Assoc. Prof. Antonio Luque
Director, IEEE Region 8
Department of Electronics Engineering
University of Seville, Spain
The use of microfluidics allow the creation of efficient, clean, and safe biomedical devices for analysis and detection of samples, and for synthesis of different pharmaceuticals. Due to the sizes involved, measuring internal magnitudes and effects can be difficult. The talk will describe sensors and measurement options for these devices, providing examples of recent developments in the area.
Antonio Luque currently holds the position of Associate Professor in the Department of Electronics Engineering, University of Seville. He has authored 20 journal papers, 40 conference papers, 3 book chapters, and a text book, in addition to supervising two PhD students. He has been invited researcher and teacher at the Swiss Federal Institute of Technology Lausanne (Switzerland), Auburn University (AL, USA), Delft University of Technology (Netherlands), Jade University (Germany), Harbin Institute of Technology (China) and Tech Institute of Monterrey (Mexico). He was a recipient of the Burgen Scholarship from the Academia Europaea in 2007.
Assoc. Prof. Szymon Lukasik
Faculty of Physics and Applied Computer Science, AGH University of Science and Technology
Systems Research Institute, Polish Academy of Sciences, Poland
Recent decades have been characterized by an unprecedented burst of new - potentially useful - data being generated in various fields of science and engineering. The toolbox of contemporary data science – though broad and reinforced by unconventional methods of artificial intelligence – does not contain algorithms that cope well with the challenges of so-called Big Data. The aim of this talk is to provide examples of techniques based on nature-inspired algorithms designed to solve large-scale unsupervised learning tasks. Besides presenting procedures uncovering unknown patterns in data set without preexisting labels we will also discuss variations of typical unsupervised data exploration, which stem from tackling real-world data science problems. Finally, the vivid landscape of nature-inspired metaheuristics will be also characterized, with the discussion on major trends and critical outlook on some individual algorithms.
Szymon Lukasik is an associate professor at at the AGH University of Science and Technology in Kraków and the Systems Research Institute of the Polish Academy of Sciences. PhD and habilitation in the field of data analysis and computational intelligence. A graduate of the Top 500 Innovators program at the Haas School of Business at the University of California, Berkeley, visiting scientist at UNINOVA (Portugal), National Laboratory of Pattern Recognition (China) and University of Technology Sydney (Australia). Author of over 60 publications in the field of data science and artificial intelligence methods. Invariably fascinated by the world of data and extracting valuable information. His main area of research interests cover nature inspired metaheuristics and unsupervised learning.
Assoc. Prof. Yuri Demchenko
BD Stream Keynote
Senior Researcher, System and Network Engineering Research Group
University of Amsterdam, Amsterdam
Modern Science is becoming increasingly data driven and works with a large amount of data, which are heterogeneous, distributed and require special infrastructure for data collection, storage, processing, and visualisation. Data Science Analytics applications are typically developed on the local computing facilities (or personal workstations) with limited size datasets. However implementation of the data analytics applications in production requires working with large real datasets and using Big Data processing platforms which are usually cloud based. Important stage in practical implementation of data analytics projects is operationalization of the developed ML/AI models that needs to start at early design and development stages. The presentation will overview popular Data Analytics process models that can be supported with new trending operationalization approaches such as DataOps/MLOps and SRE (Site Reliability Enginering). This presentation with provide overview of the modern Big Data infrastructure technologies and tools for data storage and processing which can be highly distributed and heterogeneous and currently increasingly oriented on supporting Data Analytics project operationalisation.
Yuri Demchenko is a Senior Researcher and a lecturer at the Complex Cyber Infrastructure Research Group of the University of Amsterdam. He is graduated from the National Technical University of Ukraine "Kyiv Polytechnic Institute" where he also received his PhD degree. His main research areas include Data Science and Data Management, Big Data and Infrastructure and Technologies for Data Analytics and Artificial Intelligence, DevOps and cloud based software development, general security architectures and distributed access control infrastructure. He is currently involved in the European projects GEANT4, SLICES-DS where he develops different elements of the cloud based infrastructures for scientific research and trusted data sharing, as well as projects MATES, FAIRsFAIR that address various aspects of the digital data skills management and training.
07 June 2021
23 June 2021
Notification of acceptance:
20 July 2021
31 July 2021
Late paper submission:
28 July 2021
05 August 2021
Notification of Late paper acceptance:
29 July 2021
10 August 2021
Camera ready paper:
28 July 2021
08 August 2021
07 July 2021 - 07 August 2021
19 July 2021 - 19 August 2021