Dr. Briana Mezuk

Briana Mezuk

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Dr. Mezuk is the Director of the Center for Social Epidemiology and Population Health and is an Associate Chair in the Department of Epidemiology at the University of Michigan School of Public Health. She is a psychiatric epidemiologist whose research focuses on understanding the intersections of mental and physical health. Much of her work has examined the consequences of depression for medical morbidity and functioning in mid- and late-life, with particular attention to metabolic diseases such as diabetes and frailty. She is also the Director of the Michigan Integrative Well-Being and Inequalities (MIWI) Training Program, a NIH-funded methods training program that supports innovative, interdisciplinary research on the interrelationships between mental and physical health as they relate to health disparities. She is using data science tools to analyze textual data from the National Violent Death Reporting System, with the goal of better understanding how major life transitions relate to suicide risk over the lifespan. She is committed to translating research into practice, and she writes a blog for Psychology Today called “Ask an Epidemiologist.”

Nazanin Andalibi

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I use mixed methods to investigate social media’s use and roles in relation to self-disclosure, social support exchange, and other disclosure behavior outcomes and responses to them. I concentrate on experiences that can be distressing, traumatizing, isolating, or stigmatized, and contribute to poor wellbeing. Broadly, in these contexts, I address how we can design social computing systems that facilitate beneficial sensitive disclosures and desired disclosure outcomes such as (but not limited to) exchanging social support, meaningful interactions, reciprocal disclosures, and reduced stigma. Some contexts my work has focused on in the past include: mental health, sexual abuse, and pregnancy loss.

The research trajectory described above focuses on other social media users as information/disclosure recipients. I also investigate people’s attitudes and concerns when companies and algorithms are audiences or recipients of one’s sensitive information. This work goes beyond social media applications to include other types of social technologies. I critically examine the ways emerging technologies such as emotion artificial intelligence may engage with humans in times of distress or in otherwise private and personal settings. I explore the extent to which designing these technologies is appropriate in different contexts, and investigate what it would take for them to be sensitive to and foreground people’s values, needs, and desires.

Xiaoquan William Wen

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Xiaoquan (William) Wen is an Associate Professor of Biostatistics. He received his PhD in Statistics from the University of Chicago in 2011 and joined the faculty at the University of Michigan in the same year. His research centers on developing Bayesian and computational statistical methods to answer interesting scientific questions arising from genetics and genomics.

In the applied field,  he is  particularly interested in seeking statistically sound and computationally efficient solutions to scientific problems in the areas of genetics and functional genomics.
Quantifying tissue-specific expression quantitative trait loci (eQTLs) via Bayesian model comparison

Quantifying tissue-specific expression quantitative trait loci (eQTLs) via Bayesian model comparison

Davon Norris

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I try to understand how our tools for determining what is valuable, worthwhile, or good are implicated in patterns of inequality with an acute concern for racial inequality. Often, this means my work investigates the functioning and consequences of a range of scores or ratings, from the less complex government credit ratings to the extremely complex algorithmic scores like consumer credit scores.

In related work, as a part of a multi-university team of researchers, I am using administrative credit report data from one of the largest credit reporting agencies to study credit and debt outcomes for millions of consumers in the United States.

Michael Craig

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Michael is an Assistant Professor of Energy Systems at the University of Michigan’s School for Environment and Sustainability and PI of the ASSET Lab. He researches how to equitably reduce global and local environmental impacts of energy systems while making those systems robust to future climate change. His research advances energy system models to address new challenges driven by decarbonization, climate adaptation, and equity objectives. He then applies these models to real-world systems to generate decision-relevant insights that account for engineering, economic, climatic, and policy features. His energy system models leverage optimization and simulation methods, depending on the problem at hand. Applying these models to climate mitigation or adaptation in real-world systems often runs into computational limits, which he overcomes through clustering, sampling, and other data reduction algorithms. His current interdisciplinary collaborations include climate scientists, hydrologists, economists, urban planners, epidemiologists, and diverse engineers.

Hamid Ghanbari

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My research focuses on using digital health solutions, signal processing, machine learning and ecological momentary assessment to understand the physiological and psychological determinants of symptoms in patients with atrial fibrillation. I am building a research framework for rich data collection using smartphone apps, medical records and wearable sensors. I believe that creating a multidimensional dataset to study atrial fibrillation will yield important insights and serve as model for studying all chronic medical conditions.

Zheng Song

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I received my second PhD in Computer Science (with a focus on distributed systems and software engineering) from Virginia Tech USA in 2020, and the first PhD (with a focus on wireless networking and mobile computing) from Beijing University of Posts and Telecommunications China in 2015. I received the Best Paper Award from IEEE International Conference on Edge Computing in 2019. My ongoing research projects include measuring the data quality of web services and using federated learning to improve indoor localization accuracy.

Susan Hautaniemi Leonard

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I am faculty at ICPSR, the largest social science data archive in the world. I manage an education research pre-registration site (sreereg.org) that is focused on transparency and replicability. I also manage a site for sharing work around record linkage, including code (linkagelibrary.org). I am involved in the LIFE-M project (life-m.org), recently classifying the mortality data. That project uses cutting-edge techniques for machine-reading handwritten forms.

Mortality rates for selected causes in the total population per 1,000, 1850–1912, Holyoke and Northampton, Massachusetts

Matias del Campo

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The goal of this project is the creation of a crucial building block of the research on AI and Architecture – a database of 3D models necessary to successfully run Artificial Neural Networks in 3D. This database is part of the first stepping-stones for the research at the AR2IL (Architecture and Artificial Intelligence Laboratory), an interdisciplinary Laboratory between Architecture (represented by Taubman College of Architecture of Urban Planning), Michigan Robotics, and the CS Department of the University of Michigan. A Laboratory dedicated to research specializing in the development of applications of Artificial Intelligence in the field of Architecture and Urban Planning. This area of inquiry has experienced an explosive growth in recent years (triggered in part by research conducted at UoM), as evidenced for example by the growth in papers dedicated to AI applications in architecture, as well as in the investment of the industry in this area. The research funded by this proposal would secure the leading position of Taubman College and the University of Michigan in the field of AI and Architecture. This proposal would also address the current lack of 3D databases that are specifically designed for Architecture applications.

The project “Generali Center’ presents itself as an experiment in the combination of Machine Learning processes capable of learning the salient features of a specific architecture style – in this case, Brutalism- in order to generatively perform interpolations between the data points of the provided dataset. These images serve as the basis of a pixel projection approach that results in a 3D model.

Stefanus Jasin

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My research focus the application and development of new algorithms for solving complex business analytics problems. Applications vary from revenue management, dynamic pricing, marketing analytics, to retail logistics. In terms of methodology, I use a combination of operations research and machine learning/online optimization techniques.