Speakers archive

Jane Adams

Storywrangler: A journey in visualizing 100 billion tweets in 100 languages, and what it means for future research

Data visualization artist-in-residence at the University of Vermont's Complex Systems Center, Jane Adams describes the Computational Story Lab's process of parsing tweets containing 1 trillion 1-grams from a 10% sample of all public tweets spanning from 2008 to 2021. From language identification; to parsing 1, 2, and 3-grams; computing normalized frequency and usage rank; gathering statistics on language speakers; developing a new instrument for measuring rank-turbulence divergence; and building a real-time visualization pipeline in React, this project has involved a massive team of researchers and many years of stumbling blocks before reaching the web. The creation of Storywrangler has subsequently informed research on projects related to hurricane awareness; public discourse on mental health and the #BlackLivesMatter movement; data journalism about the meteoric rise of discussion about Juneteenth, the January 6th Capitol Riots, and the verdict in the trial of Derek Chauvin; and shows promise on topics ranging from linguistic study of idioms in common parlance, to trends in the use of "cashtags" relative to financial movements in the stock and cryptocurrency markets.

Bio:

Jane Adams is the Data Visualization Artist-in-Residence at the University of Vermont Complex Systems Center. She builds interactive visualization tools for exploratory analysis of linguistics on social media platforms as part of the Computational Story Lab, and EDA for high-dimensional health information as part of the MassMutual Center of Excellence in Complex Systems and Data Science. This fall, Jane will be joining Khoury College of Computer Sciences at Northeastern University in the Data Visualization Lab. In her spare time, Jane is a practicing artist experimenting with physical computing, data visualization, and machine learning as art media. Website: universalities.com

Ryan Gallagher

Word Shift Graphs: A Method for Visualizing and Explaining Sentiment Differences Between Texts

A common task in text analysis is to quantify how the sentiment differs between two sets of texts. However, collapsing those texts into a single number makes it difficult to confidently interpret interesting or unexpected patterns we might see in the data, such as a sudden spike or drop in sentiment. To better capture fine-grained differences between texts, I introduce word shift graphs, visualizations that yield a meaningful and interpretable summary of how individual words contribution to the variation between two texts for dictionary-based sentiment analysis. Through a number of case studies, I show how word shift graphs can be used by computational social scientists, digital humanists, and other text analysis practitioners for better diagnostic investigation, hypothesis generation, and substantive interpretation when conducting sentiment analysis. 

Bio:

Ryan Gallagher is a network science PhD candidate at Northeastern University. As a member of the Communication Media and Marginalization (CoMM) Lab at Northeastern's Network Science Institute, he studies how individuals use online communication networks to amplify their voices, and how that amplification resonates through online media ecologies. To do so, his research makes advances in network science and text-as-data methodology to develop new approaches for measuring the complexities of polarization, misinformation, and the networked public sphere. He holds an MS in mathematics from the University of Vermont, where he worked with the Computational Story Lab at the Vermont Complex Systems Center, and a BA in mathematics from the University of Connecticut.

Tiago Peixoto

Visualizing network hierarchies

Tiago P. Peixoto is Associate Professor in the Department of Network and Data Science at the Central European University, in Budapest. He obtained his Habilitation in Physics at the University of Bremen in 2017, and his PhD in Physics at the University of São Paulo in 2008. He was the recipient of the 2019 Erdős-Rényi Prize in Network Science. His research focuses on characterizing, identifying and explaining large-scale patterns found in the structure and function of complex network systems — representing diverse phenomena with physical, biological, technological, or social origins — using principled approaches from statistical physics, nonlinear dynamics and Bayesian inference.

Ágnes Telek

Budapest Time Machine – find the (hi)stories behind the data!

Agnes Telek graduated from Art History Major. Her research area is the architecture and the urban history of the turn of the century Budapest. She is a team member of the research service group at the BCA and also participates in organizing preparing cultural cooperations and open day/public events.
Agnes Telek is also highly interested in contemporary visual arts and photography, curating exhibitions, giving presentations and publishing articles in this field. She is currently head of team of the Department of Maps, Plans and Photographs for the Budapest City Archives (BCA) and Time Machine Ambassador. For more info visit her Linkedin profile.

Slides: Find the (Hi)stories behind the data

Tünde Szabó

Visualizing mass mobility based on mobile cellular data, to frame the real risks behind the media hype

Tünde Szabó Ph.D. is a researcher of the Hungarian Academy of Sciences, Geographical Research Institute, and the founder of GEOInsight Ltd. Tünde started her career as an urban planner and spatial data analyst for the City Budapest, focusing on metropolitan-scale structure planning. Tünde completed her MSc degrees in Geography and Regional Planning at the Eötvös Loránd University of Sciences in Budapest, and Environmental and Infrastructural planning at the University of Groningen, in the Netherlands.  She completed her Ph.D. degree in the field of agglomeration economies, mobility patterns of metropolitan spaces, and spatial data mining. Tünde takes part in the Spatial Big Data research group at the Hungarian Academy of Sciences, working together with Hungarian Telekom, to define novel methods of scientific exploration, based on mobile cellular data. Tünde and her research fellows work also together in GEOInisght Ltd., to reveal business insights in spatial big data, using Machine Learning.

Slides: Visualizing Mass Mobility Based on Mobile Cellular Data

Attila Bátorfy

Ups and downs of building a dataviz-project for public service

Attila Bátorfy is founder and head of visual journalism team Átló, subsidiary dataviz-project of investigative journalism center Átlátszó. He teaches journalism, visual storytelling, data visualization and information design at the Media Department of Eötvös Loránd Science University. He is currently working on his Phd-thesis on the history of Hungarian information graphics. For more info visit https://www.batorfyattila.com

Silvia Fierăscu

Silvia Fierăscu has a PhD in Comparative Politics and Network Science from Central European University. Her research focuses primarily on quality of governance, political-business relations, and statistical analyses of network data. Silvia is involved in various interdisciplinary projects, translating complex problems into real-time applications for organizational management, political communication, and better governance.

Slides: Sustainable Social Impact

Jessie Labov

Jessie Labov is a Resident Fellow in the Center for Media, Data and Society, a member of the Digital Humanities Initiative, and the Project Coordinator of the Text Analysis Across Disciplines Initiative. Recent publishing projects include a co-edited volume with Friederike Kind-Kovacs, Samizdat, Tamizdat and Beyond: Transnational Media During and After Socialism (Berghahn 2013), and Transatlantic Central Europe: Contesting Geography and Redefining Culture Beyond the Nation (CEU Press 2019). She has also worked on a variety of digital humanities projects concerned with issues of canon formation, text mining, and visualizing the receptive pathways of literary journals. In July 2019, she will co-direct the CEU Summer University Course Cultures of Dissent in Eastern Europe (1945-1989): Research Approaches in the Digital Humanities.

Slides: The Beauty of the Meso

Gerardo Iñiguez, PhD

Games and computational social science:
A bridge between academia and industry

The aim of computational social science is to understand collective human behaviour by analysing data and making models of the digital traces we leave in the online world. The closing gap between off- and online activities (particularly in games, where people form social groups and create entire economies) allows us to perform this task better than ever, bringing both knowledge of the large-scale structure of society, and challenges in predicting the future behaviour of individuals. In this talk I’ll go through some of the ways gaming data has been used in computational social science research, as well as give an outlook on how online gaming platforms may help us understand the way societies transform and adapt to a changing environment.

Bio:

Gerardo Iñiguez is assistant professor at DNDS-CEU, Visiting Researcher at Aalto University (Finland) and UNAM (Mexico), and CEO & Co-founder at Predify (Mexico), where he uses large datasets of socioeconomic behaviour in social networks and other complex systems to develop mathematical models of collective dynamics, and give insights to academia and industry challenges. Gerardo has been data scientist at Next Games (Finland), assistant professor in computational social science (UNAM) and has a Ph.D. from Aalto University (computational science). His publications (dealing with spreading dynamics in social networks, opinion and deception in social interactions, conflict resolution in collaborative platforms, and hierarchy formation in complex systems) have been published in high-impact journals like Phys. Rev. Lett., Sci. Rep., Proc. R. Soc. B, and J. R. Soc. Interface. His research has attracted external funding from the World Bank, the EU, and the Academy of Finland, as well as attention from the complex systems community, the media, and the public. Find out more at www.gerardoiniguez.com and @iniguezg on Twitter.

Slides: Games and Computational Social Science

Melinda Sipos

Melinda Sipos is a Budapest based designer and cultural mediator working at the boundaries
of art, design, technology and social engineering.
Currently Melinda pursues her doctoral studies at the Moholy-Nagy Art and Design University
exploring the topic of collaborative and interdisciplinary working methods. She has a joint
research project on physical data visualisation which is one of her main artistic interest.

http://melindasipos.net

Slides: Data Embodiment

Judit Bekker

The Importance of Data Visualization 
in a Fast-Paced World

Judith Bekker is a data visualization specialist at Starschema ltd. in Budapest. She received her political science degree from the Corvinas University of Budapest, and landed a job as a leading pollster in the public sector. In the past years Judit was working in the FMCG industry in different marketing and analyst roles. Her main focus is turning complicated things both more digestible and attractive through data visualization. Judith won individual category 1st place and people’s choice award at the 2017 dataviz competition organised by OTP bank and Budapest BI forum.

Johannes Sorger

The Visualisation Integration Design
Space - Dealing with Multifaceted
Data

Johannes Sorger is a postdoctoral researcher in Data Science and Visualisation at the Complexity Science Hub in Vienna. Johannes received his computer science degree in Visual Computing from the TU Wien at the Institute of Computergraphics and Algorithms where he also finished his PhD in a collaboration with the VRVis research centre. Johannes’ main research interests are centred around the application of visualisation as an enabling technology. For his work on the visualisation of neuronal networks Johannes received the 2014 OCG Incentive Award, as well as the Best Paper Award at the 2013 IEEE Symposium on Biological Data Visualisation.

Tamer Khraisha

How Interactive Visualisations are
Created ? : The D3.js Force Layout

Tamer Khraisha is a Ph.D. candidate at the Centre for Network Science at the Central European University. He holds a master’s degree in Economics and Economic Policy and a B.A. in Financial Economics from the University of Bologna in Italy. As research topics, Tamer is interested in the study of technological innovation, technological fitness landscapes, financial innovation and economic and financial networks. Given the power and importance of web visualisations, Tamer has worked on creating several interactive visualisations using the D3.js library. To see Tamer’s visualisations, visit his blog.

Krisztina Szucs

Creating a Data Art Generator

Kristina Szucs is a Budapest-based data visualisations designer. With a Masters degree in Graphic Design at Moholy-Nagy University of Art and Design, Krisztina is a specialist in information architecture, User Interface and User Experience design, and data visualisation, working on a wide variety of projects, from politics to health and market research. Kristina is successfully freelancing in a dynamic, demanding, and growing business environment around data visualisation. ​

Benjamin Lind

Benjamin is a former Assistant Professor in the Faculty of Sociology, fellow at the Center for Advanced Studies, and research associate with the Applied Network Research Laboratory at the National Research University-Higher School of Economics. Though currently focusing on primary and secondary education, Ben continues to stay involved with academia as an author and reviewer for high impact journals. His research emphasizes data analysis and visualization in the areas of social movements and political sociology, social network analysis, quantitative methods, labor organizations, as well as comparative and historical methods. He earned his Masters and PhD from University of California, Irvine. His PhD dissertation analyzed the growth of labor contention in the United States during the late nineteenth century, questioning how some local events escalated into large-scale social upheaval. Ben’s work was awarded the Best Article Award by the Collective Behavior and Social Movements section of the American Sociological Association, and has appeared in the American Journal of Sociology, the International Journal of Emergency Management, the Blackwell Encyclopedia of Sociology, and elsewhere.

Noemi Alexa

Assistant Professor at CEU Business School, Board Member of Transparency International

Eszter Somos

Junior Data Scientist at Liligo.com Part of the winner team of the Magyar Telekom Leading Data Hackathon 2016 in Data Visualization

Mihaly Minko

Data Visualization Team Lead at Starschema

Michael Szell

Postdoctoral Research Associate, Center for Network Science, Researcher in residence at moovel Group

Viktor Lagutov

Assistant Professor at CEU, Department of Environmental Sciences and Policy, Head of Environmental Systems Laboratory

Professor Fosca Giannotti

We are honored to announce our Keynote Speaker, Professor Fosca Giannotti, senior researcher at the Information Science and Technology Institute of the National Research Council in Pisa, Italy, where she leads the Knowledge Discovery and Data Mining Laboratory. KDD Lab is a joint research initiative with the University of Pisa, founded in 1995 - one of the earliest European research groups specifically targeted at data mining and knowledge discovery. Professor Giannotti will open the third edition of the exhibition and she will also be in the discussion panel for the following meetup, on Tuesday. She is going to be teaching a short course on data visualization at CEU this year, at the Center for Network Science. 

Dino Pedreschi

Dino Pedreschi is a Professor of Computer Science at the University of Pisa, and a pioneering scientist in mobility data mining, social network mining and privacy-preserving data mining. He co-leads the Pisa KDD Lab with Prof. Giannotti. He is a founder of the Business Informatics MSc program at Univ. Pisa, a course targeted at the education of interdisciplinary data scientists. Professor Pedreschi will also be teaching the short data visualization course at CNS.

Krisztina Szucs

Krisztina Szucs is a Budapest-based data visualizations designer. With a Masters degree in Graphic Design at Moholy-Nagy University of Art and Design, Krisztina is a specialist in information architecture, User Interface and User Experience design, and data visualization, working on a wide variety of projects, from politics to health and market research. Krisztina is successfully freelancing in a dynamic, demanding, and growing business environment around data visualization.