John Barry Ryan

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My research focuses on the subfield of political communication using three primary quantitative methodologies: surveys, experiments (both psychological and behavioral economic), and content coding of text. My research has looked at the content of campaign websites, scholar’s social media accounts, newspaper coverage of elections as well as networked participants involving mock elections in a lab.My research focuses on the subfield of political communication using three primary quantitative methodologies: surveys, experiments (both psychological and behavioral economic), and content coding of text. My research has looked at the content of campaign websites, scholar’s social media accounts, newspaper coverage of elections as well as networked participants involving mock elections in a lab.

David Williams

David Williams

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I have several areas of study that touch on the fields of Data Science.

First I am the UM PI of PCORnet a national network of over 80 institutions that support clinical research. PCORnet possesses a common data model allowing for the harmonization of the electronic health record across the network. The common data model is helpful in cohort discovery, development of computable phenotypes, the study of rare diseases, and applications of machine learning for identifying patterns in disease and health care services that can help to form better models of precision care.

My second area of interest is in the use of big data to support behavioral change. PainGuide is a digital pain self-management program developed at UM that offers a variety of evidence-based methods for improving and managing pain. User data can inform AI algorithms to refine content and recommendations for the participants so as to personalize care and improve outcomes.

Yanna Krupnikov

Yanna Krupnikov

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Yanna Krupnikov uses large survey datasets to consider political attention and political expression.


Accomplishments and Awards

What are some of your most interesting projects?

How did you end up where you are today?

What is the most significant scientific contribution you would like to make?

What makes you excited about your data science and AI research?

What are 1-3 interesting facts about yourself

Aditi Verma

Aditi Verma

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I am interested in how nuclear technologies specifically and complex technologies broadly—and their institutional infrastructures—can be designed in more creative, and participatory ways that are epistemically inclusive of both lay and expert perspectives. To this end, my work focuses on developing a more fundamental understanding of the early stages of the design process to improve design practice and pedagogy, and also improve the tools with which designers of complex systems work. I am particularly interested in developing novel approaches for gathering both qualitative and quantitative data generated throughout the design processes of complex systems and using this data to improve design outcomes. In my work, I focus on three main research questions:

  1. How can a fundamental understanding of design be used to improve design practice, design tools, and engineering pedagogy?
  2. How can design processes be made more open and participatory such that epistemic plurality and inclusivity are achieved as part of the design process?
  3. How can insights from design research be applied to the designs of policies and institutions for the governance — both innovation and regulation — of nuclear technologies?

The picture depicts the Sociotechnical Readiness Level which is a framework which critiques and reimagines the traditional Technology Readiness Level Framework. As part of the new SRL framework, we are developing new metrics and indicators to measure both social and technical aspects of a technology's readiness throughout its design and development processImage: The Sociotechnical Readiness Level, which is a framework which critiques and reimagines the traditional Technology Readiness Level Framework. As part of the new SRL framework, we are developing new metrics and indicators to measure both social and technical aspects of a technology’s readiness throughout its design and development process.

What are some of your most interesting projects?

Two new projects I am most excited about are (1) developing a sociotechnical readiness level framework for the assessment of novel technologies and (2) diagnosing the causes of cost overruns in the early stages of technology design.
In the first project, we are critiquing and reimagining the traditional Technology Readiness Level framework to capture both societal and engineering aspects of a technology’s readiness and integrate them into a single framework. As part of this work, we will develop metrics and indicators for assessing a technology’s readiness across the nine levels of sociotechnical readiness.
In the second project, we are studying how decisions made in the early stages of design impact overall system cost. This work is especially crucial in a nuclear energy context because recent nuclear plant construction projects have been severely impacted by cost overruns as well as project delays. It is vitally important to diagnose the causes of these cost overruns and address them in the early stages of the design if nuclear reactors are to play a significant role in our low-carbon energy systems.

How did you end up where you are today?

I was drawn to the field of nuclear engineering because of the enormous potential nuclear technologies have for solving one of the grandest challenges of our time: access to reliable, clean, and abundant energy.

What is the most significant scientific contribution you would like to make?

I hope to develop a deeper and more fundamental understanding of the design processes of complex systems that leads to more creative, robust, societally, and environmentally beneficial design outcomes.

What makes you excited about your data science and AI research?

There is an enormous opportunity to use data science and AI to study the early stages of the design processes of complex systems and develop new tools to stimulate a broader exploration of the design space and to ensure better design outcomes.

What are 1-3 interesting facts about yourself?

I love learning new languages. Depending on how you count, I speak (with varying levels of ability) six (or seven).


Research Highlights

Runzi Wang

Runzi Wang

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Runzi Wang is a transdisciplinary researcher who studies change in natural and urban environments across space and over time, with the objective of driving positive change with ecological planning and design strategies. Combining technologies such as big data, machine learning, remote sensing, and spatial statistics, her primary research explores how land cover change and urban development pattern influence stream water quality and stormwater quality at the watershed basis, together with various environmental, climatic, and sociocultural factors. By enhancing the interpretability of machine learning in its application to landscape architecture, the most innovative part of her research is to uncover the nonlinear, interacted relationships between environmental, technological, and sociocultural dimensions of landscape systems.

What are some of your most interesting projects?

  1. I conducted the first continental-scale urban stream water quality study funded by MIDAS. We applied geospatial analysis to investigate the characteristics of the built environment (e.g., building footprint, street length, land use spatial pattern) associated with urban stream water quality, the social inequities regarding exposure to stream water contamination, as well as the spatial variations in the above processes. We developed data integration protocols for data from remote sensing products, in-situ observations, and the US Census Bureau. Using Bayesian hierarchical models, we concluded that watersheds with a higher percentage of minorities are associated with higher nutrient pollution, with the relationship being more significant in the American Northwest.
  2. I investigated how land use planning and best management practices mitigated climate change effects on Lake Erie’s water quality. With the integration of longitudinal watershed land cover, agricultural, and climatic data from 1985-2017, we found that no-tillage and reduced tillage management were effective mitigation strategies that could decrease water quality sensitivity to climate change. We plan to advance this work by fusing remote sensing-based bloom detection and process-based simulation to investigate how climate change, land cover change, and anthropogenic activities will impact the eutrophication of Lake Erie.

How did you end up where you are today?

I have a highly interdisciplinary background, receiving training in architecture, landscape architecture, urban planning, statistics, hydrology and water quality, and broader social science topics. This forms my research topic to study the relationships between people, land, and water. Specifically, I study the interconnectedness between people living in the watershed, the land use and urban form of the watershed’s built form, the resulting water quality conditions, and the ecosystem services urban streams provide for people. This background also leverages many different methodologies in my work, including data science, hydrological models, social science methods, and so on. In addition, the most important thing about my research journey is that I have a few excellent friends/researchers who help me a lot on my way and make my research life inspiring and delightful most of the time.

 


Accomplishments and Awards

Sarah Mills

Sarah Mills

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Sarah Mills’ research looks at how renewable energy development impacts rural communities (positively and negatively), the disparate reactions of rural landowners to wind and solar projects, and how state and local policies facilitate or hinder renewable energy deployment. Through a grant from the Department of Environment, Great Lakes and Energy, she also helps communities in incorporating clean energy in their planning and zoning. With respect to data science, in addition to conducting social science surveys, Sarah has also developed a unique database–energyzoning.org–which includes local government zoning regulations from six states across the Midwest

 


Research Highlights

Ken Resnicow

Ken Resnicow

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Dr. Resnicow studies tailored behavior change interventions, natural language processing of clinical encounters, analyses of large datasets related to obesity and other chronic diseases, novel designs such as SMART, MOST, and reinforcement learning, and applied complex systems and chaos theory in behavior change.

 


Accomplishments and Awards

Talia Moore

Talia Moore

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My research involves discovery, modeling, and design for comparative biomechanics and bio-inspired robotics. I develop new information-based methods to analyze the behavior and locomotion of animals in natural environments and use laboratory-based experiments to build detailed models of how animal motion is generated by muscles, tendons, and bones. I’ve developed machine learning methods to automatically segment non-standard animals in photographs for taxonomically-broad phylogenetic comparative analyses of color pattern and behavior. I also use Finite Element Analysis to understand how the evolution of 3-dimensional shape in animal bones and teeth have adapted to a variety of ecological situations, such as novel substrates or diet.

Because FEA and other biomechanical methods are so computationally intensive, I’ve adopted a surrogate model approach that uses Bayesian Optimization to perform stimulus selection and make accurate predictions of sample performance with the minimum number of model datapoints. I’ve also begun applying this surrogate model approach to explore the design space of bio-inspired grippers made from dielectric fluid actuators. Data-driven modeling also informs the design rigid legged robots in my lab, which we will use to test hypotheses regarding how limb shape affects overall locomotion.

Finite element modeling of snake fangs helps us understand how fang shape can be adapted for different types of loading conditions, and therefore prey types. We use Bayesian Optimization to select which species to analyze for our surrogate model, which minimizes the computation time while maximizing prediction accuracy.

Additional Information

What are some of your most interesting projects?

Jerboas are bipedal desert rodents that hop erratically with zig-zag trajectories when they are escaping from predators (some describe them as “ricochetal”). These animals are about the size of your fist, but they can jump over 3 feet straight up in the air or forwards. Little was known about their locomotion, and the majority of biomechanical locomotion research was performed in lab environments on treadmills. However, training an animal to run on a treadmill reduces the variability in direction and speed that the animals would need to survive in the wild. So I went out into the desert and filmed these animals jumping and running on their natural substrates.

To understand how these trajectories might confound predators, I measured the unpredictability (or entropy) and found that bipedal desert rodents are much less predictable than quadrupedal desert rodents. Then, taking a closer look using high-speed video cameras, I determined that they have at least 5 distinct bipedal gaits. Using both kinematic and dynamic data, I built a modified Spring-Loaded Inverted Pendulum model with a torsional spring to control the neutral leg swing angle. I then performed a numerical search using a continuing approach to test neighboring parameter values for viability to discover bifurcations in gait structure. I found that by decoupling the neutral leg swing angle between left and right legs, the model was capable of transitioning between gaits across the entire speed range, just as the real jerboas do. This research will be used to inform the design of controllers for legged robots to switch gaits smoothly across a wide range of speeds.

How did you end up where you are today?

I started off not knowing at all what I wanted to do, but enjoying martial arts. A friend told me to check out the biomechanics class in my last year in undergrad at UC Berkeley, and I was hooked! I joined the lab immediately and worked there for two years after graduating. I wanted to learn everything, so I worked on a different project with a different graduate student every day of the week. During that time, I worked with cockroaches, geckos, iguanas, and agama lizards, learning about how they generate and control their motions. I was also lucky to get hands-on experience with designing and building bio-inspired robots and using them to test biological hypotheses to reveal fundamental principles of animal locomotion.

After that, I went on to study biomechanics and evolutionary biology at Harvard, where I was introduced to jerboas for the first time. They are such strange and wonderful creatures that I knew I wanted to study them for the rest of my life. I came to UM in 2015 and worked as a postdoc in Ecology and Evolutionary Biology, where I developed a new modular ethogram system to analyze snake anti-predator behaviors and design snake-mimicking soft robots. Then I became a Research Scientist in the Robotics Institute, followed by being hired as an Assistant Professor in Mechanical Engineering in January of 2021. Now I am appointed in both Robotics and Mechanical Engineering and have affiliations with Ecology and Evolutionary Biology and the Museum of Zoology.

What is the most significant scientific contribution you would like to make?

I really want to build tools to bridge the gap between biology and engineering. So many biological questions are constrained by the technology we have available. By forming connections between these fields, I have already facilitated more quantitative study of non-steady-state locomotion in natural environments. There is also a big gap between what motions animals and robots are capable of performing. I hope to learn strategies from animals and design robots to succeed in unstructured and complex environments.

What makes you excited about your data science and AI research?

I’m extremely excited that data science is making it possible to analyze the types of large datasets that we can collect from animals. There is no limit to the amount of data you can collect about animal locomotion, behavior, appearance, or structure, and the types of studies that used to take decades can now be done in a semester thanks to data science and AI. This makes it possible to integrate information from multiple different data streams and understand more complex relationships between animals, their environments, and how these relationships change through space and time.

What are 1-3 interesting facts about yourself?

I’ve done fieldwork in Malaysia, China, Australia, California, the Bahamas and Peru. I think it’s incredibly important to examine animals in their natural habitats, because our assumptions about their behaviors might be totally wrong if we only see them in zoos or labs.
The first sentence I try to learn in every language is “Where is the bathroom?”
My two rules for fieldwork are: 1) Never stop moving and 2) Never sit down or lean on anything.

Dr. Briana Mezuk

Briana Mezuk

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Dr. Mezuk is the Director of the Center for Social Epidemiology and Population Health and is an Associate Chair in the Department of Epidemiology at the University of Michigan School of Public Health. She is a psychiatric epidemiologist whose research focuses on understanding the intersections of mental and physical health. Much of her work has examined the consequences of depression for medical morbidity and functioning in mid- and late-life, with particular attention to metabolic diseases such as diabetes and frailty. She is also the Director of the Michigan Integrative Well-Being and Inequalities (MIWI) Training Program, a NIH-funded methods training program that supports innovative, interdisciplinary research on the interrelationships between mental and physical health as they relate to health disparities. She is using data science tools to analyze textual data from the National Violent Death Reporting System, with the goal of better understanding how major life transitions relate to suicide risk over the lifespan. She is committed to translating research into practice, and she writes a blog for Psychology Today called “Ask an Epidemiologist.”