Allison Earl

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My primary research interests are understanding the causes and consequences of biased selection and attention to persuasive information, particularly in the context of health promotion. Simply stated, I am interested in what we pay attention to and why, and how this attention (or inattention) influences attitudinal and behavioral outcomes, such as persuasion and healthy behavior. In particular, my work has addressed disparities in attention to information about HIV prevention for African-Americans compared to European-Americans as a predictor of disparities in health outcomes. I am also exploring barriers to attention to health information by African-Americans, including the roles of stigma, shame, fear, and perceptions of irrelevance. At a more basic attitudes and persuasion level, I am currently pursuing work relevant to how we select information for liked versus disliked others, and how the role of choice influences how we process information we agree versus disagree with.

Omar Jamil Ahmed

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The Ahmed lab studies behavioral neural circuits and attempts to repair them when they go awry in neurological disorders. Working with patients and with transgenic rodent models, we focus on how space, time and speed are encoded by the spatial navigation and memory circuits of the brain. We also focus on how these same circuits go wrong in Alzheimer’s disease, Parkinson’s disease and epilepsy. Our research involves the collection of massive volumes of neural data. Within these terabytes of data, we work to identify and understand irregular activity patterns at the sub-millisecond level. This requires us to leverage high performance computing environments, and to design custom algorithmic and analytical signal processing solutions. As part of our research, we also discover new ways for the brain to encode information (how neurons encode sequences of space and time, for example) – and the algorithms utilized by these natural neural networks can have important implications for the design of more effective artificial neural networks.

Daniel P. Keating

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The primary tools currently in use are variations of linear models (regression, MLM, SEM, and so on) as we pursue the initial aims of the NICHD funded work. We are expanding into new areas that require new tools. Our adolescent sample is diverse, selected through quota sampling of high schools close enough to UM to afford the use of neuroimaging tools, but it is not population representative. To overcome this, we have begun work to calibrate our sample with the nationally representative Monitoring the Future study, implementing pseudo-weighting and multilevel regression and post-stratification. To enable much more powerful analyses, we are aiming toward the harmonization of multiple, high quality longitudinal databases from adolescence through early adulthood. This would benefit traditional analyses by allowing cross-validation with high power, but also provide opportunities for newer data science tools such as computational modeling and machine learning approaches.

Anne Fernandez

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Dr. Fernandez is a clinical psychologist with extensive training in both addiction and behavioral medicine. She is the Clinical Program Director at the University of Michigan Addiction Treatment Service. Her research focuses on the intersection of addiction and health across two main themes: 1) Expanding access to substance use disorder treatment and prevention services particularly in healthcare settings and; 2) applying precision health approaches to addiction-related healthcare questions. Her current grant-funded research includes an NIH-funded randomized controlled pilot trial of a preoperative alcohol intervention, an NIH-funded precision health study to leverage electronic health records to identify high-risk alcohol use at the time of surgery using natural language processing and other machine-learning based approaches, a University of Michigan funded precision health award to understand and prevent new persistent opioid use after surgery using prediction modeling, and a federally-funded evaluation of the state of Michigan’s substance use disorder treatment expansion.

Stephan F. Taylor

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STEPHAN F. TAYLOR is a professor of psychiatry and Associate Chair for Research and Research Regulatory Affairs in the Department of Psychiatry; and an adjunct professor of psychology.

His work uses brain mapping and brain stimulation to study and treat serious mental disorders such as psychosis, refractory depression and obsessive-compulsive disorder. Data science techniques area applied in the analysis of high dimensional functional magnetic resonance imaging datasets and meso-scale brain networks, using supervised and unsupervised techniques to interrogate brain-behavior correlations relevant for psychopathological conditions. Clinical-translation work with brain stimulation, primarily with transcranial magnetic stimulation, is informed by mapping meso-scale networks to guide treatment of conditions such as depression. Future work seeks to use machine learning to identify treatment predictors and match individual patients to specific treatments.

Frederick George Conrad

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Fred Conrad’s research concerns the development of new methods and data sources for conducting social research. His work is largely focused on survey methodology, but he also explores the use of social media content as a complement to survey data and as a source of large-scale qualitative insights. His focus is on data quality and reducing measurement error. For example, live video interviews promote more thoughtful responses, e.g., less straightlining – the tendency to give the same answer to a battery of survey questions, but they also promote less candor when answering questions on sensitive topics. Measurement error in social media include misclassification in the automated interpretation of content using methods such as sentiment analysis and topic modeling, as well as selective self-presentation (only posting flattering content). Equally challenging is not knowing the extent to which users differ from the population to which one might wish to generalize results.

Lisa Levinson

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My research interests are in natural language semantics and psycholinguistics, focusing on verbs. I conduct behavioral psycholinguistic experiments with methodologies such as self-paced reading and maze tasks, as well as surveys of linguistic and semantic judgments. I also study semantic variation using corpora and datasets such as the Twitter Decahose, to better understand how words have developed diverging meanings in different communities, age groups, or regions. I use primarily R and Python to collect, manage, and analyze data. I direct the UM WordLab in the linguistics department, working with students (especially undergraduates) on experimental and computational research focusing on lexical representations.

Zhongming Liu

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My research is at the intersection of neuroscience and artificial intelligence. My group uses neuroscience or brain-inspired principles to design models and algorithms for computer vision and language processing. In turn, we uses neural network models to test hypotheses in neuroscience and explain or predict human perception and behaviors. My group also develops and uses machine learning algorithms to improve the acquisition and analysis of medical images, including functional magnetic resonance imaging of the brain and magnetic resonance imaging of the gut.

We use brain-inspired neural networks models to predict and decode brain activity in humans processing information from naturalistic audiovisual stimuli.

Jason Goldstick

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I am a statistician and my research focuses on applied public health work in a variety of fields specific to injury prevention, including substance use, violence, motor vehicle crash, and traumatic brain injury. Within those applications, I apply analytic methods for longitudinal data analysis, spatial and spatio-temporal data analysis, and predictive modeling (e.g., for clinical prediction of future injury risk applied to injuries like stroke, Benzodiazepine overdose, and firearm injury). I am also MPI of the System for Opioid Overdose Surveillance–a near-real-time system for monitoring fatal and nonfatal overdoses in Michigan; the system generates automated spatial and temporal summaries of recent overdose trends.