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.

Photograph of Nicholas Kotov

Nicholas Kotov

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Nicholas A. Kotov is Irving Langmuir Distinguished University Professor in Chemical Sciences at the University of Michigan. He is a pioneer of theoretical foundations and practical implementations of complex systems from ‘imperfect’ nanoparticles that offer a vast field for the application of data science and machine learning. Chiral nanostructures, biomimetic nanocomposites, and graph theoretical representations are the focal points in his current work.  Nicholas is a recipient of more than 60 awards and recognitions. Together with his students, Nicholas founded several startups that commercialized self-assembled nanostructures for the energy, healthcare, and automotive industry. Nicholas is a Fellow of the America Academy of Arts and Sciences and the National Academy of Inventors.  He is an advocate for scientists with disabilities.


Accomplishments and Awards

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.”

Ivy F. Tso

Ivy F. Tso

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My lab researches how the human brain processes social and affective information and how these processes are affected in psychiatric disorders, especially schizophrenia and bipolar disorder. We use behavioral, electrophysiological (EEG), neuroimaging (functional MRI), eye tracking, brain stimulation (TMS, tACS), and computational methods in our studies. One main focus of our work is building and validating computational models based on intensive, high-dimensional subject-level behavior and brain data to explain clinical phenomena, parse mechanisms, and predict patient outcome. The goal is to improve diagnostic and prognostic assessment, and to develop personalized treatments.

Brain activation (in parcellated map) during social and face processing.

Meha Jain

Meha Jain

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​I am an Assistant Professor in the School for Environment and Sustainability at the University of Michigan and am part of the Sustainable Food Systems Initiative. My research examines the impacts of environmental change on agricultural production, and how farmers may adapt to reduce negative impacts. I also examine ways that we can sustainably enhance agricultural production. To do this work, I combine remote sensing and geospatial analyses with household-level and census datasets to examine farmer decision-making and agricultural production across large spatial and temporal scales.

Conducting wheat crop cuts to measure yield in India, which we use to train algorithms that map yield using satellite data


Accomplishments and Awards

Qing Qu

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His research interest lies in the intersection of signal processing, data science, machine learning, and numerical optimization. He is particularly interested in computational methods for learning low-complexity models from high-dimensional data, leveraging tools from machine learning, numerical optimization, and high dimensional geometry, with applications in imaging sciences, scientific discovery, and healthcare. Recently, he is also interested in understanding deep networks through the lens of low-dimensional modeling.


Accomplishments and Awards

Thomas L. Chenevert

Thomas L. Chenevert

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Multi-center clinical trials increasingly utilize quantitative diffusion imaging (DWI) to aid in patient management and treatment response assessment for translational oncology applications. A major source of systematic bias in diffusion was discovered originating from platform-dependent gradient hardware. Left uncorrected, these biases confound quantitative diffusion metrics used for characterization of tissue pathology and treatment response leading to inconclusive findings, and increasing the requisite subject numbers and trial cost. We have developed technology to mitigate systematic diffusion mapping bias that exists on MRI scanners and are in process of deploying this technology for multi-center clinical trials. Another major source of variance and bottleneck in high-throughput analysis of quantitative diffusion maps is segmentation of tumor/tissue volume of interest (VOI) based on intensities and patterns on multi-contrast MR image datasets, as well as reliable assessment of longitudinal change with disease progression or response to treatment. Our goal is development/trial/application AI algorithms for robust (semi-) automated VOI definition in analysis of multi-dimensional MR datasets for oncology trials.

Representative apparent diffusion coefficient (ADC) histograms and map overlays are shown for oncology trials to be supported by this Academic Industrial Partnership (AIP). ADC is used to characterize tumor malignancy of breast cancer, therapeutic effect in head and neck (H&N) and cellular proliferation in bone marrow of myelofibrosis (MF) patients. Relevant clinical outcome metrics are illustrated under histograms for detection sensitivity threshold (to reduce unnecessary breast biopsies (13)), Kaplan-Meier analysis of therapy response (stratified by median SD of H&N metastatic node (23)), and histopathologic proliferation stage (MF cellular infiltration classification).