high performance computing

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 focus of my research has been to investigate how cognitive agents can develop structural representation and functions via iterative interaction with the world, exercising agency and learning from resultant perceptual experience. For this purpose, my team has developed various models analogous to predictive coding and active inference frameworks based on the free energy principle. Those models have been used for conducting diverse robotics experiments which include goal-directed planning and replanning in a dynamic environment, social embodied interactions, development of the higher cognitive competency for meta-cognition. The current talk highlights a set of emergent phenomena which we observed in our recent robotics study focused on embodied language [1]. These findings could inform us how children can develop compositional linguistic competency only through limited amount of sensory-motor-language associative learning.

Reference: [1] P. Vijayaraghavan, J. Queißer, S. Flores, J. Tani, (2025). Development of compositionality through interactive learning of language and action of robots. Science Robotics, 10, eadp075.

Bio

Jun Tani received the D.Eng. degree from Sophia University, Tokyo in 1995. He started his research career with Sony Computer Science Lab. in 1993. He became a PI in RIKEN Brain Science Institute in 2001. He became a tenured Professor at KAIST, South Korea in 2012. He is currently a full Professor at OIST. He is also a visiting professor of The Technical University of Munich. His current research interests include cognitive neuroscience, developmental psychology, phenomenology, complex adaptive systems, and robotics. He is an author of “Exploring Robotic Minds: Actions, Symbols, and Consciousness as Self-Organizing Dynamic Phenomena." published from Oxford Univ. Press in 2016.

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

Climate change research today relies on climate information from the past. Historical climate records of temperature observations form global gridded datasets that are examined, for example, in IPCC reports. However, the datasets combining measurement records are sparse in the past. Even today, they contain missing values. We found that recently successful image inpainting technologies, such as those found on smartphones to get rid of unwanted objects or people in photos, are useful here. The derived AI networks are able to reconstruct artificially cropped versions in the grid space for any given month using the missing values observation mask. So herewith we have found with AI a technique that gives us data from the past that we never measured with instruments.  Other important datasets used in the Assessment Report 6 of the IPCC to study climate change, as well as advanced applications such as downscaling in atmosphere and ocean, a hybrid (AI&ESM) data assimilation approach within ICON, or precipitation in broken radar fields are shown in this presentation.

Climate research, including the study mentioned in the previous paragraph, often requires substantial technical expertise. This involves managing data standards, various file formats, software engineering, and high-performance computing. Translating scientific questions into code that can answer them demands significant effort. The question is, why? Data analysis platforms like Freva (Kadow et al. 2021, e.g., gems.dkrz.de) aim to enhance user convenience, yet programming expertise is still required. In this context, we introduce a large language model setup and chat bot interface based on GPT-4/ChatGPT, which enables climate analysis without technical obstacles, including language barriers. This approach is tailored to the needs of the broader climate community, which deals with massive data sets from kilometer-scale modeling and requires a processing environment utilizing modern technologies, but addressing society after all - such as those in the Earth Virtualization Engines (EVE eve4climate.org).

Kadow, C., Hall, D.M. & Ulbrich, U. Artificial intelligence reconstructs missing climate information. Nat. Geosci. 13, 408-413 (2020)