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.

The proposed research, by leveraging data, develops novel methods to study how the patterns of human influence and relationship may impact their productivity. We use graph theory, NLP, and convex optimization to extract information from communication logs of individuals in financial and social systems. In this project, we build upon theories from sociology, namely structural balance theory—which describes the dynamics that govern the sentiment of interpersonal relationships—and assess the impact on stock traders' profitability.

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.

Multi-armed bandits have been used in multiple contexts that call for a mixture of exploration and exploitation strategies. We are considering this paradigm in the context of repeated assignment of tasks to teams. We assume a latent structured space that encodes the attributes of individuals, teams, and tasks. The goal of the project is to use the structure of the space to develop efficient algorithms for the assignment of single and multiple tasks to teams.

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. However, it is not yet understood how patterns of interactions and relationships among team members (i.e.

The structures and dynamics of microbes (e.g. bacteria, bacteriophages) in chronic wounds are closely related to the wound clinic phenotypes and their healing process. Methods to infer the latent structure of the association networks between microbes from observed next-generation sequencing data are critical for a deeper understanding of putative microbe-microbe interactions and the outcomes.

Past Research Projects

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