Merge pull request #1881 from cagatayTurkay/patch-30

Update panels.md

content/info/panels.md

@@ -28,10 +28,10 @@ Location: Room 101+102
 <!-- Thursday, October 29: 12:00pm-1:30pm MDT -->
 
 **Organizers:** Mennatallah El-Assady (ETH Zürich), Jürgen Bernard (University of Zurich)
-<br>**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)
+<br>**Panelists:** Benjamin Bach (University of Edinburgh),  Rita Borgo (King's College Londin), Leilani Battle (University of Washington), Alex Lex (University of Utah),  Arjun Srinivasan (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! 
+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!
 
 ## <a name="panel-möller" href="https://vis4ml.github.io/vispanel" > How should VIS4ML Redefine Itself in the Rapid Evolution of AI?</a>