Luis Rocha and other School of Informatics, Computing and Engineering faculty receive Research Recognition Awards

Luis Rocha, Katy Borner, Paul Macklin and other faculty from the School of Informatics, Computing and Engineering (SICE) were the awardees of the 2019 SICE Research Awards. Luis Rocha received the award in recognition of the NSF Research Traineeship (NRT) on Complex Networks and Systems and two NIH NLM R01 grants. The awards were handed by SICE Dean Raj Acharya and Associate Dean for Research Kay Connelly.

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AI, Society and Organizations

On the 7th of March 2019, CNETS Professor Luis Rocha will participate in a panel organized by Nova SBE’s Executive Education, Instituto Gulbenkian da Ciência and ISI Foundation with the theme of AI, society and organisations: experiences from applied projects in governments, companies and NGO’s, where the role of data science in today’s world will be discussed.

Other guest speakers, include Rayid Ghani, director of the Center for Data Science and Public Policy in the University of Chicago, founder of the Data Science for Social Good fellowship and Chief Scientist at the Obama for America 2012, Daniela Paolotti, Ciro Cattuto, Joana Gonçalves-Sá and Leid Zejnilovic.… continue reading.

CNetS social media study shows how affect labeling can help moderate emotions

Your mother always told you that if something was bothering you, you should talk about it. It would make you feel better. Turns out she was right, and researchers at the School of Informatics, Computing, and Engineering have the science to prove it. Johan Bollen, a professor of informatics and computing, leads a team that analyzed the Twitter feeds of tens of thousands of users to study how emotions change before and after they were explicitly stated. In the study, “The minute-scale dynamics of online emotions reveal the effects of affect labeling,” published in the journal Nature Human Behaviour, Bollen and his colleagues used algorithms to measure how the positivity or negativity of tweets change before or after a user explicitly expressed having an emotion, e.g. saying “I feel bad” or “I feel good.” Their study not only reveals how emotions evolve over time, but also how their expression may change them, and how these changes differ between men and women.… continue reading.

Bollen social media study shows how affect labeling can help moderate emotions

Your mother always told you that if something was bothering you, you should talk about it. It would make you feel better. Turns out she was right, and researchers at the School of Informatics, Computing, and Engineering have the science to prove it. Johan Bollen, a professor of informatics and computing, leads a team that analyzed the Twitter feeds of tens of thousands of users to study how emotions change before and after they were explicitly stated. In the study, “The minute-scale dynamics of online emotions reveal the effects of affect labeling,” published in the journal Nature Human Behaviour, Bollen and his colleagues used algorithms to measure how the positivity or negativity of tweets change before or after a user explicitly expressed having an emotion, e.g. saying “I feel bad” or “I feel good.” Their study not only reveals how emotions evolve over time, but also how their expression may change them, and how these changes differ between men and women.… continue reading.

CNetS team awarded NIH grant to improve chronic-disease management with Data and Network Science

Luis M. RochaThe National Institutes of Health, under the National Library of Medicine’s program on data science research, awarded a $1.55 million grant to an interdisciplinary team lead by Luis Rocha, a professor of informatics, member of CNETS and the director of the NSF-NRT complex networks & systems program at the School of Informatics, Computing, and Engineering. The four-year project, a collaboration between SICE and the Indiana University School of Nursing, will employ innovative data- and network-science methods to produce myAURA, an easy-to-use web service for epilepsy patients. myAURA will be based on a large-scale epilepsy knowledge graph built by integrating data from social media, electronic health records, patient discussion boards, scientific literature databases, advocacy websites, and mobile app data. The knowledge graph will, in turn, be used to fuel recommendation and visualization algorithms based on the automatic inference of relevant associations. The inference will follow algorithms developed by Rocha’s team to remove redundancy and extract factual information from large knowledge graphs as well as parsimonious network visualizations developed by Katy Börner, Distinguished Professor of Engineering & Information Science at SICE. … continue reading.