Team
Copyright Andreas Friedle
Prof. Justus Piater, Ph.D.
Head of DiSC
Department of Computer Science, Intelligent and Interactive Systems
Universität Innsbruck, Digital Science Center, Innrain 15, 6020 Innsbruck
Statement
I seek to enable autonomous robots to perceive and act flexibly and robustly in unstructured environments, leveraging machine learning methods to build perceptual, motor and reasoning skills. Of particular interest is the ability of robots to conceptualise their environment such that they can learn on the basis of already-learned skills, transfer skills to novel tasks, and communicate with humans.
Copyright Miroslaw Opoka
Dr. Joanna Chimiak-Opoka
Teaching Coordinator
Universität Innsbruck, Digital Science Center, Innrain 15, 6020 Innsbruck
Statement
As a senior lecturer, my goal is to support students in learning technical foundations of digitalisation, especially in gaining programming skills enriched with elements of software development. In my programming course, I follow the inverted classroom approach and incorporate unplugged exercises to increase the intelligibility and the overall effectiveness of the learning process.
Copyright Fotostudio Thomas
Carina König, M.A.
General coordinator
Universität Innsbruck, Digital Science Center, Innrain 15, 6020 Innsbruck
Statement
As coordinator of the Digital Science Center, I serve as the primary contact for its members in all administrative matters so they can be fully dedicated to their research. I ensure effective communication between all parties involved to allow for a lively exchange of information and knowledge. That way, I seek to contribute to the DiSC’s overarching goal: the creation of synergies across disciplines.
Copyright Sébastien Court
Dr. Sébastien Court
Universität Innsbruck, Digital Science Center, Innrain 15, 6020 Innsbruck
Statement
My background are numerical simulations of fluid-structure interactions, elasticity theory, scientific computing, and optimal control theory. At the Digital Science Center, I will work on the Mathematical Foundations of Deep Learning.
Research Area
Deep Learning: Neural Networks for Big Data Analysis
Copyright Katherine Nordskog Dormandy
Katherine Nordskog Dormandy, D.Phil.
Department of Christian Philosophy
Universität Innsbruck, Digital Science Center, Karl-Rahner-Platz 1, 6020 Innsbruck
Statement
One major focus of my research is the norms of human thought in social settings. Central topics include epistemic authority, religious and worldview disagreement, the epistemological import of first-person narratives, and the role of interpersonal trust. My research is sensitive to the ways in which implicit and explicit biases affect human reasoning, and to the relevance of culture and social location on the way we think. Another focus of my research is to apply the tools of philosophy to medicine and medical information science, with an eye toward improving the ways in which medical information systems and databases enable anonymised patient data to be shared and used.
Research Area
Economics and Philosophy of Digitalisation
Copyright Matteo Saveriano
Dr. Ing. Matteo Saveriano
Department of Computer Science
Universität Innsbruck, Digital Science Center, Innrain 15, 6020 Innsbruck
Statement
My research attempts to integrate cognitive robots into smart factories and social environments through the embodiment of AI solutions, inspired by human behavior, into robotic devices. From the economic perspective, cognitive robots have the potential to significantly increase the productivity and the flexibility of modern enterprises. From the social point of view, robots with embodied AI capabilities will improve working conditions and will guarantee seamless care and assistance to people in need.
Research Area
Embodied Artificial Intelligence and Machine Learning
Copyright Reto Stauffer
Mag. Reto Stauffer, Ph.D.
Department of Statistics & Department of Atmospheric and Cryospheric Sciences
Universität Innsbruck, Digital Science Center, Innrain 15, 6020 Innsbruck
Statement
I am working on the development and refinement of statistical machine-learning methods and their application in the field of natural and atmospheric sciences: spatio-temporal regression methods to enhance climatological estimates or probabilistic decision trees to improve weather forecasts. The development of these new methods is accompanied by implementing open-source software which is able to handle vast and continuously growing amounts of data in today’s digital world.
Research Area
Data Science in Atmosphere Climate Research
Copyright Kohei Watanabe
Kohei Watanabe, Ph.D.
Department of Political Science
Universität Innsbruck, Digital Science Center, Innrain 15, 6020 Innsbruck
Statement
I develop R packages for quantitative analysis of textual data (https://quanteda.io/) to accelerate digitisation in political research and beyond. In my own projects, I apply them to measure bias in news coverage and detect spread of false information on the web from the international and political communication perspective.
Research Area
Political Communication & Digital Complexity