• 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.

  • June 2022: A new manuscript with Sangyoon Yi and Raymond Wong on “Hierarchical nuclear norm penalization for multi-view data” is now available on arXiv. R code to reproduce the results.

  • May 2022: A new 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” is now available on arXiv.

  • May 2022: A new manuscript with John Schwenck and Naresh Punjabi describing R package bp for analyses of blood pressure data, including data from Ambulatory Blood Pressure Monitors (ABPM), is available on medRxiv

  • May 2022: Irina was invited to write an article for ASA Biopharmaceutical report issue on “Digital Health”, access the issue here to read on Digital biomarkers of glucose control - reproducibility challenges and opportunities.

  • April 2022: Our paper with Dongbang Yuan on Double-matched matrix decomposition for multi-view data has been accepted to Journal of Computational and Graphical Statistics

  • April 2022: Our paper with Nisha R. Aurora, Pratik Patel and Naresh Punjabi on Glucose profiles in obstructive sleep apnea and type 2 diabetes mellitus has been accepted to Sleep Medicine

  • December 2021: Our paper with Nathaniel Fernandes, Nhan Nguyen, Elizabeth Chun and Naresh Punjabi on Open-Source Algorithm to Calculate Mean Amplitude of Glycemic Excursions Using Short and Long Moving Averages has been accepted to Journal of Diabetes Science and Technology

  • November 2021: Our paper with Alex Lapanowski on *Compressing large sample data for discriminant analysis” has been accepted for 2021 IEEE International Conference on Big Data

  • October 2021: Irina is honored with Dr. Judith Edmiston Mentoring Award from TAMU College of Science. Press release for all College of Science 2021 Award Winners

  • September 2021: Our paper with Mingze Huang and Christian L Müller on “latentcor: an R package for estimating latent correlations from mixed data types” has been accepted to Journal of Open Source Software

  • August 2021: Our paper with Yunfeng Zhang on “Joint association and classification analysis of multi-view data” has been accepted to Biometrics

  • June 2021: Irina has been awarded tenure and promotion to Associate Professor at Texas A&M effective September 1, 2021

  • May 2021: A new manuscript with Hee Cheol Chung and Yang Ni on “Phylogenetically informed Bayesian truncated copula graphical models for microbial association networks” is now available on arXiv. R code to reproduce the results.

  • May 2021: A new manuscript with Dongbang Yuan on “Double-matched matrix decomposition for multi-view data” is now available on arXiv. R code to reproduce the results.

  • May 2021: Applications open for Summer 2021 Structured Research Experience (SRE) in Statistics. All TAMU BS Statistics majors are welcome to apply.

  • April 2021: Irina is a guest on Data & Science podcast with Glen Wright Colopy talking about Replicability and Reproducibility in Scince. YouTube video and PodBean

  • April 2021: A manuscript with Ben Risk on “Simultaneous Non-Gaussian Component Analysis (SING) for Data Integration in Neuroimaging” has ben accepted to Annals of Applied Statistics.

  • April 2021: Our paper on “Interpreting blood glucose data with R package iglu.” has been accepted to PLoS one. You can download iglu from CRAN or install from Github for most recent developments. All the functionality is also available directly as the shiny app.

  • February 2021: A manuscript with with Grace Yoon and Christian L Müller on “Fast computation of latent correlations” has appeared in JCGS. Check out [R code to reproduce simulations] and the improved [R package] to estimate latent correlations for mixed (continuous, binary and zero-inflated) variable types.

  • February 2021: Irina receives a CAREER Award from the National Science Foundation (NSF). TAMU College of Science press release.

  • January 2021: Irina is a guest on Pod of Asclepius podcast talking about her team’s work on Continuous Glucose Monitors. YouTube video and PodBean.

  • December 2020: Irina took part in a virtual NISS career panel, the panel summary is here.

  • June 2020: Our team has released a list of public Continuous Glucose Monitoring (CGM) datasets. Thank you to an amazing team of undergraduate researchers: Mary Martin, Elizabeth Chun, David Buchanan, Eric Wang and Sangaman Senthil who assembled this collection as part of their Aggie Research Project.

  • September 2020: Our team has released version 2 of an R package iglu for analysis of Continuous Glucose Monitoring (CGM) data. The package is also available from CRAN (iglu) and you can learn more about it from the accompanying website. All the functionality is also available directly as the shiny app. The introductory paper/extended vignette is coming soon!

  • September 2020: A new manuscript on “Interpreting blood glucose data with R package iglu.” is now available on bioRxiv.
  • June 2020: Congratulations to Alex Lapanowski and Yunfeng Zhang for succesfully defending their PhD dissertations!

  • May 2020: A new manuscript with Naresh Punjabi and Ciprian Crainiceanu on “Modeling continuous glucose monitoring (CGM) data during sleep.” has appeared in Biostatistics

  • May 2020: A new manuscript with Alex Lapanowski on “Compressing Large Sample Data for Discriminant Analysis” is now available on arXiv

  • May 2020: A new manuscript with Ben Risk on “Simultaneous Non-Gaussian Component Analysis (SING) for Data Integration in Neuroimaging” is now available on arXiv

  • April 2020: A manuscript with Grace Yoon and Raymond Carroll on “Sparse semiparametric canonical correlation analysis for data of mixed types” has appeared in Biometrika

  • September 2019: Cornell Department of Statistics and Data Science features a conversation with Irina about her time at Cornell and advice for pursuing career in data science.

  • June 2019: A manuscript with Gen Li, “Structural Learning and Integrative Decomposition of Multi-View Data”, has been accepted to Biometrics.

  • June 2019: A manuscript with Grace Yoon and Christian L Müller, Microbial networks in SPRING - Semi-parametric rank-based correlation and partial correlation estimation for quantitative microbiome data has been accepted to Frontiers in Genetics for the special Research Topic on “Statistical and Computational Methods for Microbiome Multi-Omics Data”

  • April 2019: A manuscript on Prediction and estimation consistency of sparse multi-class penalized optimal scoring has been accepted to Bernoulli

  • April 2019: A manuscript with Alex Lapanowski on Sparse Feature Selection in Kernel Discriminant Analysis via Optimal Scoring has appeared in AISTATS

  • November 2018: A new manuscript on “Joint association and classification analysis of multi-view data” is now available on arXiv

  • September 2018: The Texas A&M College of Science promotional video featuring Irina

  • July 2018: A new manuscript on “Sparse semiparametric canonical correlation analysis for data of mixed types” is now available on arXiv
  • March 2018: Irina is the recipient of the 2018 David P. Byar Young Investigator Award for the first-place paper “Structural Learning and Integrative Decomposition of Multi-View Data”, joint work with Gen Li, Columbia University Mailman School of Public Health. Texas A&M Science article featuring the award
  • January 2018: A new manuscript on “Prediction Error Bounds for Linear Regression With the TREX” is now available on arXiv
  • November 2017: A new manuscript on “Sparse quadratic classification rules via linear dimension reduction” is now available on arXiv
  • May 2017: NSF DMS-1712943 grant, Scalable Methods for Classification of Heterogeneous High-Dimensional Data