Applications:
Behavioral Science, Bioinformatics, Biological Sciences, Complex Systems, Computational Linguistics, Electronic Medical Record Data, Genomics, Healthcare Management and Outcomes, Human Development, Human Subjects Trials and Intervention Studies, Law, Learning Analytics, Learning Health Systems, Medical Imaging, Medical Informatics, Mental Health, Organizational Research, Policy Research, Population Sciences, Precision Medicine, Public Health, Research Reproducibility
Methodologies:
Algorithms, Artificial Intelligence, Bayesian Methods, Causal Inference, Classification, Computational Tools for Data Science, Data Management, Data Mining, Data Quality, Database Systems and Infrastructure, Decision Science, Deep Learning, Dynamical Models, Information Theory, Longitudinal Data Analysis, Machine Learning, Mathematics, Natural Language Processing, Pattern Analysis and Classification, Predictive Modeling, Psychometrics, Signal Processing, Statistical Inference, Statistical Modeling, Statistics, Survey Methodology, Time Series Analysis
Relevant Projects:

MI Lighthouse COVID-19 vaccination tracking, Data Version Control


Elle O’Brien

Lecturer III; Research Investigator

School of Information

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