Seminar Series

Hosted onsite at LLNL—and now virtually—on an ongoing basis, our seminars feature speakers from other institutions around the Bay Area and beyond. We host these events to introduce new ideas and potential collaborators to the Lab. We are pleased to share seminar information here with the broader data science community.

Deep Generative Modeling in Network Science with Applications to Public Policy Research

Gavin Hartnett
Gavin Hartnett | Information Scientist | RAND Corporation

Network data is increasingly being used in quantitative, data-driven public policy research. These are typically very rich datasets that contain complex correlations and inter-dependencies. This richness promises to be quite ;useful for policy research, while at the same time poses a challenge for the useful extraction of information from these datasets —a challenge that calls for new data analysis methods. We formulate a research agenda of key methodological problems whose solutions would enable progress across many areas of policy research. We then review recent advances in applying deep learning to network data and show how these methods may be used to address many of the identified methodological problems. We particularly emphasize deep generative methods, which can be used to generate realistic synthetic networks useful for microsimulation and agent-based models capable of informing key public policy questions. We extend these recent advances by developing a new generative framework that applies to large social contact networks commonly used in epidemiological modeling. For context, we also compare these recent neural network–based approaches with the more traditional Exponential Random Graph Models. Lastly, we discuss some open problems where more progress is needed. This talk will be mainly based on our recent report.

Gavin Hartnett is an Information Scientist at the RAND Corporation and a professor at the Pardee RAND Graduate School, where he serves as the Tech and Narrative Lab AI Co-Lead. As a theoretical physicist turned machine learning (ML) researcher, his research centers around the application of ML to a diverse range of public policy areas. Hartnett's recent work includes investigations into COVID-19 vaccination strategies, applications of graph neural networks to agent-based modeling, applications of natural language processing to official U.S. government policy documents, and the implications of adversarial examples in defense scenarios. He has also worked on applications of AI/ML in the physical sciences, with a particular emphasis on spin-glass systems in theoretical physics and computer science. Prior to joining RAND, Hartnett studied black holes in string theory as a postdoc at the Southampton Theory Astrophysics and Gravitation Research Centre in the UK, and before that he was a PhD student at UCSB. His research focused on the existence and stability of black holes, and in using properties of black holes to understand phenomena in strongly coupled gauge theories through the gauge/gravity duality. As an undergraduate at Syracuse University, he researched gravitational waves as part of the LIGO collaboration, the expansion of the early universe, as well as topological defects in liquid crystals.