Tung-Hui Hu

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Hu is a media scholar interested in the historical and theoretical context of digital culture and AI. His first monograph, A Prehistory of the Cloud (MIT Press, 2015), explored the material infrastructure of the cloud, while Digital Lethargy (MIT Press, 2022), explored the racialized dimensions of labor within AI through key works of art and literature. He is at work on a new project, A History of the World in 7 Datasets. He is also a core faculty member of the Helen Zell Writers’ Program, U-M’s MFA program in creative writing.

Melissa DeJonckheere

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Melissa DeJonckheere is an adolescent health researcher specializing in qualitative, participatory, and mixed methods research. She is Co-Director of the Mixed Methods Program at the University of Michigan and regularly teaches qualitative and mixed methods research to trainees of all levels. Her research focuses on psychosocial influences on health and well-being, particularly among adolescents with type 1 or type 2 diabetes. Dr. DeJonckheere is also interested in improving access to and participation in academic research for youth, students, and trainees who have historically been excluded from science and research experiences. She is program director of MYHealth, a virtual, out-of-school research training program for high school students from southeast Michigan. She has used natural language processing to analyze text data in qualitative and mixed methods studies. She is currently pursuing research related to the use of natural language processing and AI in qualitative and mixed methods research in the health and social sciences.

John Barry Ryan

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My research focuses on the subfield of political communication using three primary quantitative methodologies: surveys, experiments (both psychological and behavioral economic), and content coding of text. My research has looked at the content of campaign websites, scholar’s social media accounts, newspaper coverage of elections as well as networked participants involving mock elections in a lab.My research focuses on the subfield of political communication using three primary quantitative methodologies: surveys, experiments (both psychological and behavioral economic), and content coding of text. My research has looked at the content of campaign websites, scholar’s social media accounts, newspaper coverage of elections as well as networked participants involving mock elections in a lab.

Grant Schoenebeck

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My current research combines machine learning tools and economic approaches (e.g game theory, mechanism design, and information design) to develop and analyze systems for eliciting and aggregating information from of diverse group of agents with varying information, interests, and abilities.
This work applies to scenarios where a collective decision-making process is required, such as peer grading, peer review, crowd-sourcing, content moderation, misinformation detection, surveys, and employment hiring/evaluation.
More broadly, I am interested in multi-agent systems, a subfield of AI; data economics; and algorithmic game theory.

Carol Menassa

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My group’s research focuses on understanding and modeling the interconnections between human experience and the built environment. We design autonomous systems that support wellbeing, safety and productivity of office and construction workers, and provides them opportunities for lifelong learning and upskilling. In all research projects, we work hard to ensure that the results are inclusive and benefit people of different abilities in their daily activities and empower them for nontraditional careers.

Matthew Bui

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Dr. Bui integrates interdisciplinary, sociotechnical, and community-based research methods and approaches to explore the opportunities for, and obstacles to, racial and data justice within society. Bui’s research primarily examines: 1) how individuals and communities call attention to—and subvert—issues of power and inequality within and through data and data science; and 2) the impacts of digital media and data-driven technologies within Black, Indigenous, and other People of Color (BIPOC) communities. In all, Dr. Bui seeks to advance both critical frameworks as well as novel data-driven methodologies that engage with the intersections of racial and data justice.

Christian Sandvig

Christian Sandvig

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I am a researcher specializing in discovering the consequences of computer systems that curate and organize culture. A major theme of my research investigates accountability mechanisms for machine learning and artificial intelligence. My research group coined the phrase “algorithmic auditing” in a 2014 paper; this was subsequently made suggested reading for submissions to the first ACM FAccT (Fairness, Accountability, and Transparency) Conferences. My work on algorithms and accountability was recommended by the White House Office of Science and Technology Policy in 2016 as one of five research strategies essential to the future of big data technologies in the US. I was the named plaintiff of a multi-year lawsuit against the federal government on behalf of computing researchers and journalists; this lawsuit changed the legal definition of “hacking” in the United States in 2022. I have also published research about social media, wireless systems, broadband Internet, online video, domain names, and Internet policy. My group blog about social media platforms was named one of the “Must-Follow Feeds” in science by Wired magazine.

A researcher tests a counterfeit, unauthorized copy of allegedly privacy-protecting fabric stolen from Adam Harvey's HyperFace design.

A researcher tests a counterfeit, unauthorized copy of allegedly privacy-protecting fabric stolen from Adam Harvey’s HyperFace design.


Accomplishments and Awards

Derek Van Berkel

Derek Van Berkel

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Dr. Van Berkel is an assistant professor at The University of Michigan, School for Environment and Sustainability. His research focuses on understanding land change at diverse scales; the physical and psychological benefit of exposure to natural environments; and how digital visualization of data can add new place-based knowledge in science and community decision-making. He has expertise in spatial statistics, data science, big data, and machine learning. Van Berkel is currently a Co-PI on an NSF grant examining how online webtools can enable the public to co-create landscape designs for novel solutions to climate-change adaptation and mitigation in urban areas. He is also part of the NOAA funded GLISA project developing land change models to support knowledge discovery in municipalities throughout the Great Lake States. His work in AI focuses on deciphering complex sentiment from multimodal content, such as understanding image content and analyzing captions and tags posted by users, at scale. This research aims to provide objective measures of behavior and attitude for modeling diverse values and benefits of nature globally.


Accomplishments and Awards

Andrew Wu

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My research focuses on the interface of technology, finance and operations management. I develop and apply new approaches in natural language processing (NLP) and text analytics to study emerging and classic OM problems including (1) new marketplaces in both Fintech and Edtech, (2) supply chain risks, and (3) societal impact of OM/financial decisions.

Fan Bu

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I am broadly interested in Bayesian and computational statistics for analyzing large-scale and complex data. I am particularly interested in spatio-temporal statistics, network inference, infectious disease models, and distributed learning. My methodological research has been motivated by applications in public health, observational healthcare studies, computational social science, and sports sciences.

I came from a math background but studied statistics in order to become a sports analyst (yes, Moneyball!). Throughout my PhD and postdoc training, I grew a strong appreciation for social sciences (how people behave and interact) and health sciences (how to provide high-quality healthcare for everyone). I see data science as the field to help us make sense of complex data that arise from our daily life and scientific endeavors, by building reliable and reproducible frameworks that transform data to evidence and then to scientific findings and decisions.