Peter Song

Peter Song

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My research interests lie in two major fields: In the field of statistical methodology, my interests include data integration, distributed inference, federated learning and meta learning, high-dimensional statistics, mixed integer optimization, statistical machine learning, and spatiotemporal modeling. In the field of empirical study, my interests include bioinformatics, biological aging, epigenetics, environmental health sciences, nephrology, nutritional sciences, obesity, and statistical genetics.

Mariel Lavieri

Mariel Lavieri

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Dr. Lavieri’s group is focused on creating novel modeling frameworks that utilize the rich datasets available in healthcare to personalize screening, monitoring, and treatment decisions of chronic disease patients. Her group has also created models for health workforce and capacity planning.

Elizabeth Bondi-Kelly

Elizabeth Bondi-Kelly

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My research interests lie broadly in the area of artificial intelligence (AI) for social impact, particularly spanning the fields of multi-agent systems and data science for conservation and public health.

Thuy Le

Thuy Le

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Dr. Le is an assistant research scientist at the University of Michigan Department of Health Management and Policy. Dr Le is also a member of the UM/Georgetown TCORS Center for the Assessment of Tobacco Regulations (CAsToR). Dr. Le is interested in mathematical modeling for cancer- and tobacco-related problems, and machine-learning applications in tobacco regulatory science. Dr. Le has developed mathematical models to evaluate the benefits and harms of breast cancer mammography and predict the number of white blood cells during acute lymphoblastic maintenance therapy in children. Dr. Le’s recent work focuses on employing mathematical models to quantify the burden of menthol cigarettes on public health and estimate the smoking cessation rate. Dr. Le is working on applying machine learning techniques to predict and understand smoking behaviors.

Ryan Stidham

Ryan Stidham

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Dr. Stidham is an academic gastroenterologist specializing in medical image analysis in Crohn’s disease, ulcerative colitis, inflammatory bowel diseases (IBD), and gastroenterology conditions at large. His research is focused on developing new measures of disease activity to power automated care models and clinical decision support systems in IBD with a focus on medical image analysis and new technology development. His work has focused on automation of existing IBD disease measures that relying on colonoscopy, CT, MRI, and ultrasound using neural networks and novel image analysis approaches. Dr. Stidham is also developing new measures of disease activity, inflammation, and fibrosis that leverage advances in image segmentation, transfer learning, signals analysis, and fuzzy network approaches as well as collaborating for development of new image acquisition modalities. Finally, his team has active projects in collaboration with the Department of Learning Health Sciences for merging data from clinical office notes with imaging data using computational linguistics approaches. His work has been supported by the NIH, DOD, NSF, and several large investigator-initiated industry collaborations.

Picture of Thomas Schwarz

Thomas A. Schwarz

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Professor Schwarz is an experimental particle physicist who has performed research in astro-particle physics, collider physics, as well as in accelerator physics and RF engineering. His current research focuses on discovering new physics in high-energy collisions with the ATLAS experiment at the Large Hadron Collider (LHC) at CERN. His particular focus is in precision measurements of properties of the Higgs Boson and searching for new associated physics using advanced AI and machine learning techniques.

Picture of David Brang

David Brang

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My lab studies how information from one sensory system influences processing in other sensory systems, as well as how this information is integrated in the brain. Specifically, we investigate the mechanisms underlying basic auditory, visual, and tactile interactions, synesthesia, multisensory body image perception, and visual facilitation of speech perception. Our current research examines multisensory processes using a variety of techniques including psychophysical testing and illusions, fMRI and DTI, electrophysiological measures of neural activity (both EEG and iEEG), and lesion mapping in patients with brain tumors. Our intracranial electroencephalography (iEEG/ECoG/sEEG) recordings are a unique resource that allow us to record neural activity directly from the human brain from clinically implanted electrodes in patients. These recordings are collected while patients perform the same auditory, visual, and tactile tasks that we use in our other behavioral and neuroimaging studies, but iEEG measures have millisecond temporal resolution as well as millimeter spatial precision, providing unparalleled information about the flow of neural activity in the brain. We use signal processing techniques and machine learning methods to identify how information is encoded in the brain and how it is disrupted in clinical contexts (e.g., in patients with a brain tumor).

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.

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