Nick Duran - Research is focused on the ways in which complex cognitive processes are revealed in the dynamics of movement and language, both within individuals and across dyads and groups. Major areas of study include deception, perspective-taking, and collaborative problem-solving, as well as the connection between language and action, cognitive dynamics, and natural language processing and corpus analytics.
Patricia Friedrich - Research focuses on the ways in which applied linguistics can be and is used across interdisciplinary areas as diverse as forensic investigations to diplomatic talks; from disability studies to creative writing; and from translation studies to machine learning.
Richard Hahn – research focuses on developing novel statistical methods for analyzing data arising from the social sciences: psychology, economics, education, and business. His current focus revolves around causal inference using regression tree models, as well as foundational issues in Bayesian statistics.
Deborah Hall - Research adopts an interdisciplinary approach to understanding and modeling social behavior, broadly, and how aspects of social identity shape interactions with others. A current focus is on developing and incorporating machine learning models that identify cyberbullying on social media into mobile apps that help families and communities.
Michelle Mancenido - Research is focused on the design and analysis of statistical experiments in engineering, scientific, and industrial applications. Major areas of study include optimal experimental designs, statistical modeling for chemical and mixture experiments, and sensory experiments.
Robert McCulloch - research focuses on Bayesian statistics. Much of McCulloch's recent research is on Bayesian approaches for tree-based ensemble models. Tree based methods have emerged as a basic tool in Machine Learning because they are a relatively simple way to uncover complex nonlinear relationships in high dimensional problems. Ensemble methods combine many tree models into one overall model which is far more powerful than any one tree model can be on its own.
Joel Nishimura - Research focuses on how simple models of behavior, learning and change can create complex systems, and given such a system, how to represent and extract information from it. Major areas of study are in network science and mathematical biology using tools from machine learning, applied dynamical systems and game theory.
Jacob Nelson is an Assistant Professor of Journalism in the Walter Cronkite School of Journalism & Mass Communication. Jacob studies the relationship between journalism and the public. His research draws on ethnographic data collected from news publishers and quantitative analyses of online audience data to understand the extent to which journalists' assumptions about their audiences align with how those audiences actually behave.
Derek Powell - Research involves applying behavioral and data science methods to better understand higher-order cognitive processes like reasoning, judgment, and belief revision.
Elias Robles - Research focuses on judgement, choice and decision making, particularly in relation to health, impulsiveness, and risk taking. Areas of interest include addictions, and the dynamic analysis and modeling of behavior processes in risky choice.
Jessica Salerno - Research focuses on the effect of emotion and intergroup dynamics within a legal setting, as well as how decision-making processes may differ in individuals versus groups. A current area of study is how extra-legal factors influence both individual jurors’ and juries’ decisions, with a strong emphasis on the group deliberation process.
Yasin Silva – Research specializes in innovative ways to analyze and process data. Major areas of study are in the areas of social media analysis, online misbehavior detection, social computing, cyberbullying detection in social networks, big data, similarity-aware data analysis, scalable database systems, and fairness and transparency in AI.
Kuai Xu – Research interests include network security, network measurement and analysis, cloud computing, home networks, and online social networks.
Yunpeng Zhao – Primary research interests include machine learning methodology and theory in network analysis with applications in biology and the social sciences. A current focus is working with high dimensional data analysis with applications in genomics. Areas of expertise include network science, machine learning, statistics, data mining, and big data.