Affiliated Faculty

IUNI has over 165 faculty affiliates from across IU. You may browse through listings below – clicking on a name will expand to show you full listings. You may also search through keywords and biographies in the search bar below.

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A - E
Allen, Colin History and Philosophy of Science and Medicine, College of Arts and Sciences / IUB
K - O
Menczer, Filippo Informatics and Computer Science, School of Informatics and Computing / IUB
Bio: Filippo Menczer is a Professor of informatics and computer science, adjunct Professor of physics, and a member of the cognitive science program at Indiana University, Bloomington. He holds a Laurea in Physics from the University of Rome and a Ph.D. in Computer Science and Cognitive Science from the University of California, San Diego. Dr. Menczer has been the recipient of Fulbright, Rotary Foundation, and NATO fellowships, and a Career Award from the National Science Foundation. He currently serves as director of the Center for Complex Networks and Systems Research and is a Fellow of the Institute for Scientific Interchange Foundation in Torino, Italy, a Senior Research Fellow of The Kinsey Institute, and an ACM Distinguished Scientist. He previously served as division chair in the IUB School of Informatics and Computing, and was Fellow-at-large of the Santa Fe Institute. His research is supported by the NSF, DARPA, and the McDonnell Foundation. It focuses on Web science, social networks, social media, social computation, Web mining, distributed and intelligent Web applications, and modeling of complex information networks.
Disciplines: Computer Science
P - T
Razo, Armando Political Science, College of Arts and Sciences / IUB
Bio: Professor Razo's research interests are in the field of comparative politics, with special interests in the political economy of development and comparative analysis of networks and institutions. His research and teaching center around two themes: (1) how political institutions in developing countries affect economic performance; and (2) the study of political institutions and political organization in nondemocratic settings. Current research projects include the development of an ontology and linguistic corpus for comparative analysis of networks in international development. He teaches courses on networks and institutions, quantitative contextual analysis, development, positive political economy, and Latin American politics. He is the author of Social Foundations of Limited Dictatorship, published by Stanford University Press in 2008, which advances a network theory of private policymaking. A student of economic history, he is also co-author with Stephen Haber and Noel Maurer of The Politics of Property Rights (2003).
Disciplines: Political Science
Smith, Eliot Psychological and Brain Sciences, College of Arts and Sciences / IUB
Bio: Distinguished Professor of Psychological and Brain Sciences Eliot Smith has pioneered the development of multi-agent models of information spread in social networks that draw on social psychological studies of social influence to incorporate realistic assumptions about how and when people will accept (and further transmit) the information they receive from others (Mason et al., 2007). Smith’s empirical studies and multi-agent modeling have focused on the cognitive and behavioral processes that occur when people receive information from others that differs from their own prior beliefs — processes that determine whether they accept the information and change their beliefs, ignore the information, or seek out further evidence to attempt to reconcile the inconsistency (Collins et al., 2011; Smith and Collins, 2009). Another investigation examined in depth strategies for processing inconsistent information and determining its validity (Smith, 2014). The multi-agent model led to the conclusion that people can best avoid misinformation by comparing incoming information to their own existing beliefs, and discarding it if it is too discrepant. Alternative strategies that are prominent in the literature — such as accepting new information if it comes from multiple independent sources — were found not to be useful. This is partly because people are not usually in a good position to know the overall structure of the social network and therefore cannot tell whether multiple information sources are truly independent of each other. That is, person A may hear the same information from both B and C and assume they are independent, when in fact both B and C might have obtained the information from a common source D.