Defence of dissertation in the field of computer science, MSc Han Xiao

Title of the dissertation is "Data science for social good: theory and applications in epidemics, polarization, and fair clustering"

Technical innovations have transformed our lives fundamentally, in both positive and negative ways. In this thesis, we look at the negative side, identify three problems to tackle, namely epidemics, online polarization, and bias in automatic decision-making, and approach them using data-driven approaches.

The rapid spread of disease is happening globally, as evidenced by the pandemic of COVID-19. To effectively contain an epidemic, early identification of infected individuals is crucial. Nonetheless, this task is challenging. We study the problem of automatic detection of hidden infections in the context of social networks.

Online polarization is formed partly due to the widespread use of online social media. As a result, people are unlikely to adopt new ideas that differ from their beliefs, which finally leads to a polarized society. To tackle online polarization, we argue that it is important to discover who is involved in the polarization. To this end, we consider a problem of finding polarized subgraphs in social networks.

Machine learning algorithms allow the automation of many decision-making processes. However, unfair results that favor one demographic group (e.g., male) over another (e.g., female) are witnessed. We focus on fairness issues for data clustering -- an important machine learning task, which has applications in infrastructure design and online social media. We propose a new clustering formulation that captures a novel fairness notion.

For all proposed problems, we study their complexity and design algorithms whose theoretical performance is analyzed. We evaluate all proposed algorithms' efficacy in both synthetic and real-world settings

Opponent: Professor Danai Koutra, University of Michigan, USA

Custos: Professor Aristides Gionis, Aalto University School of Science, Department of Computer Science

Contact information of the doctoral candidate: Han Xiao, Department of Computer Science, [email protected]

The public defense will be organized via Zoom. Zoom linkZoom Quick Guide: https://www.aalto.fi/en/services/zoom-quick-guideThe dissertation is publicly displayed as online display 10 days before the defence at: https://aaltodoc.aalto.fi/doc_public/eonly/riiputus/?lang=en

Electronic dissertation