Vineet Kamat

Vineet Kamat

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My group conducts research in automation and robotics to improve work processes in the construction, operation, and maintenance of civil infrastructure and the built environment. Our research has developed several licensable technologies that include visualization, perception, and modeling techniques to help on-site construction robots with autonomous decision making. We are particularly interested in exploring new methods for enabling collaborative work strategies for human-robot teams jointly performing field construction work. In addition, we are also interested in exploring methods to integrate data to support semi-autonomous mobility for people with physical disabilities in the urban built environment.

Data-Driven Co-Robotic Field Construction Work

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.

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.

Sabine Loos

Sabine Loos

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My research focuses on natural hazards and disaster information, everything from understanding where disaster data comes from, how it’s used, and its implications to design improved disaster information systems that prioritize the human experience and lead to more effective and equitable outcomes.

My lab takes a user-centered and data-driven approach. We aim to understand user needs and the effect of data on users’ decisions through qualitative research, such as focus groups or workshops. We then design new information systems through geospatial/GIS analysis, risk analysis, and statistical modeling techniques. We often work with earth observation, sensor, and survey data. We consider various aspects of disaster information, whether it be the hazard, its physical impacts, its social impacts, or a combination of the three.

I also focus on the communication of information, through data visualization techniques, and host a Risk and Resilience DAT/Artathon to build data visualization capacity for early career professionals.

Geospatial model for predicting inequities in recovery from the 2015 Nepal earthquake

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.

Gen Li

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Dr. Gen Li is an Assistant Professor in the Department of Biostatistics. He is devoted to developing new statistical methods for analyzing complex biomedical data, including multi-way tensor array data, multi-view data, and compositional data. His methodological research interests include dimension reduction, predictive modeling, association analysis, and functional data analysis. He also has research interests in scientific domains including microbiome and genomics.

Novel tree-guided regularization methods can identify important microbial features at different taxonomic ranks that are predictive of the clinical outcome.

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.

Ken Kollman

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I have been involved in the building of data infrastructure in the study of elections, political systems, violence, geospatial units, demographics, and topography. This infrastructure will eventually lead to the integration of data across many domains in the social, health, population, and behavioral sciences. My core research interests are in elections and political organizations.

Nicholas Henderson

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My research primarily focuses on the following main themes: 1) development of methods for risk prediction and analyzing treatment effect heterogeneity, 2) Bayesian nonparametrics and Bayesian machine learning methods with a particular emphasis on the use of these methods in the context of survival analysis, 3) statistical methods for analyzing heterogeneity in risk-benefit profiles and for supporting individualized treatment decisions, and 4) development of empirical Bayes and shrinkage methods for high-dimensional statistical applications. I am also broadly interested in collaborative work in biomedical research with a focus on the application of statistics in cancer research.