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.

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.

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.

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.

Allen Flynn

Allen Flynn

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I study medication prescription information and work on teams that create and evaluate applications of natural language processing to medication prescription information. The main thrust of my research in pharmacy informatics focuses on automating subtasks that pertain to medication prescribing by clinicians and medication prescription review by pharmacists. In addition, I work with the Knowledge Systems Lab in the Department of Learning Health Sciences to specify model repository requirements for making AI/ML models findable, accessible. interoperable, and reusable.

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

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.

Qiong Yang

Qiong Yang

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My research program at the University of Michigan (UM) integrates the fields of biophysics, quantitative systems biology, and bottom-up synthetic biology to understand complex stochastic cellular and developmental processes in early embryos.
We have developed innovative computational and experimental techniques in microfluidics and imaging to allow high-throughput quantitative manipulation and single-cell lineage tracking of cellular spatiotemporal dynamical processes in various powerful in vitro and in vivo systems we established in my lab. These systems range from cell-free extracts, synthetic cells reconstituted in microemulsion droplets, presomitic mesoderm (PSM) and progenitor zone (PZ) cells dissociated from the zebrafish tail buds, their re-aggregated 2D and 3D cell-cell communications, ex vivo live tissue explants, and live embryos.
Our current research questions center around the understanding of the design-function relation of robust biological timing, growth, and patterning, how individual molecules and cells communicate to generate collective patterns, and how biochemical, biophysical, and biomechanical signals work together to shape morphogenesis during early embryo development.

Liyue Shen

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My research interest is in Biomedical AI, which lies in the interdisciplinary areas of machine learning, computer vision, signal and image processing, medical image analysis, biomedical imaging, and data science. I am particularly interested in developing efficient and reliable AI/ML-driven computational methods for biomedical imaging and informatics to tackle real-world biomedicine and healthcare problems, including but not limited to, personalized cancer treatment, and precision medicine.

In the field of AI/ML, we focus on developing reliable, generalizable, data-efficient machine learning and deep learning algorithms by exploiting prior knowledge from the physical world, such as: Prior-integrated learning for data-efficient ML Uncertainty awareness for trustworthy ML. In the field of Biomedicine, we focus on developing efficient computational methods for biomedical imaging and biomedical data analysis to advance precision medicine and personalized treatment, such as: Multi-modal data analysis for decision making Clinical trial translation for real-world deployment.

In the field of AI/ML, we focus on developing reliable, generalizable, data-efficient machine learning and deep learning algorithms by exploiting prior knowledge from the physical world, such as: Prior-integrated learning for data-efficient ML Uncertainty awareness for trustworthy ML. In the field of Biomedicine, we focus on developing efficient computational methods for biomedical imaging and biomedical data analysis to advance precision medicine and personalized treatment, such as: Multi-modal data analysis for decision making Clinical trial translation for real-world deployment.

What are some of your most interesting projects?

Our goal is to develop efficient and reliable AI/ML-driven computational methods for biomedical imaging and informatics to tackle real-world biomedicine and healthcare problems. We hope the technology advancement in AI and ML can help us to better understand human health in different levels. Specifically, we develop Biomedical AI in different parts, including:
– AI in Biomedical Imaging: develop novel machine learning algorithms to advance biomedical imaging techniques for obtaining computational images with improved quality. Specifically, relevant topics include but not limited to: Implicit neural representation learning; Diffusion model / Score-based generative model; Physics-aware / Geometry-informed deep learning.
– AI in Biomedical Image Processing and Bioinformatics: develop robust and efficient machine learning algorithms to extract useful information from multimodal biomedical data for assisting decision making and precision medicine. Specifically, relevant topics include but not limited to: Multimodal representation learning; Robust learning with missing data / noisy labeling; Data-efficient learning such as self- / un- / semi-supervised learning with limited data / labels.

How did you end up where you are today?

I am an assistant professor in the ECE Division of the Electrical Engineering and Computer Science department of the College of Engineering, University of Michigan – Ann Arbor. Before this, I received my Ph.D. degree from the Department of Electrical Engineering, Stanford University. I obtained her Bachelor’s degree in Electronic Engineering from Tsinghua University in 2016. I is the recipient of Stanford Bio-X Bowes Graduate Student Fellowship (2019-2022), and was selected as the Rising Star in EECS by MIT and the Rising Star in Data Science by The University of Chicago in 2021.


Accomplishments and Awards