Nov 12 Lunch Pitches: Visualizing Change & Intelligent Companions
November 12 @ 12:00 - 13:00
Lunch Pitches with Anton Eriksson and Esteban Guerrero
To encourage cross pollination of ideas between researchers from different disciplines, IceLab hosts interdisciplinary research lunches with the vision of allowing ideas to meet and mate. During the Lunch Pitch Season, the creative lunches take place at KBC every other Tuesday.
Place: KBCon Glasburen (KBCon Interactive Learning Environment), KBC
Time: Tuesday 12 November at 12:00.
Pitch 1: Anton Eriksson: Visualization of change in nested labelled data for qualitative analysis
Postgraduate student at Department of Physics and Icelab, Umeå University.
Anton Eriksson is a PhD student at the Department of Physics and Icelab. Anton currently studies ways to simplify and highlight change in networks evolving through time.
This study can reveal when and how modules in complex system are formed. He also develops visualizations which help researchers explore large multilevel networks.
His background is in physics with a specialization in computational science, which means that he is part physicist, part computing scientist.
Pitch 2: Esteban Guerrero: Coaching systems based on Artificial Intelligence – towards intelligent companions
Researcher at Department of Computing Sciences, Umeå University.
Esteban is a researcher (Forskare) in computing science. His research interests lie in the field of Artificial Intelligence, specifically in the exploration, investigation and development of autonomous systems oriented to support human activities. Currently, he is working on the formalization, design and development of intelligent digital coaching systems to support behavior change.
An intelligent coaching system is a type of interactive technology oriented to support human activities. In order to be a personal companion, the software behind these systems need human-like capabilities such as adaptability, changing the output when it detects changes in human activity achievement; and proactivity, inferring and learning the status of a person’s activity and her/his environment without an explicit order. We explore mechanisms of Artificial Intelligence for learning and reasoning for building intelligent companions. In this talk, we address the research question: how can proactivity be achieved as a software mechanism?