Working Papers

• Sofie YN Delbare, Sara Venkatraman, Kate Scuderi, Martin T Wells, Mariana F Wolfner, Sumanta Basu , Andrew G Clark. Time series transcriptome analysis uncovers regulatory networks and a role for the circadian clock in the Drosophila melanogaster female’s response to Sex Peptide.


• Chiranjit Dutta, Nalini Ravishanker, Sumanta Basu . Modeling Multivariate Positive-Valued Time Series Using R-INLA.


• Sara Venkatraman, Sumanta Basu , Andrew G Clark, Sofie YN Delbare, Myung Hee Lee, Martin T Wells. An Empirical Bayes Approach to Estimating Dynamic Models of Co-regulated Gene Expression.


• Kara Karpman*, Samriddha Lahiry*, Diganta Mukherjee and Sumanta Basu . Exploring financial networks using quantile regression and granger causality .


• Yiming Sun, Yige Li, Amy Kuceyeski and Sumanta Basu . Large Spectral Density Matrix Estimation by Thresholding.


• Karl Kumbier, Sumanta Basu , James B. Brown, Susan Celniker and Bin Yu. Refining interaction search through signed iterative Random Forests .


• Sumanta Basu , Sreyoshi Das, George Michailidis and Amiyatosh Purnanandam. A system-wide approach to measure connectivity in the financial sector .

Selected Publications

An up to date list of my papers can be found on my Google Scholar page.

• Sumanta Basu and Suhasini Subba Rao (2022). Graphical models for nonstationary time series . Accepted to The Annals of Statistics .


• Chiranjit Dutta, Kara Karpman, Sumanta Basu and Nalini Ravishanker (2022). Review of Statistical Approaches for Modeling High-Frequency Trading Data . Sankhya B. .


• Arkajyoti Saha, Sumanta Basu , Abhirup Datta (2021). Random forests for spatially dependent data . Journal of the American Statistical Association , forthcoming. [R package RandomForestsGLS ].


• Ines Wilms*, Sumanta Basu *, Jacob Bien, David S Matteson (2021). Sparse identification and estimation of large-scale vector autoregressive moving averages . Journal of the American Statistical Association , forthcoming. [R package bigtime ].


• Florencia Schlamp, Sofie YN Delbare, Angela M Early, Martin T Wells, Sumanta Basu , Andrew G Clark (2021). Dense time-course gene expression profiling of the Drosophila melanogaster innate immune response . BMC genomics , 22 (1), 1-22.


• Liao Zhu, Sumanta Basu , Robert Jarrow and Martin T. Wells (2020). High-Dimensional Estimation, Basis Assets, and the Adaptive Multi-Factor Model . The Quarterly Journal of Finance , 10 (04), 2050017. [ Arxiv ]


• Erol C. Bayraktar, Konnor La, Kara Karpman, Gokhan Unlu, Ceren Ozerdem, Dylan J. Ritter, Hanan Alwaseem, Henrik Molina, Hans-Heinrich Hoffmann, Alec Millner, G. Ekin Atilla-Gokcumen, Eric R. Gamazon, Amy R. Rushing, Ela W. Knapik, Sumanta Basu , and Kivanc Birsoy (2020). Metabolic coessentiality mapping identifies C12orf49 as a regulator of SREBP processing and cholesterol metabolism . Nature Metabolism , 2, pages 487 - 498.


• Jie H. Gohlke*, Stacy M. Lloyd*, Sumanta Basu *, Vasanta Putluri, Shaiju K. Vareed, Uttam Rasaily, Danthasinghe Waduge Badrajee Piyarathna et al. (2019). Methionine-homocysteine pathway in African-American prostate cancer . JNCI cancer spectrum , 3(2).pkz019.


• Xiao Li, Yu Wang, Sumanta Basu , Karl Kumbier, and Bin Yu (2019). A Debiased MDI Feature Importance Measure for Random Forests . In Advances in Neural Information Processing Systems , pp. 8047-8057.


• Hamutal Arbel, Sumanta Basu , William W. Fisher, Ann S. Hammonds, Kenneth H. Wan, Soo Park, Richard Weiszmann, Benjamin W. Booth, Soile V. Keranen, Clara Henriquez, Omid Shams Solari, Peter J. Bickel, Mark D. Biggin, Susan E. Celniker, and James B. Brown (2019). Exploiting regulatory heterogeneity to systematically identify enhancers with high accuracy , Proceedings of the National Academy of Sciences , 116(3), 900-908.


• Sumanta Basu *, Xianqi Li* and George Michailidis (2019). Low Rank and Structured Modeling of High-dimensional Vector Autoregressions , IEEE Transactions on Signal Processing , 67(5), 1207-1222.


• Kara Karpman and Sumanta Basu (2018). Learning Financial Networks using Quantile Granger Causality . Proceedings of the Fourth International Workshop on Data Science for Macro-Modeling with Financial and Economic Datasets (DSMM 2018) in ACM SIGMOD 2018 .


• Sumanta Basu *, Karl Kumbier*, James B. Brown and Bin Yu (2018). iterative Random Forests to discover predictive and stable high-order interactions (with Commentary ), Proceedings of the National Academy of Sciences , 115 (8) 1943-1948. [ CRAN , github ]


• Ines Wilms, Sumanta Basu , Jacob Bien and David S. Matteson (2017). Interpretable Vector AutoRegressions with Exogenous Time Series , NIPS 2017 Workshop on Interpretable Machine Learning Symposium Proceedings .


• Sumanta Basu *, William Durren*, Charles R. Evans, Charles F. Burant, George Michailidis and Alla Karnovsky (2017). Sparse network modeling and Metscape-based visualization methods for the analysis of large-scale metabolomics data , Bioinformatics , 33(10): 1545-1553 . [ software ]


• Jiahe Lin*, Sumanta Basu *, Moulinath Banerjee and George Michailidis (2016). Penalized Maximum Likelihood Estimation of Multi-layered Gaussian Graphical Models , Journal of Machine Learning Research , 17(146):1-51, 2016 .


• Sumanta Basu and George Michailidis (2015). Regularized estimation of sparse high-dimensional time series models . Annals of Statistics , 43(4), 1535-1567.


• Sumanta Basu , Ali Shojaie and George Michailidis (2015). Network Granger causality with inherent grouping structure . Journal of Machine Learning Research , 16, 417-453.


• Akash K Kaushik, Shaiju K Vareed, Sumanta Basu , Vasanta Putluri, Nagireddy Putluri, Katrin Panzitt, Christine A Brennan, Arul M Chinnaiyan, Ismael A Vergara, Nicholas Erho, Nancy L Weigel, Nicholas Mitsiades, Ali Shojaie, Ganesh Palapattu, George Michailidis and Arun Sreekumar (2014). Metabolomic profiling identifies biochemical pathways associated with castration-resistant prostate cancer . Journal of proteome research , 13(2), 1088-1100.


• Ali Shojaie, Sumanta Basu and George Michailidis (2012). Adaptive thresholding for reconstructing regulatory networks from time-course gene expression data . Statistics in Biosciences , 4(1), 66-83.

[*]: equal contribution