computational fluid dynamics

Abstract

Consensus and its variants, including set agreement and approximate agreement, play a central role in our understanding of asynchronous shared memory distributed computing. I will discuss some classical and recent results about these problems, including algorithms, hierarchies, impossibility results, and space complexity lower bounds.

Bio

Faith Ellen is a Professor of Computer Science at the University of Toronto and is currently serving as the Associate Chair, Graduate Students, in the Department of Computer Science. She received her Ph.D. from the University of California, Berkeley, in 1982. Her research interests span the theory of distributed computing, complexity theory and data structures. From 1997 to 2001, she was vice chair of SIGACT, the leading international society for theory of computation and, from 2006 to 2009, she was chair of the steering committee for PODC, the top international conference for theory of distributed computing. In 2014, she co-authoured the book, "Impossibility Results for Distributed Computing". Faith is a Fellow of the ACM.

Institution

• UHH

Abstract

The brain is an information processing machine, and its function emerges from the ability of networks of neurons to process information. Yet, characterizing and measuring how neurons in the brain interact to process information has been challenging. Here I will present my computational work in developing analytical methods than can be applied to brain recordings during cognitive tasks. These methods allow us to infer how real neurons interact to encode information, transmit it downstream and generate behaviors such as perception and decision-making. They also allow us to understand differences between computations made by real neurons and computations made by machine-learning algorithms performing the same tasks.

Bio

Stefano Panzeri is a computational neuroscientist, researching at the interface between theory and experiment. His main research interest is understanding the principles of cortical information processing. He pursues this interest by developing new quantitative data analysis techniques based on the principles of Information Theory and machine learning and by developing computational models of neural network function. Stefano received a Laurea in Physics from the University of Torino, and a PhD in Computational Neuroscience from SISSA, Trieste, Italy. He has held personal research awards in both theoretical physics and computational neuroscience, including an INFN junior Fellowship in Theoretical Physics at Turin University, an EU Marie Curie postdoctoral Fellowship at the University of Oxford, and an MRC-funded Junior Group Leader position at the University of Newcastle. He has held tenured Faculty positions as assistant, associate and full professor at the Universities of Manchester and Glasgow. He has been visiting scientist at the Max Planck Institute for Biological Cybernetics and at Harvard Medical School for several years. He served as Coordinator of the Center for Neuroscience and Cognitive Systems of IIT. He also served as Deputy Chair of the UK Medical Research Council Panel for fellowships in Bioinformatics and Neuroinformatics. He currently works as Full Professor and Director of the Institute for Neural Information Processing at University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany.

Institution

• UHH