Irina Gaynanova

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, 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.

Formal short Bio

Recent news:

• August 2023: Irina Gaynanova joins Department of Biostatistics at University of Michigan

• June 2023: A manuscript with Sangyoon Yi and Raymond Wong on “Hierarchical nuclear norm penalization for multi‐view data integration” has been accepted to Biometrics.

• May 2023: A manuscript with Renat Sergazinov, Andrew Leroux, Erjia Cui, Ciprian Crainiceanu, R. Nisha Aurora and Naresh M. Punjabi on “A case study of glucose levels during sleep using fast function on scalar regression inference” has been accepted to Biometrics.

• May 2023: A manuscript with Renat Sergazinov and Mohammadreza Armandpour on “Gluformer: Transformer-Based Personalized Glucose Forecasting with Uncertainty Quantification” has been accepted to ICASSP. Python code to reproduce the results.

• September 2022: Our paper with John Schwenck and Naresh M. Punjabi describing R package bp for analyses of blood pressure data, including data from Ambulatory Blood Pressure Monitors (ABPM), is published in PLoS One.

• August 2022: A new manuscript with Hee Cheol Chung and Yang Ni on “Sparse semiparametric discriminant analysis for high-dimensional zero-inflated data” is now available on arXiv.

• August 2022: A new manuscript with Dongbang Yuan, Yunfeng Zhang, Shuai Guo and Wenyi Wang on “Exponential canonical correlation analysis with orthogonal variation” is now available on arXiv. R code to reproduce the results.

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