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 highdimensional networks.
I also develop statistical machine learning methods for estimation and inference in highdimensional 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 (nonparametric) estimation and inference procedures for highdimensional 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 blackbox 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 highdimensional 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
NetworkBased Analysis

Zhao S. and Shojaie A. (2016) "A Significance Test for GraphConstrained Estimation", Biometrics; available on arXiv

Ma J., Shojaie A. and Michailidis G., (2016)
"NetworkBased 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

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 Rpackage NetGSA.
Undirected Graphical Models

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 highdimensional 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

Voorman A., Shojaie A. and Witten D. (2014)
"Graph Estimation with Joint Additive Models",
Biometrika; pdf file
 Implemented in Rpackage spacejam
 Winner of David Byar Young Investigator Travel Award, Biometrics Section, ASA
Graphical Models for HighDimensional Time Series

Tank A., Covert I., Foti N., Shojaie A. and Fox E. (2022) "Neural Granger Causality", IEEETPAMI; available on arXiv

Safikhani A. and Shojaie A. (2022) "Joint Structural Break Detection and Parameter Estimation in HighDimensional NonStationary 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 mixedfrequency 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 MutuallyExciting Multivariate Hawkes Process", Electronic Journal of Statistics (EJS); available online

Chen S., Shojaie A. and Witten D. (2016)
"Network Reconstruction from HighDimensional 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
Flexible and Efficient Estimation and Inference Procedures

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 HighDimensional Regression with Unknown Variable Grouping", Technometrics pdf file
Causal Discovery (Learning Directed Graphical Models)

S. Kucukyavuz*, Shojaie A.*, Manzour H., Wei L., and Wu H.H. (2023) Consistent SecondOrder Conic Integer Programming for Learning Bayesian Networks. Journal of Machine Learning Research (JMLR)

Sondhi A. and Shojaie A. (2019) "The Reduced PCAlgorithm: 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

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 Rpackage spacejam
Applied and Collaborative Papers

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 restorationmediated lifespan extension in Drosophila"; PLoS Genetics

Kaushik A.^{*}, Shojaie A.^{*}, Panzitt K.^{*}, ... and Sreekumar A. (2016)
"Inhibition of the hexosamine biosynthetic pathway promotes castrationresistant 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., VivekanandanGiri 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