Koesten is a Postdoc at the University of Vienna (Austria) in the Research Group for Visualization and Data Analysis (Faculty of Computer Science) and an affiliate researcher at King’s College London, UK. Her research explores the characteristics of sensemaking with data in different contexts, such as data discovery, data reuse, and data visualizations. She uses verbal and written summarization techniques to surface sensemaking activities and studies subjective experiences of sensemaking in interactions with datasets and data visualizations. She is PI of the Talking Charts project, which investigates how charts related to climate change and COVID-19 are created and understood by public as well as by scientific audiences.

I am a traveling scholar. As such I write, lecture, and create materials for teaching.  Sometimes this includes videos, short podcasts, tech reports, sometimes papers for scientific publication, and sometimes books for everyone.  

I'm a practicing scientist.  That means I experiment and I analyze. I do field studies and I try to understand what makes online researchers tick.

Why do they sometimes query Google for [ first ], and then not click on anything? Why do some Google users only ask one query, while others can go on and on? 

This is what drives my work:  How do people make sense of the data they find?  What do people search for?  How do they do it?  How do they understand and use what they've found.  

Niki is an Associate Professor and Cooper-Siegel Chair in the Human-Computer Interaction Institute at Carnegie Mellon University. His research explores a future that scales sensemaking beyond the limits of a single individual’s mind by: (1) distributing sensemaking among many people and machines; (2) enabling people to build on the sensemaking that others have already done; and (3) seamlessly integrating human and machine cognition to make sense of large information spaces. He is also a co-founder of DataSquid, a startup that supports sensemaking by bringing the power of intuitive touch and physics to data visualization.

Nitesh Goyal (Tesh) leads research at the intersection of Safety and AI, in the Responsible AI team of Google Research. His work at Google has led to launch of ML based tools like AI Test Kitchen as a sandbox to upload generative AI applications, MakerSuite as a tool for developers to leverage Google’s generative AI capabilities without coding, Harassment Manager to empower targets of online harassment, and ML based moderation to reduce online toxic content production on platforms like OpenWeb. He also introduced design metaphor of "Sensemaking Translucence" to reduce biased sensemaking in criminal justice while managing interruptions appropriately. His research has been supported by German Govt. Fellowship, National Science Foundation, and MacArthur Genius Grant.

Dr. Pengyi Zhang is an associate professor at Department of Information Management, Peking University. She holds a Ph.D. and a MLS from College of Information Studies, University of Maryland. Her research expertise include information and knowledge organization, information/data seeking and sensemaking, and user-centered design and evaluation. She is particularly interested in how people seek for and make sense of data and information. Her recent research investigates the how individuals in groups and communities make sense together to negotiate meaning, deal with conflicts, and form consensus. She has received grants from National Science Foundation of China, Chinese Library Association, and OCLC/ALISE Library and Information Science Research Grant.

Dr. Michael Xieyang Liu is a research scientist at Google in the Pittsburgh office. He recently obtained his PhD from Carnegie-Mellon University with his thesis Tool Support for Knowledge Foraging, Structuring, and Transfer During Online Sensemaking. His work has integrated several different sensemaking tools and systems into a coherent whole, establishing patterns for bridging the gap between rapidly evolving mental models in people’s heads and the externalization of those models. Specifically, he designs and builds interactive systems to reduce the costs and increase the benefits of externalization, thereby capturing more of the cognitive work that users engage in while making sense of information.