“Neighborhood Environments as Socio-Techno-bio Systems: Water Quality, Public Trust, and Health in Mexico City (NESTSMX)” is an NSF-funded multi-year collaborative interdisciplinary project that brings together experts in environmental engineering, anthropology, and environmental health from the University of Michigan and the Instituto Nacional de Salud Pública. The PI is Elizabeth Roberts (anthropology), and the co-PIs are Brisa N. Sánchez (biostatistics), Martha M Téllez-Rojo (public health), Branko Kerkez (environmental engineering), and Krista Rule Wigginton (civil and environmental engineering). Our overarching goal for NESTSMX is to develop methods for understanding neighborhoods as “socio-techno-bio systems” and to understand how these systems relate to people’s trust in (or distrust of) their water. In the process, we will collectively contribute to our respective fields of study while we learn how to merge efforts from different disciplinary backgrounds.
NESTSMX works with families living in Mexico City, that participate in an ongoing longitudinal birth-cohort chemical-exposure study (ELEMENT (Early Life Exposures in Mexico to ENvironmental Toxicants, U-M School of Public Health). Our research involves ethnography and environmental engineering fieldwork which we will combine with biomarker data previously gathered by ELEMENT. Our focus will be on the infrastructures and social structures that move water in and out of neighborhoods, households, and bodies.
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.
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.
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.
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.
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.
Machine learning approaches and new data science algorithms are an emerging frontier for the atmospheric sciences. We explore whether newly developed physics-guided machine learning algorithms trained with atmospheric model data or observations can serve as emulators for physical processes in weather and climate models, such as the time-consuming solar radiation code, precipitation mechanisms, or the shallow or deep convection cloud schemes. A second, less aggressive approach is to utilize machine learning approaches for the estimation of uncertain parameters in the subgrid-scale physical parameterizations of atmospheric models. We use idealized weather and climate model configurations to intercompare the pros and cons of various machine learning algorithms, such as linear regression, random forests, boosted forests, artificial neural networks and deep neural networks with and without convolutions. In addition, we are interested in machine learning approaches to understand and foster the predictability of the climate system over subseasonal-to seasonal (weeks-to-months) time scales.
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.