Slides Framework

How should VIS4ML Redefine Itself in the Rapid Evolution of AI?

Thursday, October 26, 2023 3:45 PM-5:00 PM AEDT (UTC+11)
IEEE VIS 2023, Melbourne, Australia, Oct. 22-27 🔗

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 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 panelist. 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.


  • Duen Horng (Polo) Chau

    Georgia Tech,

    Duen Horng (Polo) Chau is an Associate Professor at Georgia Tech. His research bridges ML and visualization, creating scalable interpretable tools that enhance human understanding of large-scale data and complex ML models. He received 11 best paper/poster/demo awards and published 200+ refereed articles. He received prestigious faculty awards (Google, Meta, Intel), Georgia Tech wide Senior Faculty Outstanding Undergraduate Research Mentor Award, and Outstanding Mid-Career Faculty Award. His research has been deployed by Microsoft, Meta, NortonLifeLock, and Atlanta Fire Rescue Department. His students won PhD fellowships (Google, Apple, IBM, JPMorgan, NASA, NSF). He teaches 1,000+ students each semester. Polo holds a Machine Learning (ML) Ph.D. from Carnegie Mellon University (Dissertation Award, Honorable Mention). He co-directs the MS Analytics program. He is the Director of Industry Relations at The Institute for Data Engineering and Science.

  • Mennatallah El-Assady

    ETH Zurich,

    Mennatallah El-Assady works at the ETH AI Center. Her research is at the intersection of data analysis, visualization, computational linguistics, and explainable artificial intelligence. She has gained experience working in close collaboration with political science and linguistic scholars over several years, which lead to the development of the platform. More recently, she has been working on establishing the explainable AI framework She has co-founded and co-organized several workshop series, notably and Personal Website:

  • Liang Gou

    Bosch Research,

    Liang Gou is a Principal Research Scientist and Senior Research Manager at Bosch Research North America & Bosch Center for Artificial Intelligence (BCAI). At Bosch, he leads a research group that focuses on Human-Assisted AI, which employs the distinctive combination of human and machine intelligence to address certain weaknesses in Artificial Intelligence (AI) across various domains such as Autonomous Driving, Advanced Driving Assistance Systems, Industry 4.0 (I4.0), and the Internet of Things (IoT), among others. His research interests are broad and lie in the fields of visual analytics, deep learning, foundation models, and human-computer interaction. Before joining Bosch Research, Liang was a Principal Research Scientist at Visa Research and a Research Staff Member at the IBM Almaden Research Center. Liang received his Ph.D. in Information Science from Penn State University. He has published over 25 peer-reviewed, refereed scientific articles (including two that received best paper awards and two that earned honorable mention awards at top conferences), and has filed over 30 patents in the aforementioned areas.

  • Ross Maciejewski

    Arizona State University,

    Ross Maciejewski is a Professor at Arizona State University and Director of the School of Computing and Augmented Intelligence. He serves as the co-Director of the Center for Accelerating Operational Efficiency - a Department of Homeland Security Center of Excellence. His primary research interests are in the areas of visualization and explainable AI. He has served on the organizing committees for the IEEE Conference on Visual Analytics Science and Technology and the IEEE/VGTC EuroVis Conference, and he currently serves as the co-chair of the Visualization Executive Committee (VEC). Professor Maciejewski is a recipient of an NSF CAREER Award (2014), and his work has been recognized through a variety of awards at the IEEE Visual Analytics Contest (2010, 2013, 2015), a best paper award in EuroVis 2017, and CHI Honorable Mention Awards (2018, 2022).

  • Dominik Moritz

    Carnegie Mellon University,

    Dominik Moritz is on the faculty at Carnegie Mellon University where he co-directs the Data Interaction Group ( at the Human-Computer Interaction Institute. His group's research develops interactive systems that empower everyone to effectively analyze and communicate data. Dominik also manages the visualization team in Apple's machine learning organization. His systems (Vega-Lite, Falcon, Draco, Voyager, and others) have won awards at academic venues (e.g. IEEE VIS and CHI), are widely used in industry, and by the Python and JavaScript data science communities. Dominik got his PhD from the Paul G. Allen School at the University of Washington, where he was advised by Jeff Heer and Bill Howe.


  • Dylan Cashman

    Brandeis University

    Dylan Cashman is an assistant professor in the Michtom School of Computer Science at Brandeis University in Waltham, MA. He previously worked as a Senior Expert in Data Science and Advanced Visual Analytics within the Data Science and Artificial Intelligence division of Novartis Pharmaceuticals in Cambridge, MA. Dylan holds a PhD in Computer Science from Tufts University and an Sc. B in Mathematics from Brown University. His research interests include the development and evaluation of visual affordances that improve usability of artificial intelligence models and data science processes. His research has won best paper awards at the Eurovis conference, the Symposium on Visualization for Data Science (VDS), and the Workshop on Visual Analytics for Deep Learning at IEEE VIS.

  • Junpeng Wang

    Visa Research

    Junpeng Wang is a Research Scientist and Senior Consultant at Visa Research. His research interests lie broadly in the fields of visual analytics, deep learning, and data mining. At Visa, he leads a wide range of projects that uphold the integrity of financial systems, covering anomaly detection, fraud detection, and anti-money laundering. Junpeng received his Ph.D. in Computer Science and Engineering from the Ohio State University. He has published numerous papers in international conferences and journals, including IEEE VIS, IEEE PacificVis, IEEE TVCG, ICLR, KDD, and others. His work and service have been recognized by a variety of awards from IEEE VIS (Best Paper Honorable Mention 2018, Best Paper Award 2019), IEEE PacificVis (Best Paper Award 2018, Best Paper Honorable Mention 2023, Best Visualization Notes 2023), and IEEE TVCG (Best Reviewer Award 2021).

  • Qianwen Wang

    University of Minnesota

    Qianwen Wang is an Assistant Professor at the Department of Computer Science and Engineering, University of Minnesota. Her research strives to facilitate the communication between end users and machine learning models through interactive visualization, with a special interest in their applications in solving biomedical challenges. She has received two best abstract awards from BioVis ISMB, one honorable mention from IEEE VIS, and one Best paper award from IMLH@ICML. She has served as the Abstract Chair for the ISMB BioVis COSI, the Poster and VisNote Chair for the PacificVis, and a Program Committee member for IEEE VIS and ACM ACM IUI.

Feel free to contact the organizers if you have any questions.