BIFOLD Colloquium 2022/05/20

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BIFOLD Colloquium 2022/05/20

Machine Learning for Remote Sensing Applications powered by Modular Supercomputing Architectures

Speaker: Dr. Gabriele Cavallaro, Forschungszentrum Jülich

Venue: TU Berlin, Architekturgebäude, Straße des 17. Juni 152, 10623 Berlin, Room: A151

Date & time: May 20/2022, 2 pm

Title: Machine Learning for Remote Sensing Applications powered by Modular Supercomputing Architectures

Abstract:
Supercomputers are unique computing environments with extremely high computational capabilities. They are able to solve problems and perform calculations which require more speed and power than traditional computers are capable of. In particular, they represent a concrete solution for data-intensive applications as they can boost the performance of processing workflows with more efficient access to and scalable processing of extremely large data sets.
This talk will first give an overview of the work and research activities of the ‘‘AI and ML for Remote Sensing’’ Simulation and Data Lab hosted at the Jülich Supercomputing Centre (JSC). Then, it will introduce the Modular Supercomputing Architecture (MSA) systems that are operated by the JSC. An MSA is a computing environment that integrates heterogeneous High Performance Computing (HPC) systems, which can include different types of accelerators (e.g., GPUs, FPGAs) and cutting-edge computing technologies (e.g., quantum and neuromorphic computing) and that is “modularized” by its software stack. The presentation will finally include different examples from Remote Sensing applications that can exploit MSA to drastically reduce the time to solution and provide users with timely and valuable information.

Speaker:
Dr. Gabriele Cavallaro is the Head of the ‘‘AI and ML for Remote Sensing’’ Simulation and Data Lab at the Jülich Supercomputing Centre, Forschungszentrum Jülich Germany. He is currently the Chair of the ‘‘High-Performance and Disruptive Computing in Remote Sensing’’ (HDCRS) Working Group of the IEEE GRSS ESI Technical Committee and Visiting Scientist at the Φ-Lab of the European Space Agency. His research interests cover remote sensing data processing with parallel machine learning methods that scale on cutting-edge distributed computing technologies.


BIFOLD Colloquium 2022/01/05

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BIFOLD Colloquium 2022/01/05

[postponed] “Storing and Analyzing Viral Sequences through Data-driven Genomic Computing”

Speaker: Prof. Stefano Ceri (Politecnico di Milano)

Venue: Virtual event

Time and date: This event will be postponed!

Registration: If you are interested in participating, please contact: coordination@bifold.berlin.

Abstract:

Prof. Stefano Ceri will give a s imple and data-inspired illustration of what is a viral sequence, what are mutations, how mutated sequences become organized forming a “variant”, what are the effects of individual mutations and of variants. He will illustrate the process of deposition of viral sequences to public repositories (GenBank, COGUK, GISAID). In the second part of the seminar, Stefano Ceri wants to discuss the systems that were developed within his group. Specifically, he will illustrate (i) ViruSurf, a search system enabling free meta-data driven search over the integrated and curated databases, now hitting about 3 million SARS-CoV-2 sequences, continuously updated from the above repositories; (ii) VirusViz, a data visualization tool for comparatively analyzing query results; (iii) VirusLab, a tool for exploring user-provided viral sequences; (iv) EpiSurf, a tool for intersecting viral sequences with epitopes – used in vaccine design. He will also hint at ongoing projects for viral surveillance and for exploring a knowledge base of viral resources.

Speaker:
(Copyright: Stefano Ceri)

Stefano Ceri is a professor of Data Management at Politecnico di Milano. His main research interests are extending data management and then acting as data scientists in numerous domains – including social analytics, fake news detection, genomics for biology and for precision medicine, and recently studies concerning the SARS-CoV-2 viral genome. He is the recipient of two ERC Advanced Grants, “Search Computing” (2008-2013) and “data-driven Genomic Computing” (2016-2021). He is an ACM Fellow and received the ACM-SIGMOD “Edward T. Codd Innovation Award” (June 2013).