Lin Ma

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My research interests lie in the intersection of database management systems (DBMSs) and machine learning (ML), especially using ML/AI techniques to automate database administration/tuning to remove human impediments.

Catherine Kaczorowski

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The Kaczorowski laboratory, led by Dr. Catherine Kaczorowski, pioneers techniques to identify and validate genetic and cellular mechanisms that promote resilience to cognitive aging, Alzheimer’s disease, and other age-related dementias. By combining mouse and human systems; genomic, anatomic, and behavioral approaches; and integrative analyses across multiple scales, data types, environmental factors, and species, we are accelerating the discovery of the precise genetic mechanisms of cognitive resilience that could yield the next generation of targets and therapeutic strategies for promoting brain health. We are now uniquely poised to propel the field of personalized medicine forward using our genetically diverse, yet reproducible models of human neurodegenerative dementias, having already contributed conceptual and technical advances that revolutionized our ability to study complex diseases, specifically human AD dementia.

Ellie Abrons

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I am an architect, educator, and creative practitioner/scholar/researcher. My work is focused on the intersections of materiality, technology, design, and construction, with an emphasis on adaptive reuse, equitable housing, and the socio-political and cultural aspects of digital architectural production and images. I write about digital materiality, software, and authorship. I am a principal of a collaborative architecture practice, called T+E+A+M, and a member of two interdisciplinary research collectives focused on equitable housing and ethical AI. Since 2023, I have served as the Director of U-M’s Digital Studies Institute, an interdisciplinary research and teaching unit focused on technology, digital culture, and social justice, primarily through the lenses of race, gender, disability, sexuality, class, power and identity.

X. Jessie Yang

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Dr. X. Jessie Yang is an Associate Professor in the Department of Industrial and Operations Engineering, with courtesy appointments at the School of Information. She is an expert in human-autonomy/robot interaction, particularly in modeling trust in human-autonomy teams. She and her team use machine learning tools to model human behaviors when interacting with autonomous and robotic agents.

Katie Snyder

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My research centers on ethics and communication in engineering contexts, particularly related to community-engaged design of energy systems. I’m interested in developing methods for ethically engaging community members in the engineering design process to create equitable, sustainable products and processes for a shared common good. Key questions guiding this work include the following:

 


Research Highlights

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.

Srijita Das

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My research is about building sample-efficient machine learning models. My long term goal is to develop collaborative systems that can actively seek advice from humans and make faster decisions, resulting in reliable and practical systems. I specifically focus on design of sequential decision-making models to make them learn faster. We leverage advice from humans in various forms (implicit and explicit) to encourage favorable decisions and avoid decisions having catastrophic consequences. We also focus on minimizing the cost of seeking advice by building suitable machine learning models from historical advice data and reusing them when required. Our research also develops ways to solve complex tasks in Reinforcement Learning by leveraging various kinds of knowledge transfer mechanisms, curriculum learning, teacher-student framework etc. Advances in these directions would make decision-making models sample-efficient and better suited for solving real-world problems. Along the supervised machine learning spectrum, we also focus on problems related to learning with less data, traditionally known as Active Learning, semi-supervised learning, and learning from multiple experts.

Scott Peltier

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My research deals with functional MRI data acquisition and analysis. My areas of interest include brain network connectivity; multimodal imaging; real-time fMRI neurofeedback; and the use of multivariate and data-driven analysis techniques, including machine learning.

Benjamin Goldstein

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Benjamin Goldstein is Assistant Professor of Environment and Sustainability and head of the Sustainable Urban-Rural Futures (SURF) lab. The SURF Lab (www.surf-lab.ca) studies and emphasizes urban sustainability at multiple scales. Through his work at the SURF Lab, Benjamin helps understand how urban processes and urban form drive the consumption of materials and energy in cities and produce environmental change inside and outside cities. He develops methods and tools to quantify the scale of these changes and the locations where they occur using life cycle assessment, input-output analysis, geospatial data, and approaches from data science. Benjamin is particularly interested in combining quantitative methods with theory rooted in social science to explore multiple dimensions of sustainability and address issues of distributive justice. His topical foci include urban food systems (esp. urban agriculture), agri-commodities, residual resource engineering, global supply chains, sustainable production and consumption, and energy systems.