Representation learning on graphs enables downstream tasks such as graph classification and link prediction in multiple application scenarios. We design novel graph representation learning pipelines to achieve state-of-the-art performance on these downstream tasks, leveraging tools such as random-walks, social theories, graph neural networks, and bilevel optimization. We also investigate the explainability of graph representation learning models, which is vital, especially in high-stakes applications such as drug discovery and anomaly detection.

This collaborative program establishes pathways for data science training through coursework and real-world projects, connecting three main public higher education institutions in California (UCSB, Cal Poly, City College). Students will learn the underlying principles of data science, including data-generating processes and the role of measurement, ethics and privacy, information-processing tools for harnessing the power of big data, and the oral and written communication skills necessary for pursuing effective professional careers in the field.

Teams of the future will likely consist of humans and AI agents. To this purpose, we conducted experiments to explore how such teams integrate their individual decisions into a group response. We propose a set of models to explain team decision making using appraisal dynamics, Prospect Theory, Bayes rule, and eigenvector centrality.

Predicting network structure and flows on edges is a precondition to effective planning and disaster response in critical infrastructure networks. Accordingly, we propose to develop algorithms inspired by network science, physical modeling, and machine learning to determine this information from incomplete observations.

The proposed research will develop novel methods for analyzing and modeling heterogeneous dynamic networked data. Network data arises in a number of application domains ranging from IoT, cloud computing, software analysis, neuroscience, biology, geography, to social sciences. Accordingly, network analysis has emerged as a major paradigm for exploring complex processes behind observed data. Compared to high dimensional data, analysis of network data is more challenging due to interdependencies between entities, the presence of attributes, and the natural evolution of networks over time.

Advances in imaging technologies have enabled superbly fine-resolution in-vivo examination of the human brain. We are developing deep learning, statistical, and optimization frameworks to aid in the interpretability of such images by discovering regions that are preserved across a population or group of interest, and in understanding how the functional and structural networks of the brain interrelate.

Graphs have evolved into a rich framework for the solution of diverse problems, ranging from mathematical puzzles to the analysis of planetary-scale social networks. A particular class of problems that has attracted recent interest in the research community is related to how to model information that is embedded in a graph structure. In this scenario, the graph dynamics is guided by processes that are localized both in time and space, with effects at different scales.

The recent convergence of research in social and psychological sciences, dynamic and quantitative modeling, and network science has led to a re-examination of collective team behavior from a quantitative and systems-oriented viewpoint. Teams cannot be understood fully by studying their components (members) in isolation: team performance is not simply a sum of individual performances; and a diversity of opinions among members leads to better group outcomes.

Past Research Projects

For more information on the lab's previous research projects, please see our publication page.