Position: PhD Candidate
Current Institution: Columbia University
Abstract: Diversity and inequality in social networks
My research develops theories about robust models to support decision-makers bridge learning objectives with human incentives particularly in settings where there are underlying social and information networks. As machine learning algorithms play an increasingly important role in the diffusion of information—such as job opportunities or available services—through online platforms one’s online environment determines the resources one can access. Therefore the question of inequality in access to resources and information becomes central in algorithmic design. A main theme in my research is to leverage algorithmic and computational techniques to shed light on the root drivers of bias in automated decision-making and to bring algorithmic mechanism design and machine learning insights to bear on effective interventions to mitigate such bias. Towards this end in particular I draw on insights from graph theoretical models of interaction and machine learning methods to understand complex structures and incentives in networks. My doctoral research with Prof. Augustin Chaintreau develops theoretical foundations about the role of algorithms when applied to relational data in perpetuating inequality between individuals and groups. My research has contributed to equitable and adaptable recommendation systems campaigns for information dissemination and disease propagation analysis mechanisms for geographical districting as well as back-end database systems that learn from dynamic environments. My work has thus far focused on building models of network growth—often enriched with properties drawn from sociological research (what drives certain patterns of connection?)—through which I delve into a theoretical analysis of network evolution that incorporates incentives and algorithmic input (what is the effect of an algorithm on the network evolution and inequality?). Beyond this my current projects aim to understand the incentives that people have when connecting to each other and the effect of algorithms in influencing such incentives when allocating resources.
Ana-Andreea Stoica is a Ph.D. candidate at Columbia University. Her work focuses on mathematical models data analysis and inequality in social networks. From recommendation algorithms to the way information spreads in networks Ana is particularly interested in studying the effect of algorithms on people’s sense of privacy community and access to information and opportunities. She strives to integrate tools from mathematical models—from graph theory to opinion dynamics—with sociology to gain a deeper understanding of the ethics and implications of technology in our everyday lives. Ana grew up in Bucharest Romania and moved to the US for college where she graduated from Princeton in 2016 with a bachelor’s degree in Mathematics. Since 2019 she has been co-organizing the Mechanism Design for Social Good initiative.