My research focuses on developing and using methods in machine learning and natural language processing to learn about society from text, promoting better and more reproducible data science, and studying the societal impacts of these technologies. I collaborate with colleagues in statistics, linguistics, political science, and other areas of computational social science to investigate how people communicate, the effects of this communication, and to better understand the potential consequences and limitations of data science and artificial intelligence.
Our research is focused on Post ICU pain syndromes (PIPS). PIPS exhibit distinct phenotypic presentations and can be predicted by intra-ICU parameters. Our primary goal is to be able to predict post-ICU opioid use based on intra-ICU parameters. We utilize a data-driven characterization of post-ICU pain syndromes will utilize unsupervised clustering algorithms including DBSCAN and spectral clustering. Prediction of post-discharge pain severity, likelihood of specific pain presentations, and post-discharge opioid use will be achieved using logistic LASSO, random forests, and neural networks. Specifically, these tests will utilize available ICU data to predict changes between pre-
and post-ICU pain severity, incidence of specific pain presentations, and incidence of opioid use.
Dr. Hadjiyski research interests include computer-aided diagnosis, artificial intelligence (AI), machine learning, predictive models, image processing and analysis, medical imaging, and control systems. His current research involves design of decision support systems for detection and diagnosis of cancer in different organs and quantitative analysis of integrated multimodality radiomics, histopathology and molecular biomarkers for treatment response monitoring using AI and machine learning techniques. He also studies the effect of the decision support systems on the physicians’ clinical performance.
Broadly, I study legal decision making, including decisions related to crime and employment. I typically use large social science data bases, but also collect my own data using technology or surveys.
Lu’s research is focused on natural language processing, computational social science, and machine learning. More specifically, Lu works on algorithms for text summarization, language generation, argument mining, information extraction, and discourse analysis, as well as novel applications that apply such techniques to understand media bias and polarization and other interdisciplinary subjects.
Edgar Franco-Vivanco is an Assistant Professor of Political Science and a faculty associate at the Center for Political Studies. His research interests include Latin American politics, historical political economy, criminal violence, and indigenous politics.
Prof. Franco-Vivanco is interested in implementing machine learning tools to improve the analysis of historical data, in particular handwritten documents. He is also working in the application of text analysis to study indigenous languages. In a parallel research agenda, he explores how marginalized communities interact with criminal organizations and abusive policing in Latin America. As part of this research, he is using NLP tools to identify different types of criminal behavior.
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.
Dr. Fernandez is a clinical psychologist with extensive training in both addiction and behavioral medicine. She is the Clinical Program Director at the University of Michigan Addiction Treatment Service. Her research focuses on the intersection of addiction and health across two main themes: 1) Expanding access to substance use disorder treatment and prevention services particularly in healthcare settings and; 2) applying precision health approaches to addiction-related healthcare questions. Her current grant-funded research includes an NIH-funded randomized controlled pilot trial of a preoperative alcohol intervention, an NIH-funded precision health study to leverage electronic health records to identify high-risk alcohol use at the time of surgery using natural language processing and other machine-learning based approaches, a University of Michigan funded precision health award to understand and prevent new persistent opioid use after surgery using prediction modeling, and a federally-funded evaluation of the state of Michigan’s substance use disorder treatment expansion.
I am a Research Fellow in the Inter-university Consortium for Political and Social Research (ICPSR) at the University of Michigan. My research is currently supported by a NSF project, Developing Evidence-based Data Sharing and Archiving Policies, where I am analyzing curation activities, automatically detecting data citations, and contributing to metrics for tracking the impact of data reuse. I hold a Ph.D. in Geography from UC Santa Barbara and I have expertise in GIScience, spatial information science, and urban planning. My interests also include the Semantic Web, innovative GIS education, and the science of science. I have experience deploying geospatial applications, designing linked data models, and developing visualizations to support data discovery.
My methodological research focus on developing statistical methods for routinely collected healthcare databases such as electronic health records (EHR) and claims data. I aim to tackle the unique challenges that arise from the secondary use of real-world data for research purposes. Specifically, I develop novel causal inference methods and semiparametric efficiency theory that harness the full potential of EHR data to address comparative effectiveness and safety questions. I develop scalable and automated pipelines for curation and harmonization of EHR data across healthcare systems and coding systems.