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.

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.

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

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.

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.

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

Ying Xu

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Xu’s research is focused on the educational applications of artificial intelligence, in particular, natural language processing and speech technologies. She explores how these conversational technologies play the role of social partners or learning companions for children, and leverages AI to empower teachers to co-create learning resources to support their instructional goals. In addition, Xu’s research also aims to identify and actively challenge biases inherent in AI technologies used for educational purposes, with the goal of making these technologies more responsive and responsible to children, parents, and teachers from diverse backgrounds. To carry out her research, Xu closely collaborates with national media producers, including PBS KIDS and Sesame Workshop, as well as industrial partners and local community organizations. Her work has been supported by funding from the National Science Foundation, Schmidt Futures, and the Corporation for Public Broadcasting.

What are some of your most interesting projects?

Most of my work has been focused on partnering with public media to explore how AI can facilitate more active and educationally beneficial ways for children to engage with digital media. For instance, I collaborated with PBS KIDS to develop interactive television shows that allow children to talk to their favorite characters as they watch STEM-related programs. Think about children spending nearly two hours every day watching television. And considering that public media programs are valuable and also accessible learning resources, especially for children from less privileged backgrounds. If we could transform these hundreds of hours of screentime into active STEM learning experiences, that could have profound implications. We’ve carried out multiple studies to test whether these interactive videos indeed help children learn. One consistent finding is that when having interactions with the media character, children comprehend the science concepts better and are also more motivated to think about science problems than the children who watched the broadcast version that does not have the AI-assisted interactions. I remember testing this interactive program with preschoolers and observing their enthusiastic conversations with Elinor, which is the main character of a show. We also found that, when children used our interactive videos at their homes, their parents were more likely to participate in the discussion with their children. This heightened parent involvement could potentially have lasting impact on children’s STEM learning in the long run. With the support from the National Science Foundation and the Corporation for Public Broadcasting, we are working on making our interactive television shows publicly available on PBS KIDS platforms.

What is the most significant scientific contribution you would like to make?

I hope that my research can clarify some of our questions regarding how AI might impact child development. Specifically: How do children interact with, perceive, and learn from conversational technologies or non-human entities in general? Can these technologies actually become social partners for children? Ultimately, I hope my research can unpack the complex interplay among children, their social contexts, and technology. Only then will we be able to harness the unique learning experiences conversational agents can provide and ensure that this technology is integrated into children’s existing social contexts and relationships in ways that enhance their development.


Accomplishments and Awards

Liu Liu

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My primary career interest is making new discoveries through creative thinking and innovative investigations. My long term research interests in the molecular mechanisms of heart regeneration to effectively prolong and improve the lives of heart patients, particularly in the development of a comprehensive understanding of post-translational/epigenetics regulation for cardiac reprogramming based heart therapy. I am developing a novel concept for a post-translational modification (PTM) code that is applicable across different proteins. I am utilizing computational methods to gain insights into the functional implications of PTMs that transcend protein boundaries.