Collaborative Research Center

SFB-TRR 161

Quantitative Methods for Visual Computing

We are living in a data society in which data is generated at amazing speed; individuals, companies, organizations, and governments are on the brink of being drawn into a massive deluge of data. The great challenge is to extract the relevant information from vast amounts of data and communicate it effectively.

Typical scenarios include decision and policy making for urban and environmental planning or understanding relationships and dependencies in complex networks, e.g., social networks or networks from the field of bioinformatics. These scenarios are not only of interest to specialized experts; in fact, there is a trend toward including the broad public, which requires the information to be presented in a reliable, faithful, and easy-to-understand fashion.

Visual computing can play a key role in extracting and presenting the relevant information.

In visual computing research the aspect of quantification is often neglected. The SFB-TRR 161 seeks to close this gap.

The long-term goal is to strengthen the research field by establishing the paradigm of quantitative science in visual computing.

About the SFB-TRR 161

News

All SFB-TRR 161 News

SFB-TRR 161 Events

Nov 10th, 2025, 4 pm - 6 pm

University of Stuttgart

Lecture | Human-Centred Information Visualisation for the Energy Sector (and beyond)

Held by:

Sarah Goodwin, Monash University, Melbourne, Australia

Abstract:

This presentation showcases a number of energy data visualisation projects, ranging from immersive tabletop devices for casual data collaboration and knowledge sharing through to measuring cognitive load of energy control room operators. The presentation will highlight the benefits of human-centred approaches and demonstrate capabilities for improved geospatial data analysis and innovation for energy network visualisation. It will also draw on other work, such as our design and development of Gazealytics, the visual eye tracking analysis software, which has helped to identify patterns in typical energy control room operator gaze behaviour. 

Bio:

Sarah Goodwin is a Senior Lecturer at Monash University, Australia and a member of the Embodied Visualisation research group. She is co-Director of Monash Energy Institute’s Grid Innovation Hub and Director of Engagement for the Department of Human-Centred Computing. She completed her PhD at City University, London in 2015. Her research focuses on multi-dimensional geospatial data and co-designing with experts. In her work she seeks to improve human-centred methodologies for domain knowledge extraction for visualisation design, interaction and task analysis. She has worked over 20 years in the field of geospatial analysis and information visualisation in professional and academic roles. She was co-General Chair for IEEE VIS 2023 bringing the conference to Australia for the first time. She also co-chairs two international workshop series: Urban Data Visualisation (CityVis) and Energy Data Visualisation (EnergyVis).Prior to her PhD and moving to Australia she spent 5 years living and working in Germany. 

Location:

University of Stuttgart, Visualization Research Center (VISUS), Room: 00.012

The lecture will be transmitted to the University of Konstanz, Room: ZT1201.

The lecture will be available via WebEx.

Meeting-ID (access code): tba
Meeting password: tba

For participants via WebEx: The transmission will kindly be managed by Patrick Gralka. He will be on site and monitor/manage the WebEx-Session incl. Q&A during and after the talk as well. Please don´t hesitate to get in touch with Patrick in case of questions or problems regarding the transmission/your online participation: Patrick.Gralka@visus.uni-stuttgart.de

All doctoral researchers are asked to take part in the events of the lecture series.

Nov 17th, 2025, 4 pm - 6 pm

University of Konstanz

Lecture | Game AI And Immersive Analytics: Save the World Together?

Held by:

Mike Preuss, Leiden University 

Abstract:

Many tend to believe now that LLMs and only LLMs are AI. Far from it, meanwhile doubts are on the rise if they may lead to much more progress, not to speak of AGI. However, many big AI successes in recent years happened in Game AI (AlphaGo, AlphaStar, OpenAIFive, etc.). One of the strenghts of this field is that it provides methods to create immersive scenarios in a semi-automated way and this is probably where it can assist Immersive Analytics (IA). Thinking the other way around, IA could also be very useful to better understand the algorithms behind Game AI via immersive experiences. And this is not limited to Game AI of course. Coming from the AI perspective, I intend to introduce the recent past developments and current situation and attempt to develop a vision where AI and IA are better connected and mutually support each other.

Bio:

Mike Preuss is associate professor at the Leiden Institute of Advanced Computer Science and most interested in using modern AI algorithms to solve practical problems, most notably in ChemAI (as for retrosynthesis), but generally in contexts where human expertise and new AI methods meet. This encompasses LLM and image/video generation tools and how they can be integrated into human workflows meaningfully. Partly automated Procedural Content generation (PCG) is actually a well-known concept in game AI for a long time already and profits greatly from these new developments. Recently, Mike is also involved with quantum games (quantum versions of board games as Checkers) and drone research.
Mike received his PhD from TU Dortmund University, Germany, in 2013, under the supervision of Hans-Paul Schwefel in Evolutionary Computation, namely in deriving methods for complex multimodal optimization tasks, with a view to real-world applications as the design of ship propulsion engines. In the following years, he stayed with the information systems department of WWU Münster, Germany, before starting his current position at Leiden University where he established the Game Research Lab, bringing together topics from education games for teaching AI to (very recently) training Deep Reinforcement Learning algorithms to learn to play Pokemon Red in a fully automated fashion. He is always looking out for new interesting problems that can be solved by means of modern AI algorithms in and outside of computer games.

Location:

University of Konstanz, Room ZT 702.
University of Stuttgart: The lecture will be transmitted to VISUS, room 00.012

The talks are available via Zoom.

Meeting ID: tba
Passcode: tba

For participants via Zoom: The transmission will kindly be managed by Dimitar Garkov.  He will be on site and monitor/manage the Zoom-Session incl. Q&A during and after the talk as well. Please do not hesitate to get in touch with Dimitar in case of questions or problems regarding the transmission/your online participation: dimitar.garkov@uni-konstanz.de

Nov 24th, 2025, 4 pm - 6 pm

University of Konstanz

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Held by:

Tony Huang, University of Sydney 

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Dec 1st, 2025, 4 pm - 6 pm

University of Stuttgart

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Held by:

Heike Leitte, RPTU University Kaiserslautern-Landau

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Dec 8th, 2025, 4 pm - 6 pm

University of Konstanz

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Held by:

Michael Wybrow, Monash University

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Dec 15th, 2025, 4 pm - 6 pm

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Jan 12th, 2026, 4 pm - 6 pm

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Jan 19th, 2026, 4 pm - 6 pm

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Jan 26th, 2026, 4 pm - 6 pm

LMU Munich

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Joanna Bergström, University of Copenhagen

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Feb 2nd, 2026, 4 pm - 6 pm

University of Stuttgart

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Held by:

Michael Doggett, Lund University

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Past Events