Dmitry Zaytsev

Associate Professor of the Practice (SBE & Data Science)

Contact:

dzaytsev@nd.edu

Primary Interest Areas

Social, Behavioral and Economic Sciences (SBE) & Data Science, Computational Social Sciences (CSS), Social Network Analysis (SNA), Applied Statistics, Business Analytics, Social & Political Changes, Democracy & Peace Studies, Public Policy Analysis

Biography

Dmitry Zaytsev is an Associate Professor of Practice in the Lucy Family Institute for Data & Society, where he focuses on application of data science methods to Social, Behavioral and Economic (SBE) studies. He specializes in conceptualizing, measuring, explaining, modeling, forecasting, and managing social and political changes with advanced methods of computational social sciences.

His expertise centers around political, policy, computational, and network science, with a primary emphasis on modeling complex, multidimensional social and political phenomena. In his work he not only uses but develops new methods in advanced research methodology: social network analysis, structural equation modeling, time- series analysis, non-parametric methods, generalized linear models, text mining, and natural language processing. He holds a PhD in Political Science and two master’s degrees – in Public Policy and Applied Mathematics and Informatics. In the past, he had held positions of Senior Researcher at the sociological contract-based think tank; Founding Director of the center for statistical consulting & business analytics; Associate Professor in sociology and public policy departments, an Academic Supervisor of the master’s program in applied statistics and network analysis, and Academic Supervisor of the undergraduate minor in applied data and network analytics.

Featured Projects and Publications

Introduction to the special issue on scientific networks (2023)

Searching for coherence in a fragmented field: Temporal and keywords network analysis in political science (2023)

Expanding the boundaries of interdisciplinary field: Contribution of Network Science journal to the development of network science (2023)

Neural Network Modeling and What-If Scenarios: Applications for Market Development Forecasting (2023)

Computational Tools of Media Analysis for Corporate Policy Effectiveness Evaluation: Models and Their Reliability (2023)

The Power of Knowledge: How Think Tanks Impact US Foreign Policy (2022)

Data Envelopment Analysis as a Tool to Evaluate Marketing Policy Reliability (2020)

Fluctuating capacity of policy advice in Russia: testing theory in developing country context (2019)

Protest publics: Toward a new concept of mass civic action (2019)