Kolloquiumszyklus - previous dates

Abstract

Since the term "scientific visualization" was coined in the late 1980s, weather forecasting and climate modelling have been among the most prominent areas of application for this relatively young discipline. Besides statistical analysis, visualization is probably the most important tool for evaluating the complex and extensive climate simulation data. In my talk, I will briefly discuss the difference between weather and climate and explain the implications for the visualization of climate and climate change as opposed to the visualization of weather phenomena. However, since climate change will also affect actual future weather events (i.e. their probability, frequency and intensity), we need to deal with both weather and climate when analyzing and visualizing the results of climate projections, i.e. data and phenomena at different spatial and temporal scales. Using many practical examples, I will give an - admittedly subjective - overview of the current state of climate data visualization, i.e. the techniques and tools used in practice. In addition, I will also discuss the challenges we face in light of current trends in high-performance computing and climate modeling, and the resulting requirements.

Short Bio

Michael Böttinger received his Diploma in Geophysics from the University of Hamburg, Germany, in 1988, after which he started working in the field of climate modeling at the Max Planck Institute for Meteorology. In 1990 he joined the scientific visualization team at the German Climate Computing Center (DKRZ). Today, he leads DKRZ’s visualization and public relations group. His research is application oriented and focuses on scientific visualization of climate model data for scientific discovery as well as for communication to the broad public.

Abstract: Humans as well as machines exhibit complex behavior, already when acting alone, but even more when they interact with each other. Events and connections that evolve dynamically are embedded in spatial or non-spatial environments. Such scenarios can be found across various domains: Social networks, human gaze, software systems, or play data from computer games. They involve as actors human participants, traditional algorithms, and artificial agents. To understand the recorded behavior, these scenarios can all be mapped to similar data structures and visualized through related methods. The talk discusses dynamic graph visualization as a method to analyze such scenarios. It focuses on timeline-based methods, which provide a good overview of temporal developments. Since insights can be specifically gained through contrast, visual comparison is a cross-cutting challenge. Finally, game analytics serves as a use case to study complex behavior in a controlled environment. When analyzing artificial agents competing in games, insights can be gained on what behavior the agents learned and strategies they follow.

Bio: Since October 2021, Fabian Beck holds the chair of Information Visualization at the University of Bamberg. His research focuses on methods for the visualization of dynamic structures and on self-explanatory visual representations. These methods can be applied in many areas, such as understanding complex software systems, analyzing the behavior of artificial agents, or organizing literature. His research also explores the interaction of visualizations with textual content and other media for understandable visual reporting. He received his Dr. rer nat. Degree (PhD) in Computer Science from the University of Trier in 2013 and worked as a postdoctoral researcher at the University of Stuttgart Visualization Research Center (VISUS) until 2016. Afterward, he led the visualization group of the paluno Institute for Software Technology at the University of Duisburg-Essen as an assistant professor. In 2018, he was awarded the EuroVis Young Researcher Award.

Human head tracking and gesture recognition are both known problems with solutions using RGB-cameras or an infrared emitter/receiver setup.
In this thesis, we propose a method for head tracking and gesture detection using an 8-by-8 infrared sensor array. 
For this, a novel time-of-flight infrared sensor array is employed, which is both financially and computationally inexpensive, while also alleviating privacy concerns due to the very low resolution of the array.

Abstract
Graphs are a fundamental data structure. We read, use, and categorize information through graphs in daily tasks: every time we read a map of public transportation, every time we create a mental map of a concept, every time we send and receive packets through the internet, we are using graphs. Given how widespread graphs are, it is fundamental that we visualize them effectively. Here, graph layout algorithms come into play: each layout algorithm maps nodes and edges in a graph to coordinates in space, allowing us to draw a graph while attempting to respect readability criteria. We encounter graphs with a wide variety of features and users with diverse use cases: layout algorithms must take into account this breadth of possible requirements, and specialize accordingly. In this talk, I explore different challenges and applications of graph layout algorithms for layered graphs - discussing the usefulness of graph layout algorithms in temporal event sequence visualizations, in a number of case studies, and the tradeoffs involved in choosing and developing a layout algorithm.

Bio
Sara Di Bartolomeo has completed a PhD at Northeastern University, in Boston, with a thesis focused on graph layout algorithms for layered graphs, under the guidance of professor Cody Dunne. She also spent time as a visiting scholar at INRIA Paris, working on algorithms for displaying hypergraphs with professor Jean-Daniel Fekete, and at Microsoft Research, where she worked on visualizing cyberattacks on networks of computers. Currently, Sara leads a small group of PhD students as a postdoc in professor Daniel Keim’s lab at the University of Konstanz and teaches Advanced Topics in Data Visualization.

Abstract: Ensembles of contours arise in various applications like simulation, computer-aided design, and semantic segmentation. Uncovering ensemble patterns and analyzing individual members is a challenging task that suffers from clutter. Ensemble statistical summarization can alleviate this issue by permitting analyzing ensembles' distributional components like the mean and median, confidence intervals, and outliers. Contour boxplots, powered by Contour Band Depth (CBD), are a popular non-parametric ensemble summarization method that benefits from CBD's generality, robustness, and theoretical properties. In this work, we introduce Inclusion Depth (ID), a new notion of contour depth with three defining characteristics. First, ID is a generalization of functional Half-Region Depth, which offers several theoretical guarantees. Second, ID relies on a simple principle: the inside/outside relationships between contours. This facilitates implementing ID and understanding its results. Third, the computational complexity of ID scales quadratically in the number of members of the ensemble, improving CBD's cubic complexity. This also in practice speeds up the computation enabling the use of ID for exploring large contour ensembles or in contexts requiring multiple depth evaluations like clustering. In a series of experiments on synthetic data and case studies with meteorological and segmentation data, we evaluate ID's performance and demonstrate its capabilities for the visual analysis of contour ensembles.

Bio: Nicolas is a PhD candidate in the Computer Graphics and Visualization (CGV) group at TU Delft. He is also a member of the Perceptual Intelligence PI-Lab at the Faculty of Industrial Design Engineering. In his research, Nicolas pursues a human-centered interdisciplinary approach combining several fields like visualization, human-computer interaction, design, and deep learning.

= Abstract =
Describing the intricate biological processes inherent in living organisms across temporal dimensions, the field of physiology stands as a cornerstone in unraveling the mechanisms of life. Intersecting various disciplines, including biology, physics, and chemistry, physiology navigates diverse spatio-temporal scales, serving as a vital link between fundamental sciences and medical practice. Recent years have witnessed a surge in novel experimental methodologies, offering finer resolutions for data characterization. However, the sheer volume and complexity of these datasets underscore the need for sophisticated visualization techniques to complement conventional analytical methods. In this presentation, I will explore recent research on the cross-disciplinary application of illustration and visualization methods to gain insight into the complexities of biomedical processes. Specifically, I will focus on addressing the multifaceted challenges in understanding, analyzing, and communicating human physiology to audiences with varying levels of expertise.


= Biography =
Stefan Bruckner is professor at the University of Rostock, Germany, where he leads the Chair of Visual Analytics at the Institute for Visual and Analytic Computing. He earned his PhD in Computer Science from TU Wien, Austria, in 2008, and was granted the habilitation degree (venia docendi) in Practical Computer Science from the same institution in 2012. From 2013 to 2023, he was professor of visualization at the Department of Informatics of the University of Bergen in Norway. 

Bruckner's research focuses on methods for gaining insight into complex data to advance scientific understanding and discovery, medical diagnosis and treatment, and engineering, as well as techniques for effectively communicating these findings to the public. He has co-authored over 100 research papers covering various topics in visual computing, including illustrative visualization, volume rendering, smart visual interfaces, biomedical data visualization, and visual parameter space exploration.

He has served as program co-chair of EuroVis, PacificVis, the Eurographics Workshop on Visual Computing for Biology and Medicine, and the Eurographics Medical Prize. Additionally, Bruckner is an associate editor of the journals IEEE Transactions on Visualization and Computer Graphics and Computers & Graphics. He currently serves on the Eurographics Executive Committee and is a member of ACM SIGGRAPH, Eurographics, and the IEEE Computer Society.

Abstract:

In my talk, I will share the details of my latest research in visualizing microscopic biological structures at the atomistic level. First, I will introduce the acquisition and processing pipeline and discuss our work on the reconstruction, segmentation, and real-time data visualization stages. I will discuss our work on the differentiability of the presented pipeline and its impact. Afterward, I will present our work on procedural rule-based modeling of the mesoscale molecular models, such as proteins, viruses, and cellular organelles, and their real-time visualization. I will also discuss the automatic generation of documentaries (molecumentaries) on such environments and their implementation in VR and AR with conversational extensions. I will conclude my talk by presenting our work on the physicalization (physical visualization) of membrane-bounded biological structures as assemblable puzzles intended for mass production and outreach.

Biography:

Ciril Bohak is an Assistant professor at the Faculty of Computer and Information Science, University of Ljubljana. His main research topics are visualization, computer graphics, user-computer interaction, gaming technology, and gamification. Currently, he focuses on visualizing biological structures at the nanometer level.

Abstract:

Visualization of large vector data is a core task in geographic visualization systems. Vector maps have to displayed at different generalization levels, traditionally by using several discrete levels-of-detail (LODs). This limits the generalization levels to a fixed and predefined set of LODs. However, fast GPUs and novel rendering techniques can be exploited to integrate dynamic vector map LOD management into GPU-based algorithms for locally-adaptive line and polygon simplification and real-time 3D rendering. We propose new GPU-based techniques that allow for fast interactive visualization of large vector map datasets at variable LODs rendered over a 3D terrain. The technique features massive GPU-parallelized point-on-line and point-inside-polygon testing mechanisms. At run time, appropriate and view-dependent error metrics supports screen-space adaptive LOD levels and pixel-prices line or polygon rendering.

Bio:

Renato Pajarola has been a Professor in computer science at the University of Zürich since 2005, leading the Visualization and MultiMedia Lab (VMML) in the Department of Informatics. He has previously been an Assistant Professor at the University of California Irvine and a Postdoc at Georgia Tech. He has received his Dipl. Inf-Ing. ETH and Dr. sc. techn. degrees in computer science from the Swiss Federal Institute of Technology (ETH) Zurich in 1994 and 1998, respectively. His research interests include interactive large-scale data visualization, real-time 3D graphics, 3D scanning & reconstruction, geometry processing, as well as parallel rendering. He is a EUROGRAPHICS Fellow and a Senior Member of IEEE and ACM.

Technological advances in biological experimental approaches for studying human tissues at single-cell resolution are producing large amounts of complex data and are offering new ways to ask questions with far-reaching impacts on human health. To allow for comprehensive analysis and comparison of the generated data, the ultimate goal is to construct an atlas of the human body that characterizes the cell types, tissue structures, and abundance of different types of biomolecules across these structures. The data supporting these atlas efforts, however, is creating challenging visualization problems due to 1) the dimensionality and density of the data and 2) the multi-modal measurements (including proteins, genes, and metabolites) associated with these structures in both 2D images and 3D volumes. Additionally, many datasets routinely include tens of thousands to millions of cells, with up to thousands of measurements per cell, resulting in critical scalability challenges. This new paradigm of tissue atlas construction presents many relevant visualization challenges that will require the visualization community’s expertise to address. Due to the inherent anatomical nature of the data, biologists need to interact with this data in spatial and hierarchical contexts using visualization systems that are able to handle multi-modal visualization and queries at scale. In this talk I will reflect on the outcomes of our Application Spotlight sessions at IEEE VIS 2023 in Melbourne Australia.

Speaker BIO: Eric Moerth is a Research Fellow (PostDoc) in Biomedical Informatics at Harvard Medical School. He received his PhD from the University of Bergen in Norway, under the supervision of Prof. Noeska Smit. During his PhD study, Eric Moerth conducted research in multimodal medical visualization. His main focus was the research of new and innovative ways to visualize and explore medical data, e.g. MRI data to enable doctors to have a better view at their data. His projects resulted in successful publications in the field of medical visualization.

Abstract: Visual Analytics (VA) is a paradigm for insight generation and automated reasoning by transforming data into hypotheses and visualization to extract new insights, feeding them back into the data. 

Many applications use this principle to provide meaningful mechanisms to assist decision-makers in achieving their goals. This process can be affected by a variety of uncertainties that can interfere with the users decision-making process. Unfortunately, there is no methodical description and handling tool to systematically include uncertainty in VA. We introduce uncertainty-aware viual analytics and its'systematic construction to solve this issue. Further, we present success stories from biomedical applications where UAVA is utilized.

CV: Christina Gillmann is a researcher with the Signal and Image Processing Group, University of Leipzig, Germany, leading her own subgroup on uncertaintyaware visual analytics (UAVA). Her research interests include UAVA, medical visualization, uncertainty analysis, and the transferability of visualization approaches into applications. She received the Ph.D. degree in computer science from the University of Kaiserslautern, Germany, in 2018.

Objectivity is a concept from continuum mechanics, demanding invariance of a measure under continuous changes of the reference frame (coordinate system). A measure should be independent of the observer, different observers moving in different frames should come to the same conclusion about an objective measure.

While objectivity appears a rather natural condition for a useful measure, it creates problems for the analysis of flows because velocity fields are not objective. This has triggered quite some research in recent years in both computational fluid dynamics and flow visualization to come up with objective flow measures and resulting objective flow visualization techniques.

With give an overview about existing objective flow measures. In particular we focus on generic measures, i.e., approaches to transform any flow measure into an objective one. We describe recent approaches to objectivize general flow measures and resolve a dispute about the objectivity and other desired properties of our approaches.

BIO: 

Holger Theisel is professor for Visual Computing at Magdeburg University (Germany). He received his Ph.D. (1996) and habilitation (2001) degrees from the University of Rostock (Germany), and had research stays at Arizona State University (USA), ICIMAF Havana (Cuba), MPI Informatik Saarbrücken (Germany), and Bielefeld University (Germany).

His research interests focus on scientific visualization as well as on geometric design, geometry processing and information visualization and Visual Analytics. He co-authored more than 70 papers in the top journals in the field. He served the community in several ways, among them as General Chair of the IEEE VIS 2018 conference in Berlin, and VIS Executive Committee Co-Chair 2021-2023.

Dear colleagues,

we cordially invite you to attend our GCD Symposium 2023, featuring an 

exhibition of results from the SFB "Advanced Computational Design"!

Time:  November 3, 2023, 9:00-18:00

Place: Kuppelsaal, TU Wien

The detailed program can be found here:

https://gcd.tuwien.ac.at/?p=3300

Please forward this information to people who might be interested in the 

symposium.

We kindly ask potential participants to register for the symposium by sending 

your name, affiliation and email-address to:

gcd-registration(at)geometrie.tuwien.ac.at.

Attending the symposium will be free of charge.

We are looking forward to seeing you at the symposium!

Michael Wimmer

for the members of the

Center for Geometry and Computational Design and the

SFB Advanced Computational Design

We develop an optimization-based method to model smocking, a surface embroidery technique that provides decorative geometric texturing while maintaining stretch properties of the fabric. During smocking, multiple pairs of points on the fabric are stitched together, creating non-manifold geometric features and visually pleasing textures. Designing smocking patterns is challenging, because the outcome of stitching is unpredictable: the final texture is often revealed only when the whole smocking process is completed, necessitating painstaking physical fabrication and time consuming trial-and-error experimentation. This motivates us to seek a digital smocking design method. Straightforward attempts to compute smocked fabric geometry using surface deformation or cloth simulation methods fail to produce realistic results, likely due to the intricate structure of the designs, the large number of contacts and high-curvature folds. We instead formulate smocking as a graph embedding and shape deformation problem. We extract a coarse graph representing the fabric and the stitching constraints, and then derive the graph structure of the smocked result. We solve for the 3D embedding of this graph, which in turn reliably guides the deformation of the high-resolution fabric mesh. Our optimization based method is simple, efficient, and flexible, which allows us to build an interactive system for smocking pattern exploration. To demonstrate the accuracy of our method, we compare our results to real fabrications on a large set of smocking patterns.

In the quest to better understand the transmission dynamics of airborne diseases and the strategies to control its impact, a wide range of simulation models have been developed. Understanding the dynamics of novel diseases with little-known characteristics and unprecedented impacts, generates a need to model multiple aspects with very dissimilar dynamics in a consistent and formal, but also flexible and quick way to study the combined interaction of these aspects. In this talk, I will present agent-based models combining kinematic movement of agents, interaction between them and their surrounding space. These models allow the analysis of different intervention strategies and their efficacy in reducing infections in a population going through an epidemic process driven mainly by airborne contagion.

Virtual landscapes in media and games nowadays display large impressive terrains with richness of details. Therefore, the current challenge is not only to produce visually appealing scenes, but also to ensure they conform to some objective criteria for realism. For example, we could simulate the physical processes underlying natural phenomena, procedurally mimic distributions of measured properties, or learn from real data. In this talk, I will present a few works that followed these ideas to create a variety of landscapes: from deserts to glaciers, from alpine rocky peaks to gentle forested hills, and different degradation effects on natural scenes. Apart from the knowledge borrowed from Earth Sciences and other disciplines outside Computer Science, we will see the inspiration and key ideas in many of these works came from actual hikes!

Short Bio:

I am currently a Maria Zambrano research fellow at the ViRVIG group of the Universitat Politècnica de Catalunya. I obtained my PhD in Computing from UPC in 2018, under the supervision of Carlos Andújar and Antonio Chica. My thesis focused on the creation of realistic natural scenarios, leveraging machine learning techniques and real data to improve procedural and example-based modeling algorithms. After that, I was hired as a postdoctoral researcher by the CNRS in the LIRIS laboratory in Lyon, working on procedural modeling of mountainous landscapes and the simulation of natural phenomena such as dunes, glaciers and ecosystems. My current research project deals with the generation of hiking paths networks and the modeling of degradation effects caused by outdoor activities. I have published in journals such as ACM Transactions on Graphics and Computer Graphics Forum, and presented in top conferences like SIGGRAPH Asia and Eurographics.

Multidimensional data analysis is of broad interest for a wide range of applications. In this talk, I discuss visualization approaches that support the analysis of such data. I start with a brief overview of the field, a conceptual model, and a discussion of visualization strategies.
This part is accompanied by a few examples of recent advancements, with a focus on results from my own work. In the second part, I detail techniques that enrich basic visual mappings like scatterplots, parallel coordinates, or plots of dimensionality reduction by incorporating
local correlation analysis. I also discuss sampling issues in multidimensional visualization, and how we can extend it to uncertainty visualization. The talk closes with an outlook on future research directions.

Biography:
Daniel Weiskopf is a professor and one of the directors of the Visualization Research Center (VISUS) and acting director of the Institute for Visualization and Interactive Systems (VIS), both at the University of Stuttgart, Germany. He received his Dr. rer. nat. (PhD) degree in
physics from the University of Tübingen, Germany (2001), and the Habilitation degree in computer science at the University of Stuttgart, Germany (2005). His research interests include visualization, visual analytics, eye tracking, human-computer interaction, computer
graphics, augmented and virtual reality, and special and general relativity. He is spokesperson of the DFG-funded Collaborative Research Center SFB/Transregio 161 “Quantitative Methods for Visual Computing” (www.sfbtrr161.de), which covers basic research on visualization, including multidimensional visualization.

I will discuss two tracks of methods which are "advanced" in so far as they explore designs beyond classic supervised learning of a given tunable rendering pipeline. The first track makes learning itself subject to learning (meta-learning). While normal learning optimizes parameters of a tunable pipeline, meta-learning optimized the parameters of the learning. I will discuss two instances that meta-learn learning rate and initialization, and another one that meta-learns sampling.
The second track investigates the differentiability of the rendering pipeline itself. We will recall why it is challenging to differentiate through rasterization or ray-tracing. Based on this framework, we will derive methods to optimize over the space of differentiable rasterizers as well as propose a simple and effective way to differentiate the light transport equation --which has a lot of dimensions to (MC) integrate over-- by adding even more dimensions.

Biography:
Professor Tobias Ritschel has received his PhD from Saarland University (Max Planck Institute) in 2009. He was a post-doctoral researcher at Telecom ParisTech / CNRS 2009-10 and a Senior Researcher at MPI 2010-15. Tobias was appointed Senior Lecturer at University College London in 2015 where he was named Full Professor of Computer Graphics in 2019. His work has received the Eurographics Dissertation (2010) and Young Researcher Award (2014). His interests include Image Synthesis and Human Visual Perception, now frequently including applied AI.

Historically, mathematical morphology has primarily focused on the processing and analysis of two-dimensional image data. In this talk, I will survey a number of other areas where mathematical morphology has found fruitful application. I plan to address the following topics.

1. Volume processing and visualization.

Some examples are: morphological pyramids for multiresolution visualization of 3D medical data by maximum intensity projection; connected morphological operators for combined volumetric filtering and visualization; and volumetric segmentation and visualization by morphological active surface models or level sets.

2. Visual exploration of high-dimensional data.

Here there are numerous applications, such as watershed algorithms for fast reconstruction and visualization of brain networks; finding and exploring relevant subspaces in high-dimensional astronomical data; or filtering and visualization of tensor fields such as diffusion tensor imaging (DTI) data.

Visualization plays a critical role in the scientific understand of the massive streams of data from scientific simulations and experiments. Continued growth in performance and availability of large scale supercomputing resources (e.g. exascale and faster over the next decade) enables both increasing simulation resolutions and an increasing number of and breadth of simulation ensemble runs. In the modern scientific process these simulation ensembles are verified for correctness and then validated with experimental ensembles to increase our overall scientific knowledge. Effective visualization of the verification and validation (V&V) prices is a significant challenge. Additional challenges include the significant gap between supercomputing processing and data storage speeds. In this talk, I will highlight current accomplishments from the U.S. Exascale Computing Project to address these challenges include high-dimensional visual analysis, comparative visualization, in situ visualization, portable multi-threaded visualization algorithms, and automated techniques. I will present a vision of a set of needed initiatives to support the visual understanding of the complex and evolving modern scientific process.

Image Caption: This visualization is one member of a visualization ensemble used to study the potential effects of an asteroid impact in Earth's oceans. The study explores  the effects of varying the size of the asteroid, speed of the asteroid, and the angle of impact.

Abstract:
We propose diverse canonical parameterizations of 2D-curves. For instance, the arc-length parameterization is canonical in the sense that any open curve can be parameterized by arc-length in a unique way. We consider other natural parameterizations like the parameterization proportionnal to the curvature of the curve. Both aforementionned parameterizations are very natural and correspond to a natural physical movement: the arc-length parameterization corresponds to travelling along the curve at constant speed, whereas parameterization proportionnal to curvature corresponds to a constant-speed moving frame in SO(3). Many other canonical parameterizations are considered, interpolating between arc-length parameterization and curvature-length parameterization. The main idea is that to any strictly increasing function is associated a natural parameterization of 2D-curves, which gives an optimal sampling, and which can be used to compare unparameterized curves in a efficient and pertinent way. If time permits, the link to infinite-dimensional geometry will be explained. An application to point correspondence in medical imaging will be given.

Bio:
Alice Barbora Tumpach is an Associate Professor in Mathematics since 2007 (University of Lille, France, currently on leave) and P.I. of a FWF Grant entitled "Banach Poisson-Lie groups and integrable systems" since 2021. She obtained a habilitation in mathematics in 2022, a PhD in mathematics in 2005 at Ecole Polytechnique, France, and spent two years at Ecole Polytechnique Fédérale de Lausanne as a Post-Doc. She started her studies in mathematics and physics and obtained a Bachelor in each of these specialities at ENS Paris. Her research interests include infinite dimensional geometry, Lie groups and applications to Shape Analysis. She is author and co-author of several publications in international journals (TPAMI, Communications in Mathematical  Physics, Journal of Functional Analysis, Annales de l'Institut Fourier) in the above fields. She served as reviewer for many journals, including TPAMI, Mathematische Annalen, Journal of Mathematical Physics, Journal of Mathematical Analysis and Appplication, Journal of Differential Geometry...On the other hand, she has three children and loves chilli chocolate.

Recent advances in machine learning for 3D data have revolutionized the fields of computer vision and computer graphics. These techniques have enabled researchers to train neural network architectures directly from 3D data. Among these technologies, neural networks for unstructured data or point clouds have gained a lot of attention in the past years since they are able to work with sparse 3D representations, saving large amounts of memory. However, these technologies do not come without a cost. In this presentation, I will talk about the challenges that these networks pose, how to overcome them, and how they can be used to solve different problems in the fields of computer vision, computer graphics, and bio-medicine.

In this talk I will reflect on my time as a PhD student at the University of Bergen and will share my experiences throughout my research exchange at the Harvard University. I will talk about the ups and downs in PhD life and what helped me to pull it through and what I would have wished throughout my study time. Furthermore, I will also put my experiences into perspective and talk about how PhD life looks like at the Harvard University. What are the major differences and similarities and what could we learn from them as well as what could the Harvard University learn from Bergen.

Abstract:

This talk provides different perspectives on using data visualization to assist and inform choices. We face many choices in our personal and professional lives. Computing has made it easy to compile large numbers of options to choose from. Identifying the best solution among such a set is called multi-attribute choice. With no objectively optimal solution present, our human judgment is needed to trade off conflicting goals.

Data visualization is a powerful tool to help us explore and make sense of available courses of action. While many interactive visualizations already live in the context of decision-making, how to design for humans who make decisions with visualized data continues to be a vibrant research area. In this talk, I will touch upon different properties of multi-attribute choices. I will also hint at the role of related disciplines like decision theory. Finally, I will lay out some open visualization challenges along with examples where our visualizations helped learn what level of performance is achievable under which conditions.

Short Bio:

Lena Cibulski is a visualization researcher at Fraunhofer IGD and a PhD candidate at Technical University of Darmstadt, Germany. She received her master’s degree in computer science in 2017 from Otto-von-Guericke University Magdeburg, where she soon found her way into visualization research. She completed her bachelor studies with a six-month stay at the VRVis Research Center in Vienna. Lena is currently a visiting researcher at JKU Linz. Her research is at the intersection between visualization and multi-attribute decision-making, with an emphasis on design studies for engineering applications. Lena conducts industrial and research projects that aim at assisting and informing decisions by using interactive visualization. She is particularly interested in multidisciplinary collaborations to encourage discussions on human factors, methodological aspects, and applications.

Abstract:

The communication and early warning of high-impact weather events (HIWE), their associated risks, and recommendations to the general public, constitute a challenging and continued research topic. These challenges are due to the inherent unpredictability of the weather and the difficulties of quantifying its risk and communicating its uncertainties.

In this talk, I will present our latest research on the visual design of efficient visualization tools to communicate, analyze and quantify HIWE impact, and engage citizens in discussing severe weather events through citizens' participatory science and visualization.

Bio

I am a postdoctoral researcher in the Multimedia and Visualization group, led by Prof. Dr. Renato Pajarola at the Department of Informatics of the University of Zurich (UZH), Switzerland. I received my Dip. Eng. in Computer Engineering (2005) and my Ph.D. (2016) in Computer Science at the University of Buenos Aires, Argentina. Before my current position, I was a postdoctoral researcher at the Data Visualization and Analysis Group (DBVIS) at the University of Konstanz, Germany. I am also a lecturer, with experience teaching information visualization, visual analytics, and geographic information systems.

I currently perform research on visual analytics, guidelines and best practices for visualization in projects that range from geovisualization, multimedia analytics to environmental sciences. Along the years, I have dedicated strong efforts to the research and development of visual tools for operational weather forecasting and analysis of high-impact weather events. The full list of publications can be found here.
I am very interested in environmental science and animal protection activities, and I am a proud member of the Zurich Bird Protection / BirdLife Zurich and the Swiss Bird Protection/BirdLife Switzerland, as well as the green team of volunteers at ZÜRCHER TIERSCHUTZ.