Kolloquiumszyklus - previous dates

Abstract:

Research in visualization is often motivated by the endeavor to improve on the illustration of data: in order to better communicate data to others and to gain deeper insights into data models, possibly from a variety of data sources. Individuals with varying backgrounds and statistical knowledge need to gain insights from data and models that are communicated visually either in public media or in data analysis tools. However, various data and models require special visualization techniques that build on human capabilities of perceiving the data and models well. In this talk,, I will focus on two challenges: 1) visual communication of data with large value ranges that span across several orders of magnitude. Examples include Covid-19 cases or meteorologic particles and 2) visual model validation and estimation - the ability of humans to validate computer-generated models and estimate models from the data visually. Examples include linear regressions of house prices or patient data. We present various techniques and results of user studies that provide insights into capabilities and limitations of human-centered visual data communication and model building. 

Speaker Biography:

Tatiana von Landesberger is a full professor of Computer Science – Visualization at University of Cologne, Germany. She received a PhD in 2010 and finished her habilitation in 2017 at TU Darmstadt, Germany. Her research focuses on information visualization and visual analytics of spatio-temporal and network data from various domains such as biology, medicine, finance, transportation, journalism or meteorology. Her recent research addresses the challenges of perception and cognition of visualization as well as visual analysis of disease spreading. She regularly publishes at top international conferences and has received multiple awards. Tatiana has served in program committees and organization committees for IEEE VIS, EuroVis, VMV and other venues. Recently, she was full paper chair at EuroVis and is now member of EuroVis steering committee and associate editor of Computer Graphics Forum.

With the increasing adoption of machine learning models across domains, we have to think about the roles of humans and AI to build trustworthy systems. In the last few years, my collaborators and I have created a series of tools that utilize visualization and visual user interaction to help investigate behavior of machine learning models. I will present a quick intro to trustworthy AI in general and will show some of these tools that demonstrate how intuitive ideas can foster human-AI collaboration − and therefore contribute to building trustworthy systems.

Abstract

In this talk, I will present different frameworks we developed using the geometric properties of Delaunay triangulation to process hand-drawn sketches. The presentation will focus on two key areas: sketch simplification (cleaning up rough sketches) and line art colorization (adding color to hand-drawn sketches, even those with gaps). I will demonstrate how our methods combine Delaunay triangulation with interactive user input to create powerful tools for enhancing hand-drawn sketches bridging the gap between initial rough concepts and refined digital representations. By the end of the presentation, you'll understand how our simple sketch-processing techniques can significantly benefit artists, designers, and digital content creators.

Bio 

Amal Dev Parakkat has been working as a tenured Assistant Professor (Maitre de Conferences in French) at LTCI-Telecom Paris, Institut Polytechnique de Paris, since September 2021. Parakkat was born in Kerala, India. He is interested in practical algorithms for digital content creation tasks with the main focus on Sketch-based interfaces. He also works on fundamental problems in digital geometry processing, including reconstruction and meshing. He leads the ANR SketchMAD project on novel algorithms for sketch-based modeling (2024–2028) and heads the IGD masters at the Institut Polytechnique de Paris. He received the SMI Young Investigators Award 2024 and regularly serves on program committees of Eurographics, Pacific Graphics, Shape Modeling International, and Computer Graphics International. He will be the technical paper chair of Expressive 2025.

This exclusive Spectacles Demo session offers a rare opportunity to explore these AR glasses firsthand. Featuring a see-through stereo display with optical waveguides and liquid crystal on silicon (LCoS) miniature projectors, the glasses provide a 46° diagonal field of view and 37 pixels per degree resolution. The device has a 13ms latency ("motion to photon"), 6DoF tracking, 60Hz rendering, and 120Hz late stage reprojection frequency. Interact via full hand tracking, voice recognition, and an optional mobile app controller, all within a standalone untethered design. Powered by two Snapdragon processors for distributed computing, it allows up to 45 minutes of continuous runtime and includes both full color and infrared computer vision cameras. Weighing 226g, the glasses have a flexible folding temple design and an optional prescription insert. 

Advancements in 3D spatial biology have unlocked unprecedented opportunities to study biological systems across scales, from individual cells to functional tissue units. However, the complexity of these datasets demands innovative tools for exploration, interpretation, and hypothesis generation. Large Language Models (LLMs) offer transformative potential as interactive agents to guide and empower users—both laypersons and experts—in navigating these multidimensional datasets. By integrating LLMs with Extended Reality (XR) technologies, we can immerse users in 3D tissue maps, providing intuitive, spatially aware exploration of intricate biological structures. This approach allows users to observe, manipulate, and analyze 3D spatial data in an interactive and visually rich environment. Furthermore, combining 3D tissue maps with visual analytics can uncover patterns, network structures, and functional relationships within tissue architectures, enabling scalable analyses from the resolution of single cells to complex tissue-level networks. This synergy of LLMs, XR technologies, and advanced visual analytics redefines the boundaries of spatial biology, empowering users to bridge data complexity with actionable insights, fostering new discoveries in the life sciences.
 

Abstract

Visual representations play an essential role in communicating complex scientific phenomena. Effective science communication often requires a team effort, where contributors work together to ensure a faithful translation of scientific evidence, understanding of communication needs, and skillful application of visualization design principles. In this talk, I reflect on two research projects that explore how collaboration—or its absence—impacts the design process of science communication products.

Bio

Amy is a scientifically-minded illustrator, designer, and visualization researcher. She contributes to projects that foster an appreciation and understanding of science as a PhD fellow within the VisGroup at the University of Bergen and previously Science Visualization Lab at the University of Toronto.

Abstract

Anatomy has become mainstream. Visuals of human internal structures like the heart, brain, and bones can adorn today’s posters, t-shirts, and totes, ranging from highly abstracted to hyper-accurate styles. What’s wild is that much of society can recognize and read these structures without a second thought. In this talk, I will discuss the history of medical visualization, not as a long chain of increasing complexity and sophistication, but as data-driven views of the bodies that serve as conversational artifacts developed in a particular social, technical, and cultural environment. Reflecting on these various contexts is useful, even necessary, for us to recognize our own contemporary contingencies as both readers and creators of (medical) visualizations in the present and future.

Bio

Laura Garrison is an Associate Professor of Visualization in the Institute for Informatics at the University of Bergen and affiliated with the Mohn Medical Imaging and Visualization Centre (MMIV) and the Centre for Data Science (CEDAS) in Bergen, Norway. Her research investigates the processes and assumptions designers and developers make when crafting visualizations of complex data, and their impact on audience engagement and behavior.

She received her PhD in Visualization from the University of Bergen for her work on multiscale visualization of human physiology. She was awarded the Dirk Bartz Prize for Visual Computing in Medicine in 2023 and the Karl-Heinz Höhne (MedVis) Award in 2021. Prior to her PhD, she worked as an artist and content director in medical education technology start-ups in Chicago, Silicon Valley, and New York City.

Abstract

I will briefly present an overview of my PhD research, which focuses on developing interactive explainable AI methods for the robust development of deep image-to-image translation models. My work primarily targets tasks with high-dimensional image outputs, which are significantly more complex than the classification tasks typically addressed in the literature. These methods are particularly relevant to generative and medical imaging applications.

In this talk, I will highlight my work on diffusion models, a class of deep generative models recognized for their ability to produce diverse, high-quality samples. Diffusion models iteratively transform noise into refined images with deep learning, and understanding this temporal data evolution is crucial for interpreting the model's learning process. However, the high-dimensional and complex nature of this evolution poses significant challenges. To address these challenges, we developed a novel method that provides a holistic view of this generative data evolution in diffusion models. It preserves the iterative context of the generative process by sampling the generative space with tailored prompts and extracts relevant attributes from intermediate outputs. It introduces an evolutionary embedding algorithm that clusters semantically similar elements, organizes them by iteration, and aligns an instance's elements across iterations to enable studying the evolution of data. We propose rectilinear and radial layouts for effective exploration. We show how our method was applied to Stable Diffusion, offering valuable insights into its generative process.

Note: A preprint is available on arxiv.

Bio

Vidya Prasad is a final-year PhD candidate at Eindhoven University of Technology, advised by Prof. Anna Vilanova and Dr. Nicola Pezzotti. Her research focuses on developing explainable AI and visual analytics methods to enhance the safety and reliability of deep image-to-image translation models, particularly for generative and medical applications. With a strong background in explainable AI, deep learning, medical imaging, and software development, Vidya is passionate about creating robust and impactful AI solutions. She has contributed to several papers and patents and was a visiting researcher in Prof. Hanspeter Pfister's group at Harvard. Before her PhD, Vidya worked as a deep learning researcher at Philips and as a software developer at Amazon. She holds an MSc in computer science from the National University of Singapore.

Abstract

Generative adversarial networks (GANs) have seen widespread use in computer graphics and computer vision over recent years. However, they come with several drawbacks, including mode collapse, unstable training, and ambiguous convergence criteria. Moreover, conditional GANs often generate samples with limited diversity, posing significant challenges for many applications. In this talk, I will introduce normalizing flows, a unique type of generative network, discussing both their advantages and limitations. Following this, I will present various problems we have addressed using normalizing flows, such as icon colorization, basketball play synthesis, shuttle landing distribution modeling, noisy label training, and substituting datasets with normalizing flows.

Bio

Yu-Shuen Wang (王昱舜) is a professor of the Department of Computer Science at National Yang Ming Chiao Tung University. He received his PhD degree from the Visual System Laboratory, National Cheng Kung University, Tainan, Taiwan, ROC, in 2010. Currently, he lead the Computer Graphics and Visualization Lab at the Institute of Multimedia Engineering. His research interests include Computer Graphics, Computer Vision, Data Visualization, and Machine Learning. He was honored with the prestigious Wu Da-Yu Memorial Award and the NCTU EECS Outstanding Young Scholar Award in 2016.

Abstract

Humans have a long history of developing tools for achieving their goals. This talk will review some our own research and present potential directions of enhancing human capabilities. The talk will conclude on what humans are good at, what much machines are good at, and how both can achieve more in collaboration.

Bio

Florian Michahelles is a full professor at TU Wien for ubiquitous computing where he studies human-machine collaboration, exploring the use of machine learning and artificial intelligence in supporting human users for their tasks and activities while keeping human users in control. He has been an Associate Editors In-chief for IEEE Pervasive Computing, Associate Editor for IMWUT for more than 10 years. He is the chair of the steering committee for the IoT Conference.  His research has been covered in over 250 scientific articles with 7500+ citations and an h-index of 41. Together with his team he has received the best paper award from IoT2023, and a Ten Year Impact Award at 21st International Conference on Mobile and Ubiquitous Multimedia.

Before joining TU Wien Informatics, he was the research group head of artificial & human intelligence at Siemens in Berkeley. Before, he led the Auto-ID Labs at ETH Zurich in the field of RFID, IoT Architecture, and mobile applications. In 1999 he was an MIT Sloan Visiting Fellow and in 2010 a visiting researcher at Keio University.  He received a Ph.D. from ETH Zurich in December 2004 and a M.Sc. (Dipl-Inf.)  from the Ludwig-Maximilians University Munich in Computer Science.

Abstract

in this talk, I will give an overview of my own and my student's work from the visualization and visual analytics group of the Eindhoven University of Technology, The Netherlands. One focus of our group is to develop new visual representations and interaction techniques for the analysis of dynamic multivariate networks. Another focus area is visualization and interaction for explainable artificial intelligence (XAI). For both areas, a variety of examples are shown using animations and demos. Furthermore, challenges for future exploration are presented.

Bio

I am an Assistant Professor of Visual Analytics at the Eindhoven University of Technology working on Visual Analytics techniques for Networks, Events, and Explainable AI. For my PhD (cum laude) research, I developed visual analytics tools and techniques for the exploration of dynamic multivariate networks aimed at supporting both the data scientist and layman users. After and during my PhD I worked at SynerScope as VP engineering, leading the development team. I then spent time at Philips Research working on Artificial Intelligence solutions as product owner, leading a team of 15+ research scientists. My research goal is to make visual analytics easier and available to a broader audience. Artificial Intelligence (AI) is here to stay and visual analytics enables it to enhance model trust, reduce bias and increase usability and transparency.

Abstract

Our modern ability to acquire and generate vast amounts of data can potentially enable rapid progress in science and engineering, but we may not live up to that promise if our ability to create data outstrips our ability to make sense of that data. Human-centered visual computing tools are essential to gain insights into data by combining computational and statistical analysis with the power of the human perceptual and cognitive system and enabling data exploration through interactive visualizations. In this talk, I will present our work on visual computing in Connectomics, a new field in neuroscience that aims to apply biology and computer science to the grand challenge of determining the detailed neural circuitry of the brain. I will give an overview of the computational challenges and describe visual computing approaches we developed to discover and analyze the brain's neural network. The key to our methods is to keep the user in the loop, either for providing input to our fully automatic reconstruction methods, for validation and corrections of the reconstructed neural structures, or visual analytics of the resulting complex networks. The main challenges we face are how to analyze petabytes of image data in an efficient and scalable way, how to automatically reconstruct very large and dense neural circuits from nanoscale-resolution electron micrographs, and how to analyze the brain's neural network once we have discovered it.

Speaker Bio

Jakob Troidl is a Ph.D. candidate in computer science at Harvard University, advised by Prof. Hanspeter Pfister. In the summer, he will join the lab of Dr. Srinivas Turaga at Howard Hughes Medical Institute (HHMI), Janelia, as a visiting scientist. Jakob is broadly interested in data visualization and applied machine learning, especially with applications in computational neuroscience. His research focuses on building scalable interactive visual analysis tools and neural implicit representation learning approaches to analyze the hidden architecture of the brain. Jakob received an M.Sc. in visual computing in 2021 and a B.Sc. (with Honors) in medical informatics in 2019, both from TU Wien, Austria.

Abstract

Event sequence data occurs in many domains, ranging for example from healthcare to factory data and is closely related to process mining data. Event data are discrete activities that occur over time with a event category, timestamp, possible other data attributes and a possible duration. These events are chronologically ordered in time and happen to an entity, also called a case. For example, medication events over time of a patient (case). Multiple of these sequences form a sequence collection, often sharing a similar topic, e.g., patients with the similar diseases.  There are still several challenges for analysing this data and users’ domain-knowledge is needed to get relevant insights out of the data. For example, how to deal with many sequences, long sequences, high dimensional events, or how can visualization help with process mining problems. In my PhD, we try to address several of these challenges related to scalability (long sequences or many event attributes), flexibility (how do we define a sequence), comparison (of sequences and visualization methods), conformance checking, and streaming sequences (human performance).

Bio

I am a PhD candidate from the Netherlands (Eindhoven University of Technology (TU/e)) in the visualization/visual analytics group from Anna Vilanova and am currently in Vienna for two months to collaborate with Silvia Miksch. I am in my fourth year of my PhD focused around the visualization of event sequence data. Stef van den Elzen and Anna Vilanova are my supervisors. I also studied at the TU/e. I originally have a bachelor in Industrial Design. Afterward, I did a double master in Industrial Design and Computer Science. I live in Veldhoven (village attached to Eindhoven) with my boyfriend and our two pet birds. My hobbies include traveling, (beach) volleyball, playing electrical guitar, and meeting friends.

Abstract: 

Predictive rendering is normally thought of as a technology stack which is mainly useful for virtual prototyping of appearance critical objects. However, such technologies also turned out to be surprisingly useful for high quality movie VFX. After giving an overview of the research portfolio of our group in Prague, I will give a brief overview of which aspects make predictive rendering appealing to the VFX industry, and outline a few technological advances of the past years.

Bio: 

Alexander Wilkie is an associate professor at Charles University in Prague, where he leads the Computer Graphics group. He also has twice been a visiting professor at the research labs of Weta Digital in New Zealand, and has made contributions to the technology stack used by them for major movie productions.

Abstract

Efficiently simulating material appearances is a cornerstone in generating realistic imagery in content creation. While the overarching goal remains consistent across industries, the specific requirements and methodologies diverge significantly based on the intended application.

In this talk, we will discuss the divergent strategies employed by different industries in tackling the challenge of material simulation. We will discuss how modern VFX content creation pipelines are built and investigate some of their unique design choices and their implication on appearance modelling. Additionally, we will look at the transformative potential of neural network-driven approaches, offering insights into their potential to shift traditional paradigms of material representation and the novel research challenges they will bring.

Bio

Andrea Weidlich is a principal researcher and joined Nvidia in 2022 where she is part of the realtime graphics research team. Before, she worked for eight years at Weta Digital where she designed the material system attached to Weta’s proprietary physically-based renderer, Manuka, and spent several years in the automotive industry.

Her main research areas are appearance modelling and material prototyping as well as spectral image synthesis. Andrea holds a Master of Arts in Applied Media from University of Applied Arts, Vienna and a Ph.D. in Computer Science from Vienna University of Technology.

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.