Elle O’Brien

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My research focuses on building infrastructure for public health and health science research organizations to take advantage of cloud computing, strong software engineering practices, and MLOps (machine learning operations). By equipping biomedical research groups with tools that facilitate automation, better documentation, and portable code, we can improve the reproducibility and rigor of science while scaling up the kind of data collection and analysis possible.

Research topics include:
1. Open source software and cloud infrastructure for research,
2. Software development practices and conventions that work for academic units, like labs or research centers, and
3. The organizational factors that encourage best practices in reproducibility, data management, and transparency

The practice of science is a tug of war between competing incentives: the drive to do a lot fast, and the need to generate reproducible work. As data grows in size, code increases in complexity and the number of collaborators and institutions involved goes up, it becomes harder to preserve all the “artifacts” needed to understand and recreate your own work. Technical AND cultural solutions will be needed to keep data-centric research rigorous, shareable, and transparent to the broader scientific community.

View MIDAS Faculty Research Pitch, Fall 2021

 

Michael Rubyan

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My research focuses on the development and evaluation of novel interventions that leverage emerging technologies to train members of the healthcare workforce around adhering to guidelines. I study how to scale custom designed teaching and learning platforms and evaluate their use to motivate effective communication and dissemination of evidence based practice. Other emphases of my work include health policy literacy, translation and communication of health services research, and improving health system literacy in urban communities. I have developed and evaluated numerous web based educational interventions that employ the “flipped classroom” design with an emphasis on understanding the data and analytics that guide successful implementation and promote high fidelity for members of the healthcare workforce. As an implementation scientist, I rely on the integration of data and analytics to understand what motivates successful program implementation.

In addition to the development of these platforms, I have extensive experience developing and evaluating online, hybrid residential, residential courses, and MOOCs related to healthcare management, non-profit management, healthcare finance, and health economics that employ engaging lessons and modules, interactive graphics, and a blended learning format to aid health professions students, and both undergraduate and graduate public health students in understanding the healthcare system. My MOOC entitled “Understanding and Improving the U.S. Health Care System” has been taken by over 5,000 learners and is characterized by the use of “big data” to understand how future healthcare providers learn health policy.

Kevin Stange

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Prof. Stange’s research uses population administrative education and labor market data to understand, evaluate and improve education, employment, and economic policy. Much of the work involves analyzing millions of course-taking and transcript records for college students, whether they be at a single institution, a handful of institutions, or all institutions in several states. This data is used to richly characterize the experiences of college students and relate these experiences to outcomes such as educational attainment, employment, earnings, and career trajectories. Several projects also involve working with the text contained in the universe of all job ads posted online in the US for the past decade. This data is used to characterize the demand for different skills and education credentials in the US labor market. Classification is a task that is arising frequently in this work: How to classify courses into groups based on their title and content? How to identify students with similar educational experiences based on their course-taking patterns? How to classify job ads as being more appropriate for one type of college major or another? This data science work is often paired with traditional causal inference tools of economics, including quasi-experimental methods.

Quan Nguyen

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My research focuses on the application of data science in educational research, so called learning analytics. I have experience analyzing educational data on a large-scale to understand a) how course design influence students’ learning behavior and b) how students form peer networks. My work involves using multiple educational data sources such as log-data in online learning environment, course information, students’ academic records, and location data gathered from campus WiFi networks. I am interested in network analysis, time-series analysis, and machine learning.

Akbar Waljee

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I use machine-learning techniques to implement decision support systems and tools that facilitate more personalized care for disease management and healthcare utilization to ultimately deliver efficient, effective, and equitable therapy for chronic diseases. To test and advance these general principles, I have built operational programs that are guiding—and improving—patient care in costly in low resource settings, including emerging countries.

Andrew Krumm

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My research examines the ways in which individuals and organizations use data to improve. Quality improvement and data-intensive research approaches are central to my work along with forming equitable collaborations between researchers and frontline workers. Prior to joining the Department of Learning Health Sciences, I was the Director of Learning Analytics Research at Digital Promise and a Senior Education Researcher in the Center for Technology in Learning at SRI International. At both organizations, I developed data-intensive research-practice partnerships with educational organizations of all types. As a learning scientist working at the intersection of data-intensive research and quality improvement, my colleagues and I have developed tools and strategies (e.g., cloud-based, open source tools for engaging in collaborative exploratory data analyses) that partnerships between researchers and practitioners can use to measure learning and improve learning environments.

This is an image that my colleagues and I, over multiple projects, developed to communicate the multiple steps involved in collaborative data-intensive improvement. The “organize” and “understand” phases are about asking the right questions before the work of data analysis begins: “co-develop” and “test” are about taking action following an analysis. Along with identifying common phases, we have also observed the importance of the following supporting conditions: a trusting partnership, the use of formal improvement methods, common data workflows, and intentional efforts to support the learning of everyone involved in the project.

Vitaliy Popov

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My research focuses on understanding, designing, and evaluating learning technologies and environments that foster collaborative problem solving, spatial reasoning, engineering design thinking and agency. I am particularly interested in applying multimodal learning analytics in the context of co-located and/or virtually distributed teams in clinical simulations. I strive to utilize evidence in education science, simulation-based training and learning analytics to understand how people become expert health professionals, how they can better work in teams and how we can support these processes to foster health care delivery and health outcomes.

Sol Bermann

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I am interested in the intersection of big data, data science, privacy, security, public policy, and law. At U-M, this includes co-convening the Dissonance Event Series, a multi-disciplinary collaboration of faculty and graduate students that explore the confluence of technology, policy, privacy, security, and law. I frequently guest lecture on these subject across campus, including at the School of Information, Ford School of Public Policy, and the Law School.

Michelle Aebersold

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Dr. Aebersold’s professional and academic career is focused on advancing the science of learning applied in simulation to align clinician and student practice behaviors with research evidence to improve learner and health outcomes.  She focuses her scholarship in both high fidelity and virtual reality simulation and is a national leader and expert in simulation. Her scholarship has culminated in developing the Simulation Model to Improve Learner and Health Outcomes (SMILHO).

Current Research Grants and Programs:

  • Closing the loop: new data tools for measuring change in the quality for nursing education and the value of new approaches to instruction (PI) University of Michigan School of Nursing.
  • Interactive anatomy-augmented virtual simulation training (PI with Voepel-Lewis) Archie MD Award Number 045889

Profile: Building a Better Nurse

By Dan Meisler
Communications Manager, ARC

Michelle Aebersold has spent her career trying to “build a better nurse,” through training development, virtual clinical environments, and patient simulations.

As Director of Simulation and Educational Innovation at the U-M School of Nursing, Aebersold is focused on creating realistic learning environments for nursing students.

But it wasn’t until relatively recently that she realized she had an under-utilized but powerful tool at her disposal — data.

For years, Aebersold has been collecting data on how student performance is affected by participation in various simulations, but it wasn’t until speaking to faculty from the Michigan Institute for Data Science (MIDAS) that she realized the potential insights all of her historical datasets might have hidden inside.

“We’re trying to pull them all together to create a common thread, so that as we collect data on our students, we can follow them to say how they progressed. How many simulations and what type of simulations did they get and how did that impact their learning?” Aebersold said. “We hit students with a lot of simulations while they’re in school, but we don’t necessarily know which ones are better for certain things.”

Now, as MIDAS core faculty member, Aebersold will be able to easily collaborate with other MIDAS researchers to refine and analyze her data.

Eventually, she said, she’d like to be able to track nursing students as they enter their first jobs to see how their training translates to real-world performance. And a further goal for Aebersold is to help the practice of nursing education become similar to the training world-class athletes receive, in which, for example, subjects review videos of their performance to identify what works and what doesn’t.

“We’re going to introduce some things, like what can we do with virtual simulations, or what can we do with eye-tracking,” she said. “We really want to try to dive deeper, by being able to put all this information together using a lot of these data science methods.”

Aebersold has been at U-M since the mid-1980s, serving in a variety of clinical and administrative roles. She earned a Ph.D in Nursing from the University in 2008, and served as Director of the Clinical Learning Center in the School of Nursing for eight years thereafter. Since 2016, she’s been Director of Simulation and Educational Innovation.

One of Aebersold’s recent projects, “Closing the Loop: New Data Tools for measuring Changes in the Quality of Nursing Education,” applies modern data-science tools to help understand the correlation between traditional tests and student skills and competence when they enter the workforce, and whether simulations coupled with debriefing sessions translate into improvements in skills and in test outcomes.

Another potential project will focus on comparing the eye movements of novice nurses to expert nurses. Aebersold said initial findings have shown that nurses with more experience tend to focus their eyes on one thing for longer periods of time than those with less.

She said she’s enlisting help on that work from professors in the School of Information at U-M.

“By really understanding the difference between how experts do things and novices do things, you can help develop simulations that help novices get better,” Aebersold said.

She credits her father with starting her on the path to becoming a nurse; he was a police officer, and would sometimes take her to the local emergency room.

“I loved the fast pace,” she recalled, and the chance to have a real, positive impact on people’s lives.

After earning a nursing degree from Madonna University, she became a critical care nurse. She then gravitated toward supervision and administration, gaining a masters in business administration, also from Madonna University. She earned her Ph.D. from U-M while working as a nurse manager at the U-M health System.

She said all the technology, data, and innovative modes of study she’s brought together are all in service of one goal.

“For me, it all comes down to how can we make the patient care environment safer for our patients,” she said.