Professor of Political and Computational Social Science
B.A., Carleton College, 1986; M.A., Duke University, 1990; Ph.D., 1992. Assistant Professor, Caltech, 1992-95; Associate Professor, 1995-2002; Professor, 2002-.
Michael Alvarez's research focuses on public opinion and voting behavior, election technology and administration, electoral politics, political campaigns, and statistical and computational modeling. He has long been interested in empirically testing formal models of elections and voting behavior.
Alvarez is the co-director of the Caltech/MIT Voting Technology Project. He is a fellow of the Society for Political Methodology and the American Academy of Arts and Sciences. He has been recognized for his mentoring work, both at Caltech by the Graduate Student Council (twice) and by the Society for Political Methodology. He also received the Emerging Scholar Award in the Elections, Public Opinion, and Voting Behavior Section of the American Political Science Association in 2001.
Research Summary
Elections; Campaigns; Political Behavior; Election Administration; Election Security; Election Technology; Public Opinion; Political Methodology; Statistics; Computer Science; Machine Learning; Deep Learning; Survey Methodology
Sreemanti Dey, a sophomore majoring in computer science, worked with Michael Alvarez, professor of political and computational social science, to study the 2020 presidential election with machine learning and has been recognized by an international conference for her work.
Caltech political scientists, historians, computational social scientists, and data scientists examine the systems, policies, and technologies that underlie elections and other democratic processes.
Research Website