Hello Mai!

We welcome Mai Elshehaly as a Lecturer in Visualisation at the giCentre.

Mai designs, develops and evaluates visual analytics for population health management with a particular interest in the role of routine data and digital footprints in decision-making.

An Honorary Senior Research Fellow at the Wolfson Centre for Applied Health Research, Mai leads the visualisation stream at the Yorkshire and Humber Patient Safety Research Centre. She works closely with colleagues at local authorities and several partner organisations to deliver data literacy to over 30,000 young citizens in Bradford.

Mai received a PhD in Computer Science from the Center for Human-Computer Interaction at Virginia Tech, with a focus on scientific data visualisation. Her postdoctoral  research experience included NSF- and NIHR-funded fellowships at the University of Maryland, Baltimore County and University of Leeds.

Check Mai’s publications to find out more about her exciting work in visualization and public health.

Narges Mahyar & Ali Sarvghad

Seminar - Narges Mahyar & Ali Sarvghad,

University of Massachusetts (UMass) Amherst

Designing and Building Tools for Fostering Equity and Inclusion in Civic Decision-Making

Thurs 8th December, 16:00 - 17:30, ELG04 (Drysdale)


RisingEMOTIONS - capturing public emotion in response to seal level rise and flooding.

Narges Mahyar and Ali Sarvghad are doing exciting work in building tools that help communities solve real-world sociotechnical problems at the HCI-VIS Lab at UMass Amherst. They have received Best paper and Hon Mention awards at many of the major conferences in the last year or two. They will be heading here from their trip to INRIA Paris to give a talk and start a discussion on Visualization for Equity, Inclusion and Civic Decision-Making.

Please come along. Details and Bios here!

Abstract

Inclusive community engagement is paramount for fair and impartial civic decision-making. However, traditional methods rarely provide opportunities for inclusive public participation. While advances in digital civics have broadened public participation, these technologies still face several challenges in promoting inclusive participation and integrating data analysis into civic decision-making processes.

In this talk, Narges and Ali present examples of their recent work on building and studying community-centered tools for fostering equity and inclusion in civic decision-making by :

  1. lowering barriers for public participation,

  2. enriching data collection, and

  3. facilitating more inclusive public input analysis.

They describe their vision for expanding our research on democratizing civic decision-making processes and outcomes by building, deploying, and studying socially impactful technologies that integrate data visualization, social computing, and artificial intelligence.

Award : Andrienko & Andrienko join IEEE VGTC Visualization Academy

Profs Gennady and Natalia Andrienko were inducted into the IEEE VGTC Visualization Academy at IEEE VIS 2022 in Oklahoma City.

It’s a great honour to be part of a prestigious academy that highlights the accomplishments of the leaders in the field.

We’d like to congratulate both of our Professors of Visualization on these nominations that reflect their spectacular contributions to Visualization and Visual Analytics over the years.    

Other leaders in the field to be inducted this year were Prof. Valerio Pascucci of the University of Utah; Dr. Nathalie Henry-Riche of Microsoft Research, Redland; and Prof. Helwig Hauser of the University of Bergen.

New Paper : Beyond the Walled Garden - a visual essay at IEEE VIS

A visual essay in 5 chapters by Jo Wood.

"Data visualization was tended in a walled garden.”

Yet there was land beyond the trees..."

https://altvis.github.io/#walled-garden

https://vimeo.com/760406142

AI image generation from language prompts has advanced rapidly in little over a year.

It is about to change the way we design data visualization.

This picture essay considers some of the possibilities and the trade-off between the new expressiveness it provides us with and the potential loss of effectiveness that comes with learning new and more complex graphical languages.

It questions whether traditional notions of locatable objects and retinal variables are still applicable in this more sophisticated graphical environment.