Anna G. Stefanopoulou

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Energy Transportation related topics: data and simulations of various cleaner and ultimately cost-effective options for transit. exploring techno-economic and environmental issues in electric ride-sharing/hailing vehicles to create clean and convenient alternatives to single-occupancy vehicles. investigation of the location and integration of chargers with energy storage and bi-directional services, along with the connection to distributed renewable power generation such as solar arrays as well as the centralized electric grid.

Powertrain related topics: measurements, models and management of batteries, fuel cells, and engines in automotive and stationary applications.

Ashley Payne

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I am interested in using observations, reanalysis, and numerical modeling to investigate the impacts, characteristics and climatology of extreme precipitation events. I use process-based studies for model evaluation and employ simple experiments to develop a mechanistic understanding of weather extremes and their interaction with climate.

A snapshot of an atmospheric river using MERRA-2 reanalysis. The shading shows total precipitable water (cm) and the contours show the magnitude of integrated vapor transport. Atmospheric rivers (ARs) are narrow and elongated pathways of anomalously strong horizontal water vapor transport in the lower portions of the atmosphere. They are found over most major ocean basins and are generally visible in integrated water vapor imagery poleward of the tropics. Their characteristics, such as landfall location, intensity, and duration, vary on intraseasonal and interannual timescales. This variability directly affects populations through impacts to water resources, hydrological extremes, and potential to contribute to compound hazards, such as the incidence of mudslides in regions recently impacted by wildfire.

Andrea Thomer

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Andrea Thomer is an assistant professor of information at the University of Michigan School of Information. She conducts research in the areas of data curation, museum informatics, earth science and biodiversity informatics, information organization, and computer supported cooperative work. She is especially interested in how people use and create data and metadata; the impact of information organization on information use; issues of data provenance, reproducibility, and integration; and long-term data curation and infrastructure sustainability. She is studying a number of these issues through the “Migrating Research Data Collections” project – a recently awarded Laura Bush 21st Century Librarianship Early Career Research Grant from the Institute of Museum and Library Services. Dr. Thomer received her doctorate in Library and Information Science from the School of Information Sciences at the University of Illinois at Urbana‐Champaign in 2017.

Jeffrey Regier

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Jeffrey Regier received a PhD in statistics from UC Berkeley (2016) and joined the University of Michigan as an assistant professor. His research interests include graphical models, Bayesian inference, high-performance computing, deep learning, astronomy, and genomics.

Samuel K Handelman

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Samuel K Handelman, Ph.D., is Research Assistant Professor in the department of Internal Medicine, Gastroenterology, of Michigan Medicine at the University of Michigan, Ann Arbor. Prof. Handelman is focused on multi-omics approaches to drive precision/personalized-therapy and to predict population-level differences in the effectiveness of interventions. He tends to favor regression-style and hierarchical-clustering approaches, partially because he has a background in both statistics and in cladistics. His scientific monomania is for compensatory mechanisms and trade-offs in evolution, but he has a principled reason to focus on translational medicine: real understanding of these mechanisms goes all the way into the clinic. Anything less that clinical translation indicates that we don’t understand what drove the genetics of human populations.

Peter Adriaens

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My research focus is on the development and application of machine learning tools to large scale financial and unstructured (textual) data to extract, quantify and predict risk profiles and investment grade rating of private and public companies.  Example datasets include social media and financial aggregators such as Bloomberg, Pitchbook, and Privco.

9.9.2020 MIDAS Faculty Research Pitch Video.

Steven J. Katz

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Dr. Katz’s research addresses cancer treatment communication, decision-making, and quality of care. His work aims to examine the dynamics of how precision medicine presents itself in the exam room via provider and patient communication and shared decision-making. Dr. Katz leads the Cancer Surveillance and Outcomes Research Team (CanSORT), an interdisciplinary research program centered at the University of Michigan and focused on population and intervention studies of the quality of care and outcomes of cancer detection and treatment in diverse populations.  Dr. Katz and CanSORT have been collaborating with Surveillance, Epidemiology, and End Results (SEER) cancer registries since 2002 to study breast cancer treatment decision making at the population level. We obtain patient clinical and demographic information from SEER and combine this with surveys of patients and physicians to create comprehensive data sets that enable us to study testing and treatment trends and the challenges of individualizing treatments for breast cancer patients. In 2015 we added a new dimension to our research by partnering with evaluative testing firms to obtain tumor genomic and germline genetic test results for over 30,000 breast and ovarian cancer patients in the states of California and Georgia. We are also pursuing insurance claims data to assist with our analysis of physician network effects.

Steven Katz, MD discusses BRCA and multigene sequence testing at the labs of Ambry Genetics.

Steven Katz, MD discusses BRCA and multigene sequence testing at the labs of Ambry Genetics.

Perry Samson

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The capacity to predict student success depends in part on our ability to understand “educationally purposeful” student behaviors and motivations and the relationship between behaviors and motivations and academic achievement. My research focuses on how to collect student behaviors germane to learning at a higher granularity and analyze the relationships between student performance and behaviors.

Ultimately this research is aimed at designing and constructing an “earlier warning system” wherein student guidance is quasi-automated and informed by motivation, background and behaviors and delivered within weeks of the beginning of classes.

9.9.2020 MIDAS Faculty Research Pitch Video.

Jeremy M G Taylor

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Jeremy Taylor, PhD, is the Pharmacia Research Professor of Biostatistics in the School of Public Health and Professor in the Department of Radiation Oncology in the School of Medicine at the University of Michigan, Ann Arbor. He is the director of the University of Michigan Cancer Center Biostatistics Unit and director of the Cancer/Biostatistics training program. He received his B.A. in Mathematics from Cambridge University and his Ph.D. in Statistics from UC Berkeley. He was on the faculty at UCLA from 1983 to 1998, when he moved to the University of Michigan. He has had visiting positions at the Medical Research Council, Cambridge, England; the University of Adelaide; INSERM, Bordeaux and CSIRO, Sydney, Australia. He is a previously winner of the Mortimer Spiegelman Award from the American Public Health Association and the Michael Fry Award from the Radiation Research Society. He has worked in various areas of Statistics and Biostatistics, including Box-Cox transformations, longitudinal and survival analysis, cure models, missing data, smoothing methods, clinical trial design, surrogate and auxiliary variables. He has been heavily involved in collaborations in the areas of radiation oncology, cancer research and bioinformatics.

I have broad interests and expertise in developing statistical methodology and applying it in biomedical research, particularly in cancer research. I have undertaken research  in power transformations, longitudinal modeling, survival analysis particularly cure models, missing data methods, causal inference and in modeling radiation oncology related data.  Recent interests, specifically related to cancer, are in statistical methods for genomic data, statistical methods for evaluating cancer biomarkers, surrogate endpoints, phase I trial design, statistical methods for personalized medicine and prognostic and predictive model validation.  I strive to develop principled methods that will lead to valid interpretations of the complex data that is collected in biomedical research.

William Currie

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Bill Currie studies how physical, chemical, and ecological processes work together in the functioning of ecosystems such as forests and wetlands.  He studies how human impacts and management alter key ecosystem responses including nutrient retention, carbon storage, plant species interactions, and plant productivity.   Dr. Currie uses computer models of ecosystems, including models in which he leads the development team, to explore ecosystem function across the spectrum from wildland to heavily human-impacted systems.  He often works in collaborative groups where a model is used to provide synthesis.  

He is committed to the idea that researchers must work together across traditional fields to address the complex environmental and sustainability issues of the 21st century.  He collaborates with field ecologists, geographers, remote sensing scientists, hydrologists, and land management professionals.