The duration for this prae doc position is 4 years starting in the beginning of June.

Applications including your scientific curriculum vitae, letter of motivation, including ideas for a prospective doctoral project proposal, abstract of master thesis, you Master Degree  /  Diploma, list of publications and evidence of teaching experience (if available) should be submitted no later than May 12th, 2024.

The detailed job call and application procedure can be found at: jobs.univie.ac.at/job/Predoctoral-assistant-Computer-Science/1061568801/

When: Monday, April 29th, 14:00 - 15:00
Where: Währingerstraße 29, SR4, 1090 Vienna

Abstract:
Cinematic scientific visualization combines the rigor of data visualization with the artistic finesse of Hollywood filmmaking, transforming complex scientific concepts into visually compelling narratives. Widely disseminated via museums, documentary films, and social media platforms, these visualizations have garnered a vast global audience, reaching millions of viewers from 8 to 80 years old. In this talk, cinematic visualization will be explained and demonstrated through three lenses: technical challenges, design considerations, and science communication best practices.

Bio:
Kalina Borkiewicz has spent most of the last decade creating cinematic scientific visualizations as part of the Advanced Visualization Lab (AVL) at the National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign. Most recently, she served as Director of the AVL team and also Director of the Visualization Program Office. She has contributed visualizations to numerous documentary films including immersive fulldome and IMAX content. Kalina is currently a PhD student at the University of Utah.

Submissions due: Friday, February 23, 2024

Workshop date: Sunday, May 12, 2024, Hawaii Convention Center, Oahu, HI.

We encourage thought-provoking perspectives, use-cases, and studies of sensemaking activities in both professional and informal contexts.

Areas of Interest:

• What is Sensemaking Today?

• Tools for Sensemaking

• Gen-AI and Sensemaking

• Operational Questions

• Emerging Phenomena

• The Future of Online Privacy, Safety, Trust & Safety

For more details and workshop information, please visit https://sites.google.com/view/chi2024-sensemaking-workshop/home

Looking forward,

Laura Koesten, Dan Russell, Niki Kittur, Pengyi Zhang, and Michael Xieyang Liu

Speaker: Jen Christiansen (Scientific American)
Title: "Special Considerations for Science Graphics (from a journalist's point of view)"
When: Fri 1 Sep, 10:30-11:30
Where: HS 2, Währinger Straße 29, 2nd floor and online in Zoom
 
Abstract: Science graphics are beholden to the same design principles as other types of graphics. But the information they hold is often the product of a process that a lot of people in broad audiences may not be familiar with. It's important to honor the fact that the data you're presenting is both the product of a rigorous study that builds upon past studies, and that interpretations may eventually shift a bit as future research sheds more light on the topic. This talk outlines strategies for addressing three overarching themes that are particularly pertinent to science graphics: honoring complexity, avoiding misinformation pitfalls, and visualizing uncertainty.
 
 
Bio: Jen Christiansen is author of Building Science Graphics: An Illustrated Guide to Communicating Science through Diagrams and Visualizations (CRC Press) and senior graphics editor at Scientific American, where she art directs and produces illustrated explanatory diagrams and data visualizations. In 1996 she began her publishing career in New York City at Scientific American. Subsequently she moved to Washington, D.C., to join the staff of National Geographic(first as an assistant art director–researcher hybrid and then as a designer), spent four years as a freelance science communicator and returned to Scientific American in 2007. Christiansen presents and writes on topics ranging from reconciling her love for art and science to her quest to learn more about the pulsar chart on the cover of Joy Division’s album Unknown Pleasures. She holds a graduate certificate in science communication from the University of California, Santa Cruz, and a B.A. in geology and studio art from Smith College.

Speaker: Jos Roerdink (Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen)
Title: "Data-Driven Visualization of Multichannel EEG Coherence with Functional Units"
When: Thu 29 Jun, 9:30-10:30
Where: SR 7, Währinger Straße 29, 1st floor
 
Abstract: A typical data-driven visualization of electroencephalography (EEG) coherence is a graph layout, with vertices representing electrodes and edges representing significant coherences between electrode signals. To reduce clutter, the concept of functional unit (FU) was introduced as a data-driven region of interest (ROI). To detect FUs, we developed a maximal clique-based method, which is very time consuming, and a much more efficient watershed-based greedy method, thus making interactive visualization of multichannel EEG coherence possible. Group mean coherence maps enable group comparisons. The visualization of (group) FU maps provides a very economical data summary of large amounts of experimental data. The method was applied to case studies on ageing and mental fatigue [1], and can be extended to deal with the case that coherence networks evolve over time [2].
 
References:
[1] M. ten Caat, N.M. Maurits, and J.B.T.M. Roerdink. Data-driven visualization and group analysis of multichannel EEG coherence with functional units.  IEEE Trans. Visualization and Computer Graphics 14(4), 756-771, 2008.
[2] C. Ji, J.J. van de Gronde, N.M. Maurits, and J.B.T.M. Roerdink. Visual Exploration of Dynamic Multichannel EEG Coherence Networks. Computer Graphics Forum 38(1), 507-520, 2019.
 
Bio: Jos Roerdink studied biology and physics at the University of Nijmegen, the Netherlands. In 1983 he obtained his PhD in statistical physics at the University of Utrecht. After a 2-year postdoctoral stay
at the University of California, San Diego, he joined the Centre for Mathematics and Computer Science in Amsterdam, where he started work on image processing and reconstruction. In 1991 he became associate professor at the University of Groningen. In 2003 he was appointed as full professor at the same university with a chair in Scientific Visualization and Computer Graphics, with a secondary appointment at the Neuroimaging Center of the University Medical Center Groningen. He was director of the Johann Bernoulli Institute for Mathematics and Computer Science from 2015-2017, and from 2018-2020 he was director of the newly formed Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence. Since January 1, 2021, he is emeritus professor of the University of Groningen.

In our paper, we address these questions by taking a closer look at how explanations can support understanding of algorithmic systems, using a framework from the learning sciences called the “six facets of understanding”. In a second step, we investigate how this understanding enables people to take agency, meaning, in our case, if people can assess an algorithm in terms of ethical values (such as: Is it fair or not?). We find that the background of individuals strongly affects how they understand a given explanation, take for example the following two quotes and compare which aspects their understanding touches on:

• I see him almost like me. He’s 49, so for me, he would already count as 50+ and should get extra support. [...] I find the algorithm good and the human decision bad, because the human one goes by numbers and is not accommodating, The algorithm just sees 49 and makes the decision. (P17)
• I understand why the algorithm says Group "Low" in this case. If the job-seeker retrains and can explain his career well I think he could find work, but needs support to do so [...] Personally, I would perhaps not rate him that way, but I think it’s good that he gets into the group. (P6)

The first participant mentions their own personal circumstances and speaks about the difference between human and algorithmic decision-making, while the second takes a more analytical perspective on the decision and the job-seeker’s situation. Two very different approaches that are both valid in assessing the algorithm.

We recommend that future explanation approaches should consider these individual approaches to understanding as variables in their design and further highlight the value of the dialogue explanation modality to invite participants to share more of their understanding and experiences. For our other contributions, we invite you to take a look at the full paper, which is available here:

On the Impact of Explanations on Understanding of Algorithmic Decision-Making
Timothée Schmude, Laura Koesten, Torsten Möller, Sebastian Tschiatschek
In 2023 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’23), June 12–15, 2023, Chicago, IL, USA. ACM, New York, NY, USA, 12 pages.

https://vda.cs.univie.ac.at/research/publications/publication/7661/

Younesy, Hamid, Joseph Pober, Torsten Moeller, and Mohammad M. Karimi. "ModEx: A general purpose computer model exploration system." Frontiers in Bioinformatics 3: 44. https://doi.org/10.3389/fbinf.2023.1153800

Video summaries of scientific publications have gained more and more popularity over the last years, requiring many researchers to familiarize themselves with the tools and techniques of video production, which can be an overwhelming task. This paper introduces a video structuring framework embedded into the authoring tool Pub2Vid. The tool supports users with the creation of their video outline and script, providing real video examples and recommendations based on the analysis of 40 publication summarization videos which were rated in a user study with 68 participants. Following a four-tier evaluation methodology, the application’s usability is assessed and improved via amateur and expert interviews, two rounds of usability tests and two case studies. It is shown that the tool and its recommendations are particularly useful for beginners due to the simple design and intuitive components, as well as suggestions based on real video examples.

Link to the paper: https://vda.cs.univie.ac.at/research/publications/publication/7703

Pub2Vid-App: https://pub2vid.web.app

How do different audiences make sense of climate change data visualizations and what do they take away as a main message? In this workshop paper, the authors are taking a closer look at this question by comparing takeaway messages of two exemplary data visualizations formulated by experts and lay persons. Thereby, they are building on the results of a previous study, for which they conducted semi-structured interviews with 17 experts in the fields of climate change, science communication, or data visualization, as well as with six lay persons with no professional background in either of these areas. Through a thematic analysis, they observed differences in the formulated takeaway messages regarding the included contents, the length and abstraction of messages, and the sensemaking process between and among the participant groups.

Adaptive Precision Training (AdaPT): A dynamic quantized training approach for DNNs
Authors: Lorenz Kummer, Kevin Sidak, Tabea Reichmann, and Wilfried Gansterer

https://epubs.siam.org/doi/10.1137/1.9781611977653.ch63 

Papersː 
Talking datasets – Understanding data sensemaking behaviours
Dataset Reuse: Toward Translating Principles to Practice

When:  February 15^th 2022, 18:30 CET 

Where: https://www.youtube.com/watch?v=8VJmR-3lTac 

For more information, please contact: Charlotte Zott <charlotte.zott@univie.ac.at>

Open Data Showcase: 

https://www.parlament.gv.at/recherchieren/open-data/showcases/ 

Central European University, Quellenstraße 51-55, 1100 Vienna, Austria (hybrid event) 

Program: https://datastoriesceu.org/program 

Project Website: https://talking-charts.vda.univie.ac.at/ 

Abstract: 

Talking Charts: How are everyday visualisations produced and understood?

Urgent messages with immediate effects on public life, i.e. the exponential growth of COVID-19 or the pace of climate change, are often communicated via data visualizations. It is not clear whether public interpretations of these visualisations match the messages their creators aim to convey. The same is true for data visualisations in science; it is not a given that experts will interpret them the way they are intended.

In this talk, we introduce Talking Charts, a three-year mixed methods project which explores how charts related to climate change and COVID-19 are created and understood by researchers and public audiences. We give an overview of the project and discuss initial results and studies focusing on everyday visualisations. 

Where? on-site @ Seminarraum 9, Währinger Straße 29 2.OG  

Latest information on COVID-19 hygiene rules and rules of conduct: https://event.univie.ac.at/ 

Abstract:
Data visualization research often seeks to help solve real world
problems across application domains, from biomedicine to engineering.
There is considerable merit in such endeavors, which often help
advance knowledge in these application domains. Beyond these
contributions, as we work alongside domain experts, we also have a
unique opportunity to observe qualitatively and analyze how these
clients interact with the data through our tools and paradigms. Thus,
we have a rare opportunity to better ground data visualization theory
on these observations. In this talk, I will examine how working with
real world data and problems can point out specific gaps in our
theoretical knowledge, can challenge underlying assumptions in the
data visualization field, and can lead to new insights and theoretical
guidelines. I will focus on several theoretical contributions grounded
in this experience, from activity-centered design to visual
scaffolding, the details-first paradigm, and visual explainability in
artificial intelligence. Last, I will reflect on the lessons learned
through this experience, with particular emphasis on the barriers our
field poses to new theoretical contributions.

Bio:
Liz Marai is an associate professor of Computer Science at the
University of Illinois at Chicago. Her research interests go from
visual-system related problems that can be robustly solved through
automation, to problems that require human experts in the
computational loop, and the principles behind this work. Marai's
research has been recognized by multiple prestigious awards,
including: a Test of Time award from the International Society for
Computational Biology, and several Outstanding Paper awards, along
with her students; an NSF CAREER Award and multiple NSF awards; and
several multi-site NIH R01 awards as a lead investigator. She has
co-authored scientific open-source software adopted by thousands of
users, and she is a patent co-author, whose algorithms have been
embedded in a medical device.

univis.univie.ac.at/ausschreibungstellensuche/flow/bew_ausschreibung-flow

Project “Talking charts”

The project combines perspectives and methods from Computer Science and Science & Technology Studies to explore how charts related to climate change and COVID-19 are created and understood both by researchers and public audiences. 

We welcome candidates with a background in Computing (with a specialization in Human Computer Interaction, Data Visualisation or Data Science) or in Information Science, Science and Technology Studies, or related areas. Read more about the project and the job description here: 

• Project-specific PhD (3-years): https://tinyurl.com/35fpetkd 
• Open topic PhD (4-years): https://tinyurl.com/yhbh66m9 

Please get in touch via email with Laura Koesten (laura.koesten@univie.ac.at) or Kathleen Gregory (kathleen.gregory@dans.knaw.nl)  if you have any questions about the role descriptions or application process for the Talking Charts project.

Project “Interpretability and Explainability as Drivers to Democracy”

The project is about machine learning models that are used for making decisions with significant societal impact in democratic societies. In particular, the project's aim is to enable informed evaluation of machine learning models and their usage by the electorate through interpretability and explainability of the used models and communication of these models and their roles in the decision making process in suitable forms. The underlying decision making process is considered to involve different stakeholders with different levels of expertise and achieving the project's aims requires the development of novel machine learning models, visualization approaches, and guidelines.

• Project-specific PhD: https://tschiatschek.net/opening.html

Please get in touch via email with Sebastian Tschiatschek (sebastian.tschiatschek@univie.ac.at) if you have any questions about the role descriptions or application process for the Interpretability and Explainability project.

Our research groups are also filling the following related PhD positions:

• Toward explainable models for environmental geoscience: https://bit.ly/2UhPry2 
• Toward explainability of deep-neural-network models: https://tinyurl.com/5ck4fned 
• On reinforcement Learning or probabilistic models: https://tinyurl.com/4ud9jewv 

Please get in touch via email with Torsten Möller (torsten.moeller@univie.ac.at) with questions about these roles.

Parallel coordinates are an established technique to visualize multivariate data. Since these graphs are generally hard to read, we need interaction techniques to judge them accurately. Adding to the existing brushing techniques used in parallel coordinate plots, we present a triangular selection that highlights lines with a single click-and-drag mouse motion. Our selection starts by clicking on an axis and dragging the mouse away to select different ranges of lines. The position of the mouse determines the angle and the scope of the selection. We refined the interaction by running and adapting our method in two small user studies and present the most intuitive version to use.

Paper: https://diglib.eg.org/bitstream/handle/10.2312/evs20211064/109-113.pdf 

Video-Link: https://www.youtube.com/watch?v=xXXlKQJTfOQ&t=3687s 

https://univis.univie.ac.at/ausschreibungstellensuche/flow/bew_ausschreibung-flow?_flowExecutionKey=_c253C4558-99AC-E514-E5E1-730B8850E510_kD15974C6-5012-581A-7B99-58E4C314C43B&tid=86689.28 

You can find the paper here: https://dl.acm.org/doi/abs/10.1145/3411764.3445145 
You can also watch the video of the presentation at CHI on youtube: https://www.youtube.com/watch?v=ICpZj67uPYQ 

Link to the paper: https://arxiv.org/abs/2101.01036 

Link to the data repository: 

https://ieee-dataport.org/open-access/ieee-vis-figures-and-tables-image-dataset 

Please have a look at the project website as well as the introduction video for further information:

https://ivan.ai

https://youtu.be/dRK40JgeDs8

Deadline: 31.07.2020

1 PostDoc position in the area of visual data analysis (starting asap)
Link: https://tinyurl.com/y9jjap2c

Deadline: 31.07.2020


3 fully-funded PhD positions in the area of transparent and
explainable models (with a focus on deep neural networks as well as clustering)
Starting Oct 1st, 2020

Deadline: 31.07.2020

* with a focus on Mathematical Solutions:
Link: https://tinyurl.com/y7yt44sn

* with a focus on Data Mining Solutions:
Link: https://tinyurl.com/yafeydnk

* with a focus on Visual Analysis Solutions:
Link: https://tinyurl.com/ybwc5hg3

1 platform coordinator for the platform (admin position 25h per week)
Link: https://tinyurl.com/y73ggpo5

Deadline: 31.07.2020

We also want to bring to your attention 2 Tenure Track positions at the Faculty for Computer Sciences at the University of Vienna:

* for the field of Stochastic Methods for Data Science

Link: https://tinyurl.com/y77a22lf

Deadline: 23.09.2020

* for the field of Management of Massive Data

Link: tinyurl.com/ybsf2hym

Deadline: 23.09.2020

The research platform Data Science @ Uni Vienna has been in existance since 2018 and is growing rapidly. With 24 faculty members across 7 faculties, we are a very interdisciplinary group. We organize lecture series, summer schools, scientific meetings, retreats, and coordinate the three new master programs in Business Analytics, Data Science, and Digital Humanities at the University of Vienna.

While the University of Vienna is the oldest German speaking university around, our research and communication happens in English. Vienna is a very multi-cultural and international city as well as very welcoming to international researchers and visitors. In addition, Vienna is one of the top rated cities to live in and a cultural hot spot of Europe.