Sandun Perera

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Professor Perera is Assistant Professor of Operations and Supply Chain Management in the School of Management at the University of Michigan, Flint

Professor Perera’s research broadly focuses on Supply Chain Management, Revenue Management, the Operations-Finance interface, the Operations-Marketing interface, Healthcare Operations Management and Financial Engineering. He is particularly interested in stochastic and deterministic inventory problems under general cost structures, government (central bank) operations in the foreign exchange market, consumer behavior under social learning, optimal delivery strategies for various supply chain networks, and asymmetric information in fads models. His recent research in healthcare operations management, revenue management, stochastic inventory management and financial engineering are mainly data and algorithm oriented.

Peter Lenk

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Peter Lenk, PhD, is Professor of Technology and Operations, Stephen M Ross School of Business, at the University of Michigan, Ann Arbor.

Prof. Lenk develops Bayesian models that disaggregate data to address individuals.  He also studies Bayesian nonparametric methods and currently consider shape constraints.  Prof. Lenk teaches and uses data mining methods such as recursive partition and neural networks.

Pascal Van Hentenryck

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Pascal Van Hentenryck, Phd, is the Seth Bonder Collegiate Professor of Industrial and Operations Engineering, Professor of Electrical Engineering and Computer Science, College of Engineering, at the University of Michigan, Ann Arbor.

His research is concerned with evidence-based optimization, the idea of optimizing complex systems holistically, exploiting the unprecedented amount of available data. It is driven by an exciting convergence of ideas in big data, predictive analytics, and large-scale optimization (prescriptive analytics) that provide, for the first time, an opportunity to capture human dynamics, natural phenomena, and complex infrastructures in optimization models. He applies evidence-based optimization to challenging applications in environmental and social resilience, energy systems, marketing, social networks, and transportation. Key research topics include the integration of predictive (machine learning, simulation, stochastic approximation) and prescriptive analytics (optimization under uncertainty), as well as the integration of strategic, tactical, and operational models.

The video above is of a planned evacuation of 70,000 persons for a 1-100 year flood in the Hawkesbury-Nepean Region using both predictive and prescriptive analytics and large data sets for the terrain, the population, and the transportation network.

Eric Schwartz

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Eric Schwartz, PhD, is Associate Professor of Marketing in the Ross School of Business at the University of Michigan, An Arbor.

Prof. Schwartz’s expertise focuses on predicting customer behavior, understanding its drivers, and examining how firms actively manage their customer relationships through interactive marketing. His research in customer analytics stretches managerial applications, including online display advertising, email marketing, video consumption, and word-of-mouth. The quantitative methods he uses are primarily Bayesian statistics, machine learning, dynamic programming, and field experiments. His current projects aim to optimize firms’ A/B testing and adaptive marketing experiments using a multi-armed bandit framework. As marketers expand their ability to run tests of outbound marketing activity (e.g., sending emails/direct mail, serving display ads, customizing websites), this work guides marketers to be continuously “earning while learning.” While interacting with students and managers, Professor Schwartz works to illustrate how today’s marketers bridge the gap between technical skills and data-driven decision making.

Josh Pasek

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Josh Pasek is Assistant Professor of Communication Studies and Faculty Associate in the Center for Political Studies at the University of Michigan.  His substantive research explores how new media and psychological processes each shape political attitudes, public opinion, and political behaviors.  Josh also examines issues in the measurement of public opinion including techniques for incorporating social trace data as a means of tracking attitudes and behaviors.  Current research evaluates whether the use of online social networking sites such as Facebook and Twitter might be changing the political information environment, and assesses the conditions under which nonprobability samples, such as those obtained from big data methods or samples of Internet volunteers can lead to conclusions similar to those of traditional probability samples.  His work has been published in Public Opinion Quarterly, Political Communication, Communication Research, and the Journal of Communication among other outlets.  He also maintains two R packages for producing survey weights (anesrake) and analyzing weighted survey data (weights).

Muzammil M. Hussain

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Muzammil M. Hussain is an Assistant Professor of Communication Studies, and Faculty Associate in the Institute for Social Research at the University of Michigan.

Dr. Hussain’s interdisciplinary research is at the intersections of global communication, comparative politics, and complexity studies. At Michigan, Professor Hussain teaches courses on research methods, digital politics, and global innovation. His published books include “Democracy’s Fourth Wave? Digital Media and the Arab Spring” (Oxford University Press, 2013), a cross-national comparative study of how digital media and information technologies have supported the opening-up of closed societies in the MENA, and “State Power 2.0: Authoritarian Entrenchment and Political Engagement Worldwide” (Ashgate Publishing, 2013), an international collection detailing how governments, both democracies and dictatorships, are working to close-down digital systems and environments around the world. He has authored numerous research articles, book chapters, and industry reports examining global ICT politics, innovation, and policy, including pieces in The Journal of Democracy, The Journal of International Affairs, The Brookings Institution’s Issues in Technology and Innovation, The InterMedia Institute’s Development Research Series, International Studies Review, International Journal of Middle East Affairs, The Communication Review, Policy and Internet, and Journalism: Theory, Practice, and Criticism.

Twitter: @m_m_hussain.

Fred Feinberg

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My research examines how people make choices in uncertain environments. The general focus is on using statistical models to explain complex decision patterns, particularly involving sequential choices among related items (e.g., brands in the same category) and dyads (e.g., people choosing one another in online dating), as well as a variety of applications to problems in the marketing domain (e.g., models relating advertising exposures to awareness and sales). The main methods used lie primarily in discrete choice models, ordinarily estimated using Bayesian methods, dynamic programming, and nonparametrics. I’m particularly interested in extending Bayesian analysis to very large databases, especially in terms of ‘fusing’ data sets with only partly overlapping covariates to enable strong statistical identification of models across them.

Applying Bayesian Methods to Problems in Dynamic Choice

Applying Bayesian Methods to Problems in Dynamic Choice

 

Puneet Manchanda

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My interest is in using econometrics, especially Bayesian econometrics, and machine learning methods to infer causality. I tend to work with mostly parametric models of firm and consumer behavior to assess the effectiveness of firm actions. My work spans a variety of industries such as pharmaceuticals, e-commerce, gaming and hi-technology.

Jun Li

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Jun Li, PhD, is Assistant Professor in the department of Technology and Operations in the Ross School of Business at the University of Michigan, Ann Arbor.

Jun Li’s main research interests are empirical operations management and business analytics, with special emphases on revenue management, pricing, consumer behavior, economic and social networks. She has worked extensively with large-scale data, including transactions, pricing, inventory and capacity, consumer online search and click stream data, supply chain relationships and disruptions, clinical and healthcare claims. She is the Winner  of INFORMS Revenue Management and Pricing Practice Award for her close collaboration with retailing practitioners in implementing best response pricing algorithms. Her paper on airline pricing and consumer behavior is the finalist for Best Management Science Papers in Operations Management 2012 to 2014. She is also the principal investigator of a National Science Foundation funded project: “Gaining Visibility Into Supply Network Risks Using Large-Scale Textual Analysis”. Her work has enjoyed coverage by The Economist, New York Times and Forbes.

Supply Chain Risk Events

Supply Chain Risk Events