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.

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.

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.

Zach Landis-Lewis

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My research focuses on the use and effectiveness of coaching and appreciation feedback in healthcare. I lead a team that develops a software-based precision feedback system to generate messages about performance to healthcare professionals and teams. My work involves the processing of performance data to detect signals of motivating information that can be delivered with algorithmically prioritized messages, to support performance improvement and sustainment. I lead the DISPLAY-Lab, which collaborates with researchers in a range of clinical and health-related domains, including biomedical informatics, implementation science, and human-centered design.

Cyrus Omar

Cyrus Omar

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I lead the Future of Programming Lab (FP Lab), where we design modern user interfaces for modern programming languages. Much of how we program today is rooted in tools designed 40+ years ago, e.g. how we enter code (using simple text editing, which leads to profligate parse errors), how we validate code (using tests or impoverished type systems), how we explore code (in a slow, batched, textual manner), how we communicate change (by throwing away the edits we performed and forcing diff algorithms to guess what we did), and so on. My lab develops new programming language and editor mechanisms, starting from theoretical foundations in mathematics and building up to human interfaces.

Integrating live GUIs into programs with holes

Integrating live GUIs into programs with holes

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

Cheng Li

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My research focuses on developing advanced numerical models and computational tools to enhance our understanding and prediction capabilities for both terrestrial and extraterrestrial climate systems. By leveraging the power of data science, I aim to unravel the complexities of atmospheric dynamics and climate processes on Earth, as well as on other planets such as Mars, Venus, and Jupiter.

My approach involves the integration of large-scale datasets, including satellite observations and ground-based measurements, with statistical methods and sophisticated machine learning algorithms including vision-based large models. This enables me to extract meaningful insights and improve the accuracy of climate models, which are crucial for weather forecasting, climate change projections, and planetary exploration.

Dani Jones

Dani Jones

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Dani Jones’ research program drives CIGLR’s portfolio of research in data science, machine learning, and artificial intelligence, as applied to physical limnology, weather forecasting, water cycle predictions, ecology, and observing system design. This research program aims is to advance societal adaptations to the effects of climate change, including flooding of coasts, rivers, and cities. Dani’s background is in physical oceanography, with specific expertise in adjoint modeling for comprehensive sensitivity analysis and unsupervised classification for data analysis, mostly applied to the North Atlantic and Southern Ocean. In Dani’s current role, they are establishing CIGLR’s new Artificial Intelligence Laboratory, leveraging the institute’s extensive observing assets, datasets, modeling capacity, interdisciplinary expertise, and numerous regional and international partnerships.

Tian An Wong

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Analysis of policing technology and police data, including impact assessment of surveillance technology, media sentiment analysis, and fatal police violence. Methods include topological data analysis, natural language processing, multivariate time series analysis, difference-in-differences, and complex networks.

 


Research Highlights