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.

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.

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.

Mohamed Abouelenien

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Mohamed Abouelenien’s areas of interest broadly cover data science topics, including applied machine learning, computer vision, and natural language processing. He established the Affective Computing and Multimodal Systems Lab (ACMS) which focuses on modeling human behavior and developing multimodal approaches for different applications. He has worked on a number of projects in these areas, including multimodal deception detection, multimodal sensing of drivers’ alertness levels and thermal discomfort, distraction detection, circadian rhythm modeling, emotion and stress analysis, automated scoring of students’ progression, sentiment analysis, ensemble learning, and image processing, among others. His research is funded by Ford Motor Company (Ford), Educational Testing Service (ETS), Toyota Research institute (TRI), and Procter & Gamble (P&G). Abouelenien has published in several top venues in IEEE, ACM, Springer, and SPIE. He also served as a reviewer for IEEE transactions and Elsevier journals and served as a program committee member for multiple international conferences.

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

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.

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.

Bing Ye

Bing Ye

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The focus of our research is to address (1) how neuronal development contributes to the assembly and function of the nervous system, and (2) how defects in this process lead to brain disorders. We take a multidisciplinary approach that include genetics, cell biology, developmental biology, biochemistry, advanced imaging (for neuronal structures and activity), electrophysiology, computation (including machine learning and computer vision) and behavioral studies.

We are currently studying the neural basis for decision accuracy. We established imaging and computational methods for analyzing neural activities in the entire central nervous system (CNS) of the Drosophila larva. Moreover, we are exploring the possibility of applying the biological neural algorithms to robotics for testing these algorithms and for improving robot performance.

A major goal of neuroscience is to understand the neural basis for behavior, which requires accurate and efficient quantifications of behavior. To this end, we recently developed a software tool—named LabGym—for automatic identification and quantification of user-defined behavior through artificial intelligence. This tool is not restricted to a specific species or a set of behaviors. The updated version (LabGym2) can analyze social behavior and behavior in dynamic backgrounds. We are further developing LabGym and other computational tools for behavioral analyses in wild animals and in medicine.

The behavior that this chipmunk performed was identified and quantified by LabGym, an AI-based software tool that the Ye lab developed for quantifying user-defined behaviors.

The behavior that this chipmunk performed was identified and quantified by LabGym, an AI-based software tool that the Ye lab developed for quantifying user-defined behaviors.

What are some of your most interesting projects?

1) Develop AI-based software tools for analyzing the behavior of wild animals and human.
2) Use biology-inspired robotics to test biological neural algorithms.

How did you end up where you are today?

Since my teenage years, I have been curious about how brains (human’s and animals’) work, enjoyed playing with electronics, and learning about computational sciences. My curiosity and opportunities led me to become a neuroscientist. When I had my own research team and the resources to explore my other interests, I started to build simple electronic devices for my neuroscience research and to collaborate with computational scientists who are experts in machine learning and computer vision. My lab now combines these approaches in our neuroscience research.

What makes you excited about your data science and AI research?

I am very excited about the interactions between neuroscience and data science/AI research. This is a new area and has great potential of changing the society.

Yan Chen

Yan Chen

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Yan Chen’s research interests are in behavioral and experimental economics, market and mechanism design. She conducts large-scale randomized field experiments on gig economy platforms to test the efficacy of team formation algorithms on gig worker productivity and retention. She also conducts experiments in online communities to evaluate what increases pro-social behavior. Her experiments are informed by economic theory and causal inference techniques.


Accomplishments and Awards

David Williams

David Williams

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I have several areas of study that touch on the fields of Data Science.

First I am the UM PI of PCORnet a national network of over 80 institutions that support clinical research. PCORnet possesses a common data model allowing for the harmonization of the electronic health record across the network. The common data model is helpful in cohort discovery, development of computable phenotypes, the study of rare diseases, and applications of machine learning for identifying patterns in disease and health care services that can help to form better models of precision care.

My second area of interest is in the use of big data to support behavioral change. PainGuide is a digital pain self-management program developed at UM that offers a variety of evidence-based methods for improving and managing pain. User data can inform AI algorithms to refine content and recommendations for the participants so as to personalize care and improve outcomes.