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).
The Language and Information Technologies (LIT) lab, directed by Rada Mihalcea, conducts research in natural language processing, information retrieval, and applied machine learning. The group specifically focuses on projects concerned with text semantics (word/text similarity, large semantic networks), behavior analysis (multilingual opinion analysis, multimodal models for deception detection, emotion recognition, alertness detection, stress/anxiety detection, analysis of counseling speech), big data for cross-cultural analysis (geotagging, understanding cross-cultural differences and worldview), educational applications (pedagogical search engines, automatic short answer grading, conversational technologies for student advising).
Several of the projects in the LIT lab are interdisciplinary, acknowledging the fact that language can be used to deepen our understanding in many different fields, such as psychology, sociology, history, and others. Some of the ongoing projects in the lab are collaborations with psychologists and sociologists, and target a rich modeling of human behavior through language analysis, seeking answers to questions such as “what are the core values of a culture?” and “are there differences in how different groups of people perceive the surrounding world?” The lab is also actively working on multimodal projects to track and understand human behavior, where language analysis is complemented with other channels such as facial expressions, gestures, and physiological signals.
Of interest, Prof. Mihalcea was quoted in a story about sexism and today’s virtual assistants such as Amazon’s Alexa, Apple’s Siri, and Microsoft’s Cortana; Refinery29.
I develop clinical trial designs and data analytic methods for informing sequential decision making in health. In particular I focus on methods for constructing real time individualized sequences of treatments (a.k.a., treatment policies or Just-in-Time Adaptive Interventions) delivered by mobile devices. This is an area of Precision Medicine. I develop new clinical trial designs (designs in which each person is randomized 100s or 1000s of times) and generalize reinforcement learning algorithms to analyze the data and construct treatment policies. I also generalize data analytic method from causal inference for use in analyzing mobile health data.
My research interests include mathematical analysis, probability, networking, and algorithms. I am especially interested in randomized algorithms with applications to harmonic analysis, signal and image processing, computer networking, and massive datasets.