When: Wednesday, October 25, 2023 3:45 PM-5:00 PM AEDT (UTC+11)
Location: Room 101+102
Organizers: Carolina Nobre (University of Toronto), Cindy Xiong (University of Massachusetts), Joshua A Levine (University of Arizona), Emily Wall (Emory University), Dominik Moritz (Carnegie Mellon University), Evanthia Dimara (Utrecht University)
Panelists: Leni Yang (Hong Kong University of Science and Technology), Cindy Xiong (University of Massachusetts Amherst), Dominik Moritz (Carnegie Mellon University), Joshua A Levine (University of Arizona), Evanthia Dimara (Utrecht University)
In this panel, we will discuss academic life and share advice and experience around navigating the complexities of an academic career. These are important for the whole community to reflect more broadly and are particularly inspirational for early career researchers and students. The panelists include a broad spectrum of academics, which includes researchers in the US, Canada, and Europe, from small private schools to larger public institutions, and both pre-and post-tenure. The panel will discuss common challenges in being an academic including their own experiences in handling these challenges. Topics will include managing the amount of freedom often afforded by a faculty position, pushing back against the eternal pursuit of ”work-life balance”, particularly for women, navigating interdisciplinary collaborations, and considerations beyond CS rankings when choosing an academic home.
When: Wednesday, October 25, 2023 10:45 AM-12:00 PM AEDT (UTC+11)
Location: Room 101+102
Organizers: Mennatallah El-Assady (ETH Zürich), Jürgen Bernard (University of Zurich)
Panelists: Benjamin Bach (University of Edinburgh), Rita Borgo (King’s College Londin), Leilani Battle (University of Washington), Alex Lex (University of Utah), Matthew Brehmer (Tableau Research), Emily Wall (Emory University)
Existing characterizations of Visual Analytics (VA) echo the strengths of combining interactive visual data representations and algorithmic models to enable humans making data-driven decisions effectively. For about 20 years, VA was one of three pillars in the interactive data analysis and visualization (VIS) community. Generation after generation, the VA community has evolved its understanding of research problems and, along the way, contributed various techniques, applications, and research methods. While some developed techniques have stood the test of time, we will consider what else needs to be remembered or even revitalized from the good old days in this panel. Further, VA is currently facing exciting times, with great changes and trends within and outside the community. In this panel, we want to analyze current research trends in VA and discuss our most exciting ideas and directions. Looking ahead, it can already be anticipated that the future of VA is subject to change. Following productive and successful panels at EuroVA 2023 and EuroVis 2023, in this panel, we want to continue mapping out future research directions for our community, with an emphasis on VA. Along the lines of the past, the present, and the future of VA, the guiding theme of our interactive panel will be three types of (provoking) statements: (i) In the good old days, I liked when we did … (ii) Currently, a most exciting trend is … & (iii) In the future, we will be doing … Come and join us to reflect on past and present trends, daring a look ahead to an exciting future for the interactive data analysis and visualization community!
When: Thursday, October 26, 2023 3:45 PM-5:00 PM AEDT (UTC+11)
Location: Room 101+102
Organizers: Dylan Cashman (Brandeis University), Junpeng Wang (Visa Research), Qianwen Wang (University of Minnesota)
Panelists: Duen Horng (Polo) Chau (Georgia Tech) Mennatallah El-Assady (ETH Zürich), Liang Gou (Bosch), Ross Maciejewski (Arizona State University), Dominik Moritz (Carnegie Mellon University), GPT-4 (Offline-Panelist)
We propose a panel to discuss the changing role of visualization in the development and deployment of machine learning models in light of the rapid evolution of artificial intelligence (AI). Visualization for machine learning (VIS4ML) has been a thriving research area within the visualization community because of the need for better affordances and representations to enable broad groups of stakeholders to interact with and interpret machine learning models. However, recent advancements in AI are changing our understanding of the capabilities of machine learning models, both in performance and in their ability to interact with the general population. In the light of these advancements, we feel it is an important time for the visualization community to consider how the opportunities for visualization have changed. We have gathered a diverse set of panelists from both academia and industry, with varying levels of experience. We hope that providing a multitude of perspectives will shed light on new opportunities for visualization research, while providing context on the natural evolution of the field over the last few decades. The panel format will begin with introductory statements from each panelists. Then, through a set of open ended questions, we will ask panelists to have an open discussion about which types of stakeholders, use cases, and steps of the modeling pipeline they expect to change the most. The panel will conclude by asking each panelist to share where they feel the best opportunities are for VIS4ML research in the medium term future.