Contact:
Department of Biostatistics
University of Michigan
1415 Washington Heights
Ann Arbor, MI 48109
Office: 4602 SPH I
Email: irinagn [at] umich.edu
I develop statistical methods to analyse modern high-dimensional biomedical data. My methodological interests are primarily in data integration, machine learning and high-dimensional statistics, motivated by challenges arising in analyses of multi-omics data (e.g. RNASeq, metabolomics, micribiome) and data from wearable devices (continuous glucose monitors, ambulatory blood pressure monitors, activity trackers). I am convinced that challenging applied problems give rise to better statistical methodology, and that better statistical methodology in turn aids scientific discovery. I believe that collaboration plays a key role in achieving this goal and I enjoy working with both domain scientists and methodological researchers. My research has been supported by the National Science Foundation, the National Institutes of Health, and recognized with a David P. Byar Young Investigator Award from the Biometrics section of the American Statistical Association and NSF CAREER Award. If you would like to join the research group, click here to explore available opportunities.
I deeply care about the training of next generation, and put large emphasis on reproducible research practices and computational skills in my teaching. I embrace integration of my research and education missions, and employ a team-based approach to research with active student engagement. My efforts in mentoring undergraduate students have been recognized with Dr. Judith Edmiston Mentoring Award from the Texas A&M College of Science.
Recent news:
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October 2024: A new manuscript with Renat Sergazinov and Armeen Taeb on “A spectral method for multi-view subspace learning using the product of projections” is now available on arXiv
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July 2024: Irina became an elected member of the International Statistical Institute
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June 2024: A new manuscript with Zihang Wang, Aleksandr Aravkin and Benjamin Risk on “Sparse Independent Component Analysis with an Application to Cortical Surface fMRI Data in Autism” has been accepted to Journal of the American Statistical Association. Link to R package.
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June 2024: A new manuscript with Elizabeth Chun and Nathaniel J. Fernandes on “An Update on the iglu Software for Interpreting Continuous Glucose Monitoring Data” has been accepted to Diabetes Technology and Therapeutics. The paper describes updated software for analysis of continuous glucose monitoring (CGM) data which is available on GitHub and CRAN.
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June 2024 A new manuscript with Carson James, Dongbang Yuan and Jesus Arroyo on “Learning Joint and Individual Structure in Network Data with Covariates” is now available on arXiv
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June 2024 NIH R01 grant on “New machine learning methods for extracting features from digital health data with applications to sleep apnea”
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June 2024 Invited comments on data integration via analysis of subspaces co-authored by Renat Sergazinov
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April 2024 A manuscript led by Elizabeth Chun on “Pre- Versus Postmeal Sedentary Duration; Impact on Postprandial Glucose in Older Adults with Overweight or Obesity” has appeared in Journal for the Measurement of Physical Behaviour.
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March 2024 A manuscript with So-Min Cheong on “Sensing the impact of extreme heat on physical activity and sleep” has appeared in Digital Health.
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March 2024: A new manuscript with Alex Coulter, Nisha Aurora and Naresh Punjabi on “Fast variable selection for distributional regression with application to continuous glucose monitoring data” is now available on arXiv
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January 2024: A manuscript led by Renat Sergazinov with an amazing team of students (Elizabeth Chun, Valeriya Rogovchenko, Nathaniel Fernandes and Nicholas Kasman) on “GlucoBench: Curated List of Continuous Glucose Monitoring Datasets with Prediction Benchmarks” has been accepted to ICLR (International Conference on Learning Representations).Python repository with public datasets and benchmarks, as well as scripts to reproduce the results.