Bio：Dr. Jian Zhao is an Assistant Professor in the Cheriton School of Computer Science, University of Waterloo, where he directs the WatVis (Waterloo Visualization) research group. He is also a member of the Waterloo Artificial Intelligence Institute (Waterloo.ai). His research lies in the intersection of information visualization, human-computer interaction, and data science. He is dedicated to developing advanced interaction and visualization techniques that promote the interplay between humans, machines, and data. Dr. Zhao received his Ph.D. from the Department of Computer Science, University of Toronto. He is the recipient of several awards such as NSERC Discovery Grant Accelerator Award and seven best paper or honorable mention paper awards at top-tier venues (e.g., VIS, CHI, and MobileHCI). He has served on the program committees and has taken critical roles (e.g., Publication Chairs and Paper Chairs) in the organizing committees of many world-class conferences such as VIS, CHI, PacificVis, and ChinaVis. In addition, he has experiences working at other leading industry labs including Microsoft, IBM, and Adobe Research. Dr. Zhao holds more than a dozen patents and some have successfully generated impact on products. His research has been covered in various media outlines including The Record, CBC News, and CityNews. More information can be found on his website: www.jeffjianzhao.com.

Title：Towards More Holistic Environment of Data, Computers, and Humans

Abstract：Every day, we are continuously generating a huge amount of data in different forms. While many computational methods have been proposed, a tremendous number of problems are still ill-defined, vague, and exploratory, requiring human engagement and supervision. This results in a large gap between users and the data as well as the analytical models applied. My research takes a step to address this issue using advanced interaction and visualization techniques. In this talk, I will discuss how these techniques can help users understand and communicate abstract data and insights. I am going to showcase a range of example projects situated in a range of real-world applications, such as data analysis, data gathering, presentation, graphics design, and mobile communication.

Bio： Yalong Yang is joining the School of Interactive Computing at Georgia Tech as an Assistant Professor in 2023 Fall. He has been an Assistant Professor at Virginia Tech from August 2021. Prior to this, He was a Postdoctoral Fellow in the Visual Computing Group at Harvard University, and received his Ph.D. from Human-Centered Computing Department, Monash University, Australia. His research encompasses a wide range of topics within the fields of Visualization (VIS), VR/AR, and Human-Computer Interaction (HCI). He actively contributes to these communities and regularly publish his work in leading venues such as IEEE VIS, ACM CHI, IEEE TVCG, EuroVis, and IEEE VR. He has received three best paper honorable mention awards, notably from IEEE VIS in 2016 and 2022, as well as ACM CHI in 2021. He also serves as a Program Committee member for several prestigious conferences in his fields, including IEEE VIS 2022/23, ACM CHI 2023, and IEEE VR 2022/23. He is also one of the online experience chairs for ISMAR 2023.

Title： Data Visualization in the Metaverse: What, Why, and How?

Abstract： Data visualization is the process of converting complex raw data into meaningful graphics. Leveraging the powerful human visual system to summarize information in a cognitively efficient way, visualization becomes ubiquitous in science, analysis, and media. A long-standing core research question in visualization research is how to design novel and effective visualizations. Display and interaction technologies define how data can be visually represented and how people can interact with them. As an emerging display and interaction platform, VR/AR provides unparalleled potential for renewing our human-data interaction experience. This talk will present novel visualization and interaction techniques for the metaverse. The presentation will first discuss unique characteristics in VR/AR that conventional 2D displays cannot offer, in the context of data visualization. The presenter will then exemplify how to take advantage of those features to build novel and effective visualizations in VR/AR. Finally, the presenter will describe the vision of the future workspace, where VR/AR will be an essential component, and conclude on an optimistic note: with deep integration between hardware, software, and user experience, we can achieve this vision in the near future.

Bio： Chaoli Wang is a Professor in the Department of Computer Science and Engineering at the University of Notre Dame. He holds a Ph.D. d egree in Computer and Information Science from The Ohio State University. Dr. Wang's primary research interest is scientific visualization. He has published around 120 refereed papers in international journals and conferences. He has served the professional community as a Paper Co-Chair for multiple visualization conferences (IEEE VIS, IEEE PacificVis, IEEE LDAV, ChinaVis, and ISVC), an Associate Editor of IEEE TVCG, a member of the Steering Committee of IEEE LDAV, and the International Liaison of the IEEE VGTC Executive Committee. For more information, please visit https://sites.nd.edu/chaoli-wang/.

Title： Generative Tasks in Scientific Visualization: Recent Advances and Future Perspectives

Abstract： Over the past five years, generative tasks for scientific visualization have quickly become a focused direction in DL4SciVis research. In this talk, I will provide an overview of scientific visualization, generative tasks, and network structures. Then I will present recent advances using representative works and briefly discuss future perspectives for this vibrant research direction.

Bio： Dr. Jie Hua received his PhD from the University of Technology Sydney in 2014. His research interests include Data Visualisation, Big Data, Machine Learning, and Cyber Range. He has over 15 years of industry experience, working as a Senior Software Engineer in both China and Australia. Currently, he holds positions as a Researcher and Software Architect at the University of Technology Sydney and Macquarie University. In 2020, he was honoured with the "Hundred Talents Program" in Hunan, China.

Title： Visualising Tomorrow: Unleashing the Power of Data Visualisation for Decision Making in Multiple Fields

Abstract： In the era of data-driven decision-making, the role of visualisation has become increasingly crucial. The ability to distil complex information into clear, intuitive visual representations empowers decision-makers across various domains. In this talk, we will delve into the cutting-edge techniques and advancements in data visualisation that will shape the future of decision-making. It aims to explore the future potential of data visualisation in three distinct yet interconnected realms by showcasing examples and case studies. Data visualisation revolutionises stock market analysis by uncovering hidden relationships among stocks, aiding portfolio management and trading strategies. In the context of Covid-19, visualising demographic and infection data helps understand community profiles and tailor strategies for effective interventions. In musical data analysis, interactive graphs enable exploration of diverse acoustic characteristics in crossover singing, enhancing accessibility for valuable insights. Visualisation empowers decision-makers across finance, health, and music domains.

Bio： Dr. Bei Wang Phillips is an Associate Professor in the School of Computing and a faculty member in the Scientific Computing and Imaging (SCI) Institute, University of Utah. She obtained her Ph.D. in Computer Science from Duke University. Her research focuses on topological data analysis, data visualization, and computational topology. She works on combining topological, geometric, statistical, data mining, and machine learning techniques with visualization to study large and complex data for information exploration and scientific discovery. Some of her current research activities involve the analysis and visualization of high-dimensional point clouds, scalar fields, vector fields, tensor fields, networks, and multivariate ensembles. Dr. Phillips is a DOE Early Career Research Program (ECRP) awardee in 2020 and an NSF CAREER awardee in 2022. She has also received more than ten research awards as PI and co-PI from NSF, NIH, and DOE.

Title： Visualizing and Exploring the Topology and Geometry of Word Embeddings

Abstract： In this talk, we will discuss recent research efforts in visualizing and exploring the topology of word embeddings from large language models. We present TopoBERT, a visual analytics system for interactively exploring the fine-tuning process of transformer-based models from a topological perspective. If time permits, we will also discuss visualizing the geometry of debiasing techniques for word embeddings.