Briana Mezuk

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My research program uses epidemiologic methods to examine the interrelationships between mental and physical health over the lifespan. A core feature of my research is the integration of conceptual and analytical approaches, methods, and models from social science, including natural language processing, and clinical/health disciplines with the aim of arriving at a more nuanced and comprehensive understanding of the ways in which mental and physical health interrelate. The goal of this work is to inform interventions that reflect an integrative approach to health.

Gen Li

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Dr. Gen Li is an Assistant Professor in the Department of Biostatistics. He is devoted to developing new statistical methods for analyzing complex biomedical data, including multi-way tensor array data, multi-view data, and compositional data. His methodological research interests include dimension reduction, predictive modeling, association analysis, and functional data analysis. He also has research interests in scientific domains including microbiome and genomics.

Novel tree-guided regularization methods can identify important microbial features at different taxonomic ranks that are predictive of the clinical outcome.

Wenbo Sun

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Uncertainty quantification and decision making are increasingly demanded with the development of future technology in engineering and transportation systems. Among the uncertainty quantification problems, Dr. Wenbo Sun is particularly interested in statistical modelling of engineering system responses with considering the high dimensionality and complicated correlation structure, as well as quantifying the uncertainty from a variety of sources simultaneously, such as the inexactness of large-scale computer experiments, process variations, and measurement noises. He is also interested in data-driven decision making that is robust to the uncertainty. Specifically, he delivers methodologies for anomaly detection and system design optimization, which can be applied to manufacturing process monitoring, distracted driving detection, out-of-distribution object identification, vehicle safety design optimization, etc.

Lyllian Simerly

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Lyllian Simerly is a senior studying Biopsychology, Cognition and Neuroscience with a Minor in Statistics.

Joyojeet Pal

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My research examines the social media behavior of politicians. Using machine learning methods to curate lists of Twitter accounts by class, such as politicians, journalists, influencers, etc, I research temporal and topical patterns of how political communication – including campaign outreach, network alignments, hate speech, and polarization play out online before, during, and after major electoral campaigns. My work is primarily based on Twitter data, using the Twitter academic API to pull amd store tweets of accounts identified through an iterative process of shortlisting handles of interest. Thereafter, we use amix of descriptives and advanced statistical techniques to seek patterns in the data.

J.J. Prescott

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Broadly, I study legal decision making, including decisions related to crime and employment. I typically use large social science data bases, but also collect my own data using technology or surveys.

Lu Wang

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Lu’s research is focused on natural language processing, computational social science, and machine learning. More specifically, Lu works on algorithms for text summarization, language generation, argument mining, information extraction, and discourse analysis, as well as novel applications that apply such techniques to understand media bias and polarization and other interdisciplinary subjects.

Edgar Franco-Vivanco

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Edgar Franco-Vivanco is an Assistant Professor of Political Science and a faculty associate at the Center for Political Studies. His research interests include Latin American politics, historical political economy, criminal violence, and indigenous politics.

Prof. Franco-Vivanco is interested in implementing machine learning tools to improve the analysis of historical data, in particular handwritten documents. He is also working in the application of text analysis to study indigenous languages. In a parallel research agenda, he explores how marginalized communities interact with criminal organizations and abusive policing in Latin America. As part of this research, he is using NLP tools to identify different types of criminal behavior.

Examples of the digitization process of handwritten documents from colonial Mexico.

Benjamin Fish

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My research tackles how human values can be incorporated into machine learning and other computational systems. This includes work on the translation process from human values to computational definitions and work on how to understand and encourage fairness while preventing discrimination in machine learning and data science. My research combines tools from the theory of machine learning with insights from economics, science and technology studies, and philosophy, among others, to improve our theories of the translation process and the algorithms we create. In settings like classification, social networks, and data markets, I explore the ways in which human values play a primary role in the quality of machine learning and data science.

The likelihood of receiving desirable information like public health information or job advertisements depends on both your position in a social network, and on who directly gets the information to start with (the seeds). This image shows how a new method for deciding who to select as the seeds, called maximin, outperforms the most popular approach in previous literature by decreasing the correlation between where you are in the social network and your likelihood of receiving the information. These figures are taken from work by Benjamin Fish, Ashkan Bashardoust, danah boyd, Sorelle A. Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian. Gaps in information access in social networks. In The World Wide Web Conference, WWW 2019, San Francisco, CA, USA, May 13-17, 2019, pages 480–490, 2019.