- Zhao S. and Shojaie A. (2016) "A Significance Test for Graph-Constrained Estimation", Biometrics; available on arXiv
- Ma J., Shojaie A. and Michailidis G., (2016) "Network-Based Pathway Enrichment Analysis with Incomplete Network Information", Bioinformatics; available online
- Shojaie A. and Michailidis G. (2010), "Penalized Principal Component Regression on Graphs for Analysis of Subnetworks", in Advances in Neural Information Processing Systems; pdf file
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Shojaie A. and Michailidis G. (2010)
"Network Enrichment Analysis in Complex Experiments",
Statistical Applications in Genetics and Molecular Biology;
Journal Link pdf file
- An earlier version of this paper received a 2009 ENAR Distinguished Student Paper Award. -
Shojaie A. and Michailidis G. (2009)
"Analysis of Gene Sets Based on The Underlying Regulatory Network",
Journal of Computational Biology (Editor's Pick, March 2009)
Journal Link pdf file
- Implemented in R-package NetGSA. - Shojaie A. (2020) "Differential Network Analysis: A Statistical Perspective", WIRES Computational Statistics; available on arxiv
- Saegusa T. and Shojaie A. (2016), "Joint Estimation of Precision Matrices in Heterogenous Populations", Electronic Journal of Statistics; available on arXiv
- Lin L., Drton M. and Shojaie A. (2016) "Estimation of high-dimensional graphical models using regularized score matching", Electronic Journal of Statistics; available on arXiv
- Tan K.-M., Witten D., and Shojaie A. (2015) "The Cluster Graphical Lasso for Improved Estimation of Gaussian Graphical Models", Computational Statistics and Data Analysis (CSDA); available on arXiv
- Chen S., Witten D. and Shojaie A. (2015) "Selection and Estimation for Mixed Graphical Models", Biometrika; available on arXiv
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Voorman A., Shojaie A. and Witten D. (2014)
"Graph Estimation with Joint Additive Models",
Biometrika; pdf file
- Implemented in R-package spacejam
- Winner of David Byar Young Investigator Travel Award, Biometrics Section, ASA - Tank A., Covert I., Foti N., Shojaie A. and Fox E. (2022) "Neural Granger Causality", IEEE-TPAMI; available on arXiv
- Safikhani A. and Shojaie A. (2022) "Joint Structural Break Detection and Parameter Estimation in High-Dimensional Non-Stationary VAR Models", Journal of the American Statistical Association (JASA); available on arXiv
- Tank A., Fox E. and Shojaie A. (2019) "Identifiability and estimation of structural vector autoregressive models for subsampled and mixed-frequency time series", Biometrika; available on arXiv - Winner of a 2017 Best Student Paper Award from the ASA Business and Economics Section
- Chen S., Witten D. and Shojaie A. (2017) "Nearly Assumptionless Screening for the Mutually-Exciting Multivariate Hawkes Process", Electronic Journal of Statistics (EJS); available online
- Chen S., Shojaie A. and Witten D. (2016) "Network Reconstruction from High-Dimensional Ordinary Differential Equations", Journal of American Statistical Associations (JASA); available on arXiv
- Basu S., Shojaie A. and Michailidis G. (2015) "Network Granger Causality with Inherent Grouping Structure", Journal of Machine Learning Research (JMLR); available on arXiv
- Haris A., Simon N. and Shojaie A.(2022) "Generalized sparse additive models", J Machine Learning Research; available on arXiv
- Haris A., Shojaie A., and Simon N (2019) "Nonparametric regression with adaptive truncation via a convex hierarchical penalty", Biometrika; available on arXiv
- Randolph T., Zhao S., Copeland W., Hullar M. and Shojaie A. (2018) "Kernel Penalized Regression for Analysis of Microbiome Data", Annals of Applied Statistics (AoAS); available on arXiv
- Witten D., Shojaie A. and Zhang F. (2014) "The Cluster Elastic Net for High-Dimensional Regression with Unknown Variable Grouping", Technometrics pdf file
- S. Kucukyavuz*, Shojaie A.*, Manzour H., Wei L., and Wu H.-H. (2023) Consistent Second-Order Conic Integer Programming for Learning Bayesian Networks. Journal of Machine Learning Research (JMLR)
- Sondhi A. and Shojaie A. (2019) "The Reduced PC-Algorithm: Improved Causal Structure Learning in Large Random Networks", Journal of Machine Learning Research (JMLR); available online - Winner of a 2018 Best Student Paper Award from the ASA Biopharmaceutical Section
- Shojaie A.*, Jauhiainen A.*, Kallitsis M.* and Michailidis G. (2014) "Inferring Regulatory Networks by Combining Perturbation Screens and Steady State Gene Expression Profiles", PLoS ONE
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Shojaie A. and Michailidis G. (2010)
"Penalized Likelihood Methods for Estimation of Sparse High Dimensional Directed Acyclic Graphs", Biometrika; Journal Link pdf file
- Implemented in R-package spacejam - Jin K., Wilson K., Beck J., Nelson C., Brownridge G., Harrison B., Djukovic D., Raftery D., Brem R., Yu S., Drton M., Shojaie A., Kapahi P., and Promislow D. (2020) "Genetic and metabolic architecture of variation in diet restoration-mediated lifespan extension in Drosophila"; PLoS Genetics
- Kaushik A.*, Shojaie A.*, Panzitt K.*, ... and Sreekumar A. (2016) "Inhibition of the hexosamine biosynthetic pathway promotes castration-resistant prostate cancer", Nature Communications.
- McCormick T., Lee H., Cesare N., Shojaie A. and Spiro E. (2015) "Using Twitter for Demographic and Social Science Research: Tools for Data Collection", Sociological Methods and Research. pdf file
- Putluri N.*, Shojaie A.*, Vasu V.*, Vareed S., Nalluri S., Putluri V., Tallman C., Butler C., Sana S., Fischer S., Sica G., Brat D., Shi H., Weizer A., Terris M., Shariat S., Michailidis G., and Sreekumar A. (2011) "Metabolomic Profiling Reveals Potential Markers and Mechanism for Bladder Cancer Progression", Cancer Research.
- Putluri N., Shojaie A., Vasu V., Nalluri S., Vareed S., Putluri V., Vivekanandan-Giri A., Creighton C., Byun J, Pennathur S., Sana T., Fischer S., Michailidis G. and Sreekumar A. (2011) "Metabolomic Profiling Reveals A Role for Androgen in Activating Amino Acid Metabolism and Methylation in Prostate Cancer Cells". PLoS One pdf file
I am interested in developing statistical methods for analysis of large, complex systems, particularly biological and social systems.
The main characteristic of these systems is that they are often comprised of a large number of interacting components, and the behavior of the system may not be evident from that of individual components.
Networks offer a convenient framework for analyzing large, complex systems. My research thus focuses on statistical methods for high-dimensional networks. I also develop statistical machine learning methods for estimation and inference in high-dimensional problems (i.e., when there are more variables than observations), particularly, when variables and/or observations are correlated with each other. This intersection of statistical machine learning, network analysis is an emerging research area with many interesting methodological and applied problems.
A second direction of my research focuses on flexible (non-parametric) estimation and inference procedures for high-dimensional data. The goal here is to relax some of the (restrictive) assumptions of parametric approaches, which can result in biased estimation and inference. Recent developments in machine learning and artificial intelligence (ML/AI) offer many opportunities for developing such flexible methods. However, many ML/AI approaches are based on black-box algorithms that are difficult to interpret and are therefore not ideal for biomedical applications. Our work tries to address this limitation by developing explainable ML/AI methods as well as developing statistical inference procedures for ML/AI methods.
Finally, I am also interested in developing estimation and inference procedures that account for, and leverage, the complex structure of high-dimensional data. The goal of this research is use the external information about the structure of the data in order to develop methods that can offer better prediction performance or can better capture the signal in high dimensions.
The selected list of publications below highlights some of my past and current research projects. Please see my CV for the full list of publications or check out my Google Scholar profile. Software for some of our methods is also available on my GitHub page.
Selected Publications
Current and former group members are shown in pink
*: Equal contribution