understood as a process in which memories are formed to make predictions), the stochastic thermodynamics of strongly coupled systems, and the thermodynamics of information engines. Prof. Still also pioneered approaches to dynamical and interactive learning in the context of information theoretically motivated learning methods, as well as generalizations to quantum information processing. She has contributed to the foundations of information theory, and applied her expertise in statistical learning theory and support vector machines to a variety of disciplines, including planetary science, mathematical finance and robotics.

Prof. Still received her Ph.D. in Physics at ETH, Zürich. She designed neuromorphic hardware during her PhD, working with the late Misha Mahowald. Before joining the faculty of the University of Hawai`i she was a postdoctoral researcher in William Bialek's Theoretical Biophysics Group at Princeton University.

Prof. Still regularly gives invited talks at conferences around the world, and serves on the Editorial Board of Entropy, and as a reviewer for over a dozen high quality journals, including PRL and PRX.