Invited Speakers

Torsten Moeller
http://vda.cs.univie.ac.at/

Title: Democratizing Data Science -- the human in the loop

Abstract:

In this talk I will suggest that Data Science is all about modeling with data. My own goal is the democratization of modeling to make it accessible to a broad audience, especially to those people that have data and want to answer questions based on this data. Often times, these folks are experts in their own fields, but not necessarily experts in math, stats, nor computer science. The "secret" weapon in my approach is the appropriate use of visual analysis, leading to the field of Visual Data Science. I will present a number of developments in vis tools that make the interaction with data (and models) much easier than ever before. Further, I will talk about the challenges in collaboration between visualization experts and domain scientists in creating visual tools for very specific applications.

Bio:
Torsten Möller is a professor of computer science at the University of Vienna, Austria, since 2013. Between 1999 and 2012 he served as a Computing Science faculty member at Simon Fraser University, Canada. He received his PhD in Computer and Information Science from Ohio State University in 1999 and a Vordiplom (BSc) in mathematical computer science from Humboldt University of Berlin, Germany. He is a senior member of IEEE and ACM, and a member of Eurographics. His research interests include algorithms and tools for analyzing and displaying data with principles rooted in computer graphics, human-computer interaction, signal processing, data science, and visualization. He co-founded the research platform Data Science @ Uni Vienna and heads the research group on Visualization and Data Analysis. Since 2018, he serves as the editor-in-chief for IEEE Computer Graphics and Applications. He was appointed Vice Chair for Publications of the IEEE Visualization and Graphics Technical Committee (VGTC) between 2003 and 2012. He has served on a number of program committees and has been papers co-chair for IEEE Visualization, EuroVis, Graphics Interface, and the Workshop on Volume Graphics as well as the Visualization track of the 2007 International Symposium on Visual Computing. He has also co-organized the 2004 Workshop on Mathematical Foundations of Scientific Visualization, Computer Graphics, and Massive Data Exploration as well as the 2010 Workshop on Sampling and Reconstruction: Applications and Advances at the Banff International Research Station, Canada. He is a co-founding chair of the Symposium on Biological Data Visualization (BioVis). In 2010, he was the recipient of the NSERC DAS award. He received best paper awards from IEEE Conference on Visualization (1997), Symposium on Geometry Processing (2008), EuroVis (2010), and ACM Intelligent User Interfaces (IUI, 2016), as well as two second best paper awards from EuroVis (2009, 2012). In 2016 he received the Teaching Award from the University of Vienna and was subsequently nominated for the Ars docendi (in 2017 and 2019), the Austrian governmental teaching award.

Ilaria Torre
https://www.dibris.unige.it/en/torre-ilaria

Title: Exploring the dynamics of concepts and their dependency relations in educational resources

Abstract: Extracting dependency relations from unstructured content and visualizing the learned relations are two challenging issues in many domains. Prerequisite relations are highly relevant dependency relations in the educational domain since they convey meaning about which knowledge is needed to understand and learn new knowledge. Educational resources, such as textbooks, slides and video lectures, implicitly rely on a knowledge graph of concepts and prerequisite relations which can show different dynamics along the timeline of the textual/video content. This is because concepts’ description evolves and becomes richer along the text/video flow, involving new dependency relations with other concepts. Capturing these relations and exploring them provides means towards cognition and AI-powered services. In this talk I will present our research on this topic and some exploration techniques that we used to support the definition of our algorithms for knowledge extraction.

Bio: Ilaria Torre is an Associate Professor at the Department of Informatics, Bioengineering, Robotics and Systems Engineering in Genoa University. In 2003 she obtained her Ph.D at the Department of Computer Science in Turin University, with a dissertation on User Modelling and Adaptive Systems. Her main research interests include intelligent user interfaces, adaptive systems, recommender systems and ubiquitous systems. Over the years, she worked on the application of HCI and AI in various fields, including technology-enhanced learning, cultural heritage, tourism, smart cities, and privacy management – formerly in Turin University and then in Genoa University. She is author of a book and published over 80 papers in international journals and conference proceedings. In 2020 she has been General Chair of the ACM UMAP Conference on User Modeling, Adaptation and Personalization. She is Senior Program Committee Member for the ACM IUI Conference on Intelligent User Interfaces and over the years she served as a PC member of several conferences including ACM UMAP, ACM IUI, Interact, and ACM RecSys. She is an Associate Editor of AI for Human Learning and Behavior Change, specialty section of Frontiers in Artificial Intelligence, and Guest Editor of a Special Section for the IEEE Transactions on Learning Technologies Journal. Se organized and co-chaired several workshops, including IUadaptMe (ACM UMAP 2018), IoTAAL (IEEE GIobal IoT Summit 2017), and SmartLearn (ACM IUI 2017).