Ayumi Fujisaki-Manome

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Fujisaki-Manome’s research program aims to improve predictability of hazardous weather, ice, and lake/ocean events in cold regions in order to support preparedness and resilience in coastal communities, as well as improve the usability of their forecast products by working with stakeholders. The main question Fujisaki-Manome’s research aims to address is: what are the impacts of interactions between ice and oceans / ice and lakes on larger scale phenomena, such as climate, weather, storm surges, and sea/lake ice melting? Fujisaki-Manome primarily uses numerical geophysical modeling and machine learning to address the research question; and scientific findings from the research feed back into the models and improve their predictability. Her work has focused on applications to the Great Lakes, the Alaska’s coasts, Arctic Ocean, and the Sea of Okhotsk.

View MIDAS Faculty Research Pitch, Fall 2021

Areal fraction of ice cover in the Great Lakes in January 2018 modeled by the unstructured grid ice-hydrodynamic numerical model.

Marie O’Neill

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My research interests include health effects of air pollution, temperature extremes and climate change (mortality, asthma, hospital admissions, birth outcomes and cardiovascular endpoints); environmental exposure assessment; and socio-economic influences on health.
Data science tools and methodologies include geographic information systems and spatio-temporal analysis, epidemiologic study design and data management.

Carina Gronlund

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As an environmental epidemiologist and in collaboration with government and community partners, I study how social, economic, health, and built environment characteristics and/or air quality affect vulnerability to extreme heat and extreme precipitation. This research will help cities understand how to adapt to heat, heat waves, higher pollen levels, and heavy rainfall in a changing climate.

Xianglei Huang

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Prof. Huang is specialized in satellite remote sensing, atmospheric radiation, and climate modeling. Optimization, pattern analysis, and dimensional reduction are extensively used in his research for explaining observed spectrally resolved infrared spectra, estimating geophysical parameters from such hyperspectral observations, and deducing human influence on the climate in the presence of natural variability of the climate system. His group has also developed a deep-learning model to make a data-driven solar forecast model for use in the renewable energy sector.

Rajiv Saran

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Dr. Saran is an internationally recognized expert in kidney disease research – specifically, in the area of kidney disease surveillance and epidemiology. From 2014 – 2019, he served as Director of the United States Renal Data System (USRDS; www.usrds.org), a ‘gold standard’ for kidney disease data systems, worldwide. Since 2006 he has been Co-Principal Investigator for the Centers for the Disease Control and Prevention’s (CDC’s) National CKD Surveillance System for the US, a one of a kind project that complements the USRDS, while focusing on upstream surveillance of CKD and its risk factors (www.cdc.org/ckd/surveillance). Both projects have influenced policy related to kidney disease in the US and were cited extensively in the July 2019 Advancing American Kidney Health Federal policy document. Dr. Saran led the development of the first National Kidney Disease Information System (VA-REINS), for the Department of Veterans Affairs (VA), funded by the VA’s Center for Innovation, and one that led to the VA recognizing the importance of kidney disease as a health priority for US veterans. Dr. Saran has recently (2018-2021) been funded on a spin off project from VA REINS for investigation of ‘hot-spot’ of kidney disease among US Veterans involving both risk-prediction and geospatial analyses – a modern approach to health system big data being used for prevention and population health improvement, using kidney disease as an example. This approach has broad application for prevention and optimizing management of major chronic diseases.

Catherine Hausman

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Catherine H. Hausman is an Associate Professor in the School of Public Policy and a Research Associate at the National Bureau of Economic Research. She uses causal inference, related statistical methods, and microeconomic modeling to answer questions at the intersection of energy markets, environmental quality, climate change, and public policy.

Recent projects have looked at inequality and environmental quality, the natural gas sector’s role in methane leaks, the impact of climate change on the electricity grid, and the effects of nuclear power plant closures. Her research has appeared in the American Economic Journal: Applied Economics, the American Economic Journal: Economic Policy, the Brookings Papers on Economic Activity, and the Proceedings of the National Academy of Sciences.

Mihaela (Miki) Banu

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In the area of multi-scale modeling of manufacturing processes: (a) Models for understanding the mechanisms of forming and joining of lightweight materials. This new understanding enables the development of advanced processes which remove limitations of current state-of-the-art capabilities that exhibit limited formability of high strength lightweight alloys, and limited reproducibility of joining quality; (b) Innovative multi-scale finite element models for ultrasonic welding of battery tabs (resulting in models adopted by GM for designing and manufacturing batteries for the Chevy Volt), and multi-scale models for ultrasonic welding of short carbon fiber composites (resulting in models adopted by GM for designing and manufacturing assemblies made of carbon fiber composites with metallic parts); (c) Data-driven algorithms of prediction geometrical and microstructural integrity of the incremental formed parts. Machine learning is used for developing fast and robust methods to be integrated into the designing process and replace finite element simulations.

Maureen Sartor

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My lab has two main areas of focus: molecular characteristics of head and neck cancer, and the intersection of regulatory genomics and pathway analysis. With head and neck cancer, we study tumor subtypes and biomarkers of prognosis, treatment response, and recurrence. We perform integrative omics analyses, dimension reduction methods, and prediction techniques, with the ultimate goal of identifying patient subsets who would benefit from either an additional targeted treatment or de-escalated treatment to increase quality of life. For regulatory genomics and pathway analysis, we develop statistical tests taking into account important covariates and other variables for weighting observations.