International Conference on Web and Social Media · 2025
Cited
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Resume (English only)
Academic Achievements
Paper 'Election Polls on Social Media: Prevalence, Biases, and Voter Fraud Beliefs' received Best Paper Honorable Mention at ICWSM'25.
Study 'Political Biases on X before the 2025 German Federal Election' was broadcast by ZDF (German public TV channel).
Published an eLetter in Science, along with an editorial and article, questioning a widely reported Science paper funded by Meta.
Research on election polls on social media covered by various media outlets including Tech Policy Press, El País, Fox network, and Phys.org.
Three submissions to the International Conference for Computational Social Science (IC2S2) accepted as talks.
Two manuscripts submitted to ICWSM'24 accepted, and two others received 'Revise and Resubmit' decisions.
Manuscript 'Learning from Discriminatory Training Data' accepted to AIES'23.
Paper 'Marrying Fairness and Explainability in Supervised Learning' accepted to FAccT'22 conference.
Successfully organized SemEval-2022 Task 8: Multilingual news article similarity, attracting over 30 research teams and releasing the largest labeled multilingual dataset of news articles published across 124 countries.
Gave a talk and participated in a panel at a Responsible AI workshop at Carnegie Mellon University.
Quoted in the BusinessWest article 'AI Promises To Impact The Workforce In Unexpected Ways'.
Quoted in an article by UMass Amherst about his graduate course on Responsible AI.
Research Experience
Leads the SIMS lab, the EQUATE initiative, and social media public opinion projects. Focuses on developing statistical methods to understand public opinion and designing representative social media to facilitate political discourse.
Education
Information not available
Background
Assistant Professor of Computer Science at University College Dublin and Adjunct Professor of Computer Science at the University of Massachusetts Amherst. Heads the SIMS lab, the EQUATE initiative, and the website socialpolls.org. Research interests include fair and explainable machine learning, data science, computational social science, social media, network science, causality, and open-world learning.