Genevera Allen
Home
Research
Software
Students
Teaching
News


Go to: Selected Publications (by area), CV, Selected Talks.

Selected Publications:

  • G. I. Allen, ``Statistical Data Integration: Challenges and Opportunities'', Statistical Modeling, 17:4-5, 1-6, 2017.

  • J. Nagorski and G. I. Allen, ``Genomic Region Detection via Spatial Convex Clustering'', (Submitted), arXiv:1611.04696, 2016. [pdf]

  • D. I. Inouye, E. Yang, G. I. Allen, P. Ravikumar, "A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution", Wiley Interdisciplinary Reviews: Computational Statistics, 9:3, 2017. [pdf]

  • Y. W. Wan, G. I. Allen, and Z. Liu, "TCGA2STAT: Simple TCGA Data Access for Integrated Statistical Analysis in R", Bioinformatics 32:6, 952-954, 2016. [pdf] [R Package] [Vignette]

  • Y. W. Wan, G. I. Allen, Y. Baker, E. Yang, P. Ravikumar, and Z. Liu, "XMRF: An R package to Fit Markov Networks to High-Throughput Genetics Data", BMC Systems Biology, 10(S3):69, 2016. [link] [R Package] [Vignette]

  • M. Narayan and G. I. Allen, " Mixed Effects Models for Resampled Network Statistics Improves Statistical Power to Find Differences in Multi-Subject Functional Connectivity", Frontiers in Neuroscience, 10:108, 2016. [pdf] [supplement]
    • Winner of a 2016 ENAR Distinguished Student Paper Award.

  • S. Tomson, M. Schreiner, M. Narayan, T. Rosser, N. Enrique, A. J. Silva, G. I. Allen, S. Y. Bookheimer, and C. Bearden, "Resting state functional MRI reveals abnormal network connectivity in Neurofibromatosis 1", Human Brain Mapping, 36:11, 4566-4581, 2015. [pdf]

  • G. I. Allen, F. Campbell, and Y. Hu, "Comments on visualizing statistical models: Visualizing modern statistical methods for Big Data", Statistical Analysis and Data Mining, 8:4, 226-228, 2015. [pdf]

  • F. Campbell, G. I. Allen, "Within Group Variable Selection through the Exclusive Lasso", (To Appear), Electronic Journal of Statistics, 2017. [pdf]

  • M. Narayan, G. I. Allen, "Population Inference for Node Level Differences in Functional Connectivity", In IEEE International Workshop on Pattern Recognition in Neuroimaging, 2015. [pdf]
    • Oral presentation.

  • Y. Hu, E. C. Chi, G. I. Allen, "ADMM Algorithmic Regularization Paths for Sparse Statistical Machine Learning", In Splitting Methods in Communication and Imaging, Science and Engineering, R. Glowinski, W. Yin, and S. Osher (eds), Springer, 433-459, 2016. [pdf]

  • M. Narayan, G. I. Allen, S. Tomson, "Two Sample Inference for Populations of Graphical Models with Applications to Functional Connectivity", (Submitted) arXiv:1502.03853, 2015. [pdf]

  • E. Yang, P. Ravikumar, G. I. Allen, Y. Baker, Y. W. Wan, Z. Liu, "A General Framework for Mixed Graphical Models", (Submitted) arXiv:1411.0288, 2014. [pdf]

  • E. C. Chi, G. I. Allen, and R. Baraniuk, "Convex Biclustering", Biometrics, 73:1, 10-19, 2017. [pdf]

  • Y. Hu and G. I. Allen, "Local-Aggregate Modeling for Big-Data via Distributed Optimization: Applications to Neuroimaging", Biometrics, 71:4, 905-917, 2015. [pdf] [supplement]
    • Winner of a 2015 ENAR Distinguished Student Paper Award.

  • E. Yang, Y. Baker, P. Ravikumar, G. I. Allen, Z. Liu, "Mixed Graphical Models via Exponential Families", In International Conference on Artificial Intelligence and Statistics (AISTATS), JMLR W & CP, 33:1042-1050, 2014. [pdf]
    • Oral presentation.

  • Y. W. Wan, C. M. Mach, G. I. Allen, M. L. Anderson, and Z. Liu, "On the Reproducibility of TCGA Ovarian Cancer MicroRNA Profiles", PLoS One, 9:1, e87782, 2014. [link]
    • Highlighted by Nature Methods: ``miRNA profiling depends on platform'', 11, 369, March 2014. [link]

  • Y. W. Wan, J. Nagorski, G. I. Allen, Z. Li, Z. Liu, "Identifying Cancer Biomarkers through a network regularized cox model", In IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS), 2013. [pdf]
    • Oral presentation.

  • G. I. Allen, "Multi-way Functional Principal Components Analysis", In IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2013. [pdf]

  • G. I. Allen, "Sparse and Functional Principal Components Analysis", arXiv:1309.2895, 2013. [pdf]

  • E. Yang, P. Ravikumar, G. I. Allen, and Z. Liu, "On Poisson Graphical Models", In Advances in Neural Information Processing Systems (NIPS), 2013. [pdf]

  • E. Yang, P. Ravikumar, G. I. Allen, and Z. Liu, "Conditional Random Fields via Univariate Exponential Families", In Advances in Neural Information Processing Systems (NIPS), 2013. [pdf]

  • S. Tomson, M. Narayan, G. I. Allen and D. Eagleman, "Neural networks of colored sequence synesthesia", Journal of Neuroscience, 33:35, 14098-14106, 2013. [pdf]
    • Congressman McNerney highlights research in speech on the House floor, September 20, 2013.

  • W. Zhang, W.Y. Wan, G. I. Allen, K. Pang, M. L. Anderson, and Z. Liu, "Molecular pathway identification using biological network-regularized logistic models", BMC Genomics, 14:8, S7, 2013. [pdf]

  • M. Narayan and G. I. Allen, "Randomized Approach to Differential Inference in Multi-Subject Functional Connectivity", In IEEE International Workshop on Pattern Recognition in Neuroimaging, 2013. [pdf]
    • Oral presentation & Winner of the PRNI Student Travel Award.

  • Y. Hu and G. I. Allen, "Local-Aggregate Modeling for Multi-Subject Neuroimage Data via Distributed Optimization", In IEEE International Workshop on Pattern Recognition in Neuroimaging, 2013. [pdf]

  • G. I. Allen and Zhandong Liu, "A Local Poisson Graphical Model for Inferring Genetic Networks from Next Generation Sequencing Data", IEEE Transactions on NanoBioscience, 12:3, 1-10, 2013. [pdf]

  • E. C. Chi, G. I. Allen, H. Zhou, O. Kohannim, K. Lange, and P. M. Thompson, "Imaging genetics via sparse canonical correlation analysis", In IEEE International Symposium on Biomedical Imaging, 2013. [pdf]
    • Oral presentation.

  • E. Yang, P. Ravikumar, G. I. Allen, and Z. Liu, "Graphical Models via Univariate Exponential Family Distributions", Journal of Machine Learning Research, 16, 3813-3847, 2015. [pdf]

  • E. Yang, P. Ravikumar, G. I. Allen, and Z. Liu, "Graphical Models via Generalized Linear Models", In Advances in Neural Information Processing Systems (NIPS), 2012. [pdf]
    • Oral presentation.

  • G. I. Allen and Zhandong Liu, "A Log-Linear Graphical Model for Inferring Genetic Networks from High-Throughput Sequencing Data", In IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2012. [pdf]
    • Oral presentation.

  • G. I. Allen, C. Peterson, M. Vannucci, and M. Maletic-Savatic, "Regularized Partial Least Squares with an Application to NMR Spectroscopy", Statistical Analysis and Data Mining, 6:4, 302-314, 2013. [pdf] [software]

  • G. I. Allen, "Regularized Tensor Factorizations and Higher-Order Principal Components Analysis", Rice University Technical Report No. TR2012-01, arXiv:1202.2476, 2012. [pdf]
    • IBS Young Statistician Showcase

  • G. I. Allen, "Sparse Higher-Order Principal Components Analysis", In Proceedings of the 15th International Conference on Artificial Intelligence and Statistics, 2012. [pdf]

  • G. I. Allen and M. Maletic-Savatic, "Sparse Non-negative Generalized PCA with Applications to Metabolomics", Bioinformatics, 27:21, 3029-3035, 2011. [pdf] [supplement] [software]

  • G. I. Allen and P. O. Perry, `"Singular value decomposition for high-dimensional data", Encyclopedia of Environmetrics Second Edition, A. H. El-Shaarawi and W. Piegorsch (eds), John Wiley & Sons Ltd, Chichester, UK, 2469-2472, 2012.

  • G. I. Allen, "Comment on Article by Hoff", Bayesian Analysis, 6:2, 197-202, 2011. [pdf]

  • G. I. Allen, Logan Grosenick, and Jonathan Taylor, "A Generalized Least Squares Matrix Decomposition", Journal of the American Statistical Association, Theory & Methods, 109:505, 145-159, 2014. [pdf] [supplement] [software]

  • G. I. Allen and R. Tibshirani, "Inference with Transposable Data: Modeling the Effects of Row and Column Correlations", Journal of the Royal Statistical Society, Series B (Theory & Methods), 74:4, 1-23, 2012. [pdf] [supplement] [software]

  • G. I. Allen, "Automatic feature selection via weighted kernels and regularization", Journal of Computational and Graphical Statistics, 22:2, 284-299, 2013. [pdf] [supplement] [software]

  • G. I. Allen and R. Tibshirani, "Transposable regularized covariance models with an application to missing data imputation", Annals of Applied Statistics, 4:2, 764-790, 2010. [pdf] [supplement] [software]

Research supported by National Science Foundation DMS-1554821, DMS-1209017, DMS-1317602, DMS-1264058; Collaborative Advances in Biomedical Computing, Ken Kennedy Institute for Information Technology at Rice University supported by the John and Ann Doerr Fund for Computational Biomedicine; Center for Computational and Integrative Biomedical Research Seed Funding Program, Baylor College of Medicine.


Curriculum Vitae: [pdf]
Updated 06/2017.


Selected Talks:

  • "Population Inference for Functional Brain Connectivity", Spring 2016. [pdf]

  • "Mixed Graphical Models with Applications to Integrative Genomics", Spring 2016. [pdf]

  • "Within Group Variable Selection through the Exclusive Lasso ", Fall 2015. [pdf]

  • "Convex Biclustering", Fall 2015. [pdf]

  • "Statistical Data Integration: Using Networks to Integrate a Variety of Big Biomedical Data", (Big Data in Biomedicine Conference) May 2015. [pdf]

  • "Functional Connectivity: Estimation and Inference with Markov Networks", (UCLA Advanced Neuroimaging Summer Program), July 2014. [pdf]

  • "Markov Networks: Methods and Applications for Genomics and Neuroimaging", (Keck Seminar), March 2014. [pdf]

  • "Sparse and Functional Principal Components Analysis", April 2014. [pdf]

  • "Regularized Tensor Decompositions and Higher-Order PCA", August 2012. [pdf]

  • "Generalizing Principal Components Analysis", October 2011. [pdf]

    • "Sparse Generalized Principal Components Analysis with Applications to Neuroimaging", March 2013. [pdf]

  • "Inference with Transposable Data: Modeling the Effects of Row and Column Correlations", Joint Statistical Meetings, August 2010. [pdf]

  • "Transposable Regularized Covariance Models with Applications to High-Dimensional Data", May 2010. [pdf]

PhD Thesis:

  • "Transposable Regularized Covariance Models with Applications to High-Dimensional Data", June 2010. [pdf]