Publications

The following are publications by Dr.Suprateek Kundu, organized by year. For additional details, please visit Google Scholar.

+ as advisee, * as corresponding author

2024

Peer-Reviewed Publications: Theory, Methods and Applications

  1. Dey, A., and Kundu*, S. (2024). A network analysis approach to evaluating COVID-19 vaccine acceptance in the US. Accepted in Statistics in Medicine.

  2. Kundu*, S., and Lukemire, J. (2024).Bayesian Non-parametric Modeling of Population of Vector Auto- regressions. The Journal of Machine Learning Research, 25(146), pp 1–52

  3. Lyu*, R., Vannucci, M., and Kundu, S. (2024). Bayesian Tensor Models for Image-based Classification of Alzheimer’s Disease. Neuroinformatics doi:10.1007/s12021-024-09669-33

  4. Wilson*, M., Needham, T., Park, C., Kundu, S., and Srivastava, A. (2024). A Wasserstein-type Distance for Wrapped Gaussian Mixtures on Riemannian Manifolds. SIAM Journal on Imaging Sciences, 17(3):1433 - 1466 doi:10.1137/23M1620363

  5. Li, W., Chang, C., Kundu, S., Long, Q. (2024). Accounting for Network Noise in Graph-guided Bayesian Modeling of Structured High-dimensional Data. Biometrics, 80(1) doi:10.1093/biomtc/ujae012

Manuscript with Invited Revision

  1. Ma*, X., and Kundu, S. (2024+). A Unified Sparse Learning Framework for Lipschitz Loss Functions with Measurement Errors.

  2. Ming, J., and Kundu*, S. (2024+). Flexible Bayesian Support Vector Machines for Brain Network-based Classification.

  3. Lange, S., Idel, D., Reinhardt, A., Labbate, C., Adibi, M., Kundu, S., Matin, S. (2024+). Expert con- sensus of variables that impact endoscopic management of upper tract urothelial carcinoma: Development of the Endometry Score. Revision Invited.

  4. Chakraborty, N., Long, Q., and Kundu*, S. (2024+). Scalar-on-Image Regression with Spatial Interac- tions.

Submitted Manuscripts

  1. Yao, T., and Kundu*, S. (2024). Flexible Bayesian Nonparametric Product Mixtures for Multi-scale Functional Clustering.

  2. Lyu, R., Vannucci, M., Kundu*, S. (2024). Bayesian Scalar-on-Tensor Quantile Regression for Longitu- dinal Neuroimaging Data in Alzheimer’s Disease.

  3. Reinhardt, A., Nikzad, N., Park, P.C., Hollis, R.J., Jacobson, G., Roach, M.A., Beretta, L., Jalal, P.K., Fuentes, D., Koay, E., and Kundu, S. (2024). Heterogeneous Image-based Risk Prediction Using Distributional Data Analysis.

  4. Amer, A., Khose, S., Pokhylevych, H., Alhasan, H., Calle, S., Liu, H., Kundu, S., Johnson, J.M. (2024+). Dynamic Contrast Enhancement Processing Comparison for Determining True Progression from Pseudo- progression in High Grade Glioma.

  5. Zhang, Q., Li, W., Kundu, S., and Long, Q. (2024). Knowledge-guided Bayesian Biclustering Model with Denoised Networks.

  6. Corrigan, K., Holliday, E., Nyugen, S., Kundu, S., Reinhardt, A., Smith, L., Das, P., You, Y. (2024). Prospective evaluation of pelvic radiation among patients with young-onset rectal cancer: Impact on patient-reported outcomes.

  7. Holliday, E., Yao, T.H., Kundu, S. (2024). Post-Radiation Vaginal Estrogen Prescriptions for Women with Early Onset Rectal Cancer.

  8. Zaveri, S., Chen, J.H., Meas, S., Yang, Z., Kundu, S., Lucci, A. (2024). Primary Tumor Surgery in Patients with de novo Stage IV Breast Cancer: Is there an Optimal Subgroup for Locoregional Therapy?

  9. Lin, T., Mori, L., Batra, A., Chakraborty, N., Kundu, S. (2024). Utilization of Breathwork Meditation in Improving Wellness in Trainees of Speech-Language Pathology A Prospective Cohort Study.

  10. Lam K., Ozkizilkaya, H.I., Milton, D.R., Dono, A., Liu, Y., Kundu, S., Kumar, V.A., Johnson, J., Esquenazi, Y., Patel, C.B., Ballester, L.Y. (2024). Molecular alterations in IDH-mutant astrocytoma: A multi-institutional retrospective study.

Manuscripts Under Preparation

  1. Drexler, M., Kundu, S., Risk, B., Lah, J., and Qiu, D. (2024). Deep Learning based Joint and individual variance explained (DeepJIVE) for multimodal data analysis.

  2. Reinhardt, A.E. and Kundu, S. (2024). Voxel-level Neuroimaging Data Harmonization via Tensors.

  3. Li, W., Long, Q., and Kundu, S. (2024). Scalar-on-Function Prediction Using Noisy fMRI Data.

  4. Chakraborty, M., Ha, M.J., Ma, X., Kundu, S. (2024). Longitudinal Bayesian Tensor Response Regression for Mapping Genetic Signatures Associated with Brain Structural Changes.

  5. Fu, J., Kundu, S., and Vannucci, M. (2024). A Bayesian Latent-Scale Approach for High-dimensional Network Mediation Models.

Peer-Reviewed Publications: Collaborative

  1. Woodland, M., Castelo, A., Al Taie, M., Albuquerque, J., Silva, M., Eltaher, M., Mohn, F., Shieh, A., Kundu, S., Yung, J.P., Patel, A.B., and Brock, K.K. (2024). Feature Extraction for Generative Medical Imaging Evaluation: New Evidence Against an Evolving Trend. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15012. Springer, Cham https://doi.org/10.1007/978-3-031-72390-2_9

  2. Kiani, S., Eggebeen, J., Al-Gibbawi, M., Smith, P., Preiser, T., Kundu, S., Zheng, Z., Bhatia, N.K., Shah, A.D., Westerman, S.B., De Lurgio, D.B., Tompkins, C.M., Patel, A.M., El-Chami, M.F., Merchant, F.M., Lloyd, M.S. (2024). Costs, efficiency, and patient-reported outcomes associated with suture-mediated percutaneous closure for atrial fibrillation ablation: Secondary analysis of a randomized clinical trial. Journal of Cardiovascular Electrophysiology doi:10.1111/jce.16440

  3. Barcena, A.J.R., Ravi, P., Kundu, S., Tappa, K. (2024). Emerging Biomedical and Clinical Applications of 3D-Printed Polylactic Acid-Based Devices and Delivery Systems. Bioengineering (Basel). 11(7):705 doi:10.3390/bioengineering11070705

  4. Gottiparthy, A., Tummala, S., Yang, Z., and Kundu, S. (2024). Neurofilament light chain as a novel biomarker for diagnosis, prognostication, and recovery in cancer patients: case series. CNS Oncology, 13(1):2386233 doi:10.1080/20450907.20245

  5. Biermann, M., Godiers, M., Kundu, S., Jain, A.S. (2024). The Functional Lumen Imaging Probe Con- tractile Response Pattern is the Best Predictor of Botulinum Toxin Response in Esophagogastric Junction Outflow Obstruction. Neurogastroenterology and Motility, 36(9):e14859 doi:10.1111/nmo.14859

  6. Dal Lago, E.A., Guimaraes de Sousa, L., Yang, Z., Bonini, F., Sawyer, M., Wang, K., Lewis, W., Kundu, S., Godoy, M., Ferrarotto, R. (2024). Prognostic value of tumor volume doubling time in lung-metastatic adenoid cystic carcinoma. Oral Oncology, 151 doi: 10.1016/j.oraloncology.2024.106759

  7. Wahid, K., Sahin, O., Kundu, S., Lin, D., Tehami, S., Fuentes, D., Gillespie, E., Fuller, C. (2024). Associations Between Radiation Oncologist Demographic Factors and Segmentation Similarity Benchmarks: Insights From a Crowd-Sourced Challenge Using Bayesian Estimation. JCO Clinical Cancer Informatics, 8:e2300174 doi:10.1200/CCI.23.00174

2023

Peer-Reviewed Publications: Theory, Methods and Applications

  1. Liu, Y., Chakraborty, N., Qin, Z.S., and Kundu*, S. (2023). Integrative Bayesian Tensor Regression for Imaging Genetics Applications. Frontiers in Neuroscience, 17:1212218 doi:10.3389/fnins.2023. 1212218

  2. Kundu, S., Reinhardt*, A., Song, S., Krishnamurthy, V. (2023). Bayesian Longitudinal Tensor Response Regression for Modeling Neuroplasticity. Human Brain Mapping, 44(18), 63266348 doi.org/10.1002/hbm.26509

  3. Yang, L., Ma*, X., Sunderaman, R., Ji, S., and Kundu, S. (2023). Accounting for Temporal Variability in Functional Magnetic Resonance Imaging Improves Prediction of Intelligence. Human Brain Mapping, 44(13):4772-4791. doi:10.1002/hbm.26415

Peer-Reviewed Publications: Collaborative

  1. Barcena, A.J., Dhal, K., Patel, P., Ravi, P., Kundu, S. and Tappa, K. (2023). Current Biomedical Applications of 3D-printed Hydrogels. Gels, 10(1):8 doi:10.3390/gels10010008

  2. Nieto, Y., Yang, Z., Valdez, B., Bassett, R., Kundu, S., …, and Qazilbash, M. (2023). Safety and efficacy of a new high-dose chemotherapy regimen of panobinostat, gemcitabine, busulfan and melphalan for 1st or 2nd salvage autologous stem cell transplant for patients with refractory/relapsed or high-risk myeloma: Matched pair comparisons with concurrent control cohorts. American Journal of Hematology, 99(2):245-253doi:10.1002/ajh.27168

  3. Biermann, M., Hersh, M., Kline, M., Fowler, H. Calderon, L., Godiers, M., Kundu, S., Jain, A. (2024). Functional Lumen Imaging Probe Topography Identifies Patients with Normal Acid Exposure and Esophageal Hypervigilance Amongst Proton-Pump Inhibitor Non-Responders. Surgical Endoscopy, 38(1):291-299 doi:10.1007/s00464-023-10556-2

  4. Alkhaldi, H., Reinhardt, A., barnett, M., Kundu, S., Hosing, C., Ramdial, J., Saini, N., Srour, S., Alousi, A., Kebriaei, P., Popat, U., Qzailbash, M., Champlin, R., Shpall, E.J., Pinnix, C., Ahmed, S., Steiner, R., Andersson, B.S., and Nieto, Y. (2023). High-Dose Chemotherapy and Autologous Stem-Cell Transplant with Consolidative Radiation Therapy for Relapsed or Refractory Primary Mediastinal B-Cell Lymphoma, Transplantation and Cellular Therapy, 29(11):690-694 doi:10.1016/j.jtct.2023.08.019

  5. Young, A.L., Kundu, S., Tappa, K.K., Liu, H.A., and Kumar, V. (2023). Incidence of resting-state functional connectivity to secondary language areas and contralateral language areas in patients with brain tumors. Neuroradiology Journal doi:10.1177/19714009231196471

  6. Liang, T., Katz, M., Prakash, L., Chatterjee, D., Wang, H, Kim, M, Tzeng, C, Ikoma, N., Wolff, R., Zhao, D., Koay, E., Maitra, A., Kundu, S., Wang, H. (2023). Comparative Analyses of the Clinicopathologic Features of Short and Long Survivors of Patients With Pancreatic Ductal Adenocarcinoma Who Received Neoadjuvant therapy and Pancreaticoduodenectomy. Cancers, 15, 3231 doi:10.3390/cancers15123231

  7. Lauck, K., Ahmad, A.S., Nguyen, Q.D., Yang, Z., Kundu, S., Huen, A.O. (2023). Management Strategies and Survival in Cutaneous B Cell Lymphoma: A Population Based Study. JAAD International, 13:28-29 doi:10.1016/j.jdin.2023.06.015

  8. Yacout, H., Smith, B.L., Foster, S., Lora, M., Niles-Carnes, L.V., Zheng, Z., Kundu, S., Cantos, V.D. (2023). PrEP Adherence and Discontinuation at a Patient-Centered PrEP Program in Atlanta, GA. Journal of the American College of Clinical Pharmacy, 6(6): 576-580 doi:10.1002/jac5.1792

  9. Tappa, K., and Kundu, S. (2023). Applications of Additive Manufacturing Technologies in healthcare sectors during Covid-19 Pandemic. American Journal of Biomedical Science and Research, 18(3) doi: 10.34297/AJBSR.2023.18.002480

  10. Drwiega, E., Rab, S., Schechter, M., Andruski, R., Kundu, S., Zheng, Z., and Patel, M. (2023). Impact of a Dedicated Outpatient Parenteral Antimicrobial Therapy Program on Peripherally Inserted Cen- tral Catheter Removal at a Large Academic Medical Center. Infectious Diseases in Clinical Practice, 31(2):e1174 doi:10.1097/IPC.00000000000011746

2022

Peer-Reviewed Publications: Theory, Methods and Applications

  1. Wu*, Y., Kundu, S., Stevens, J.S., Srivastava, A. (2022). Elastic Shape Analysis of Sub-cortical Structures in Posttraumatic Stress Disorder. Frontiers in Neuroscience, 16:954055 doi:10.3389/fnins.2022.954055

  2. Xin+, Ma., Kundu, S. (2022). Multi-task Learning with High-Dimensional Noisy Images. The Journal of the American Statistical Association, Theory and Methods, 119(545), 650663 doi:10.1080/01621459.2022.2140052.

  3. Ma+, X., Kundu*, S., and Stevens, J. (2022). Semi-parametric Bayes Regression with Network Valued Covariates. Machine Learning Journal.111:37333767 doi:10.1007/s10994-022-06174-z.

Peer-Reviewed Publications: Collaborative

  1. Mitchell, D., Buszek, S.M., Tran, B., Farhat, M., Goldman, J., Erickson, L., Curl, B., Liu, H., Suki, D., Ferguson, S., Kundu, S., and Chung, C. (2022). Comparison of Radiomic Feature Variability between Dif- ferent MR Pulse Sequences in Brain Metastases. F1000Research, 11:892 doi:10.12688/f1000research. 122871.1

  2. Woodland, M. K., Wood, J., Anderson, B. M., Kundu, S., Lin, E., Koay, E., Odisio, B., Chung, C., Kang, H. C., Venkatesan, A. M., Yedururi, S., De, B., Lin, Y. M., Patel, A. B., and Brock, K. K. (2022). Evaluating the Performance of StyleGAN2-ADA on Medical Images. In: Zhao, C., Svoboda, D., Wolterink, J.M., Escobar, M. (eds) Simulation and Synthesis in Medical Imaging. SASHIMI 2022. Lecture Notes in Computer Science, vol 13570, Springer, Cham. doi:10.1007/978-3-031-16980-9_14

  3. Singh S, Roszik J, Saini N, Singh VK, Bavisi K, Wang Z, Vien LT, Yang Z, Kundu S, Davis RE, Bover L, Diab A, Neelapu SS, Overwijk WW, Rai K, Singh M. (2022). B Cells Are Required to Generate Optimal Anti-Melanoma Immunity in Response to Checkpoint Blockade. Frontiers of Immunology, May 26;13:794684 doi:10.3389/fimmu.2022.794684

  4. Godwin, L., Zheng, Z., Kundu, S., Cousins, R., Mullinax, B.J., Ko, Y., Little, K., Smith, A., Quyyumi, A., Goyal, A., Pearson, T., Moncayo, V., Mitchell, A.J. (2022). Serial Myocardial Perfusion Imaging in Kidney Transplant Candidates: Risk Factors Associated with New-Onset Perfusion Abnormalities. The American Journal of Cardiology, 174:84-88 doi:10.1016/j.amjcard.2022.03.030

  5. Jarrett, S.A., Lo, K.B., Body, C.B.B, Kim, J.J, Zheng, Z., Kundu, S., Huang, E., Basu, A., Flynn, M., Dietz-Lindo, K.A., Shahnavaz, N., Christie, J. (2022). Nausea, Vomiting and Dyspepsia Following Solid Organ Abdominal Transplant. Cureus, 14(4): e24274 doi:10.7759/cureus.24274

  6. Calderon, L.F., Kline, M., Hersh, M., Shah, K.P., Kundu, S., Tkaczuk, A., McColloch, N., Jain, A. (2022). The Upper Esophageal Sphincter Distensibility Index Measured Using Functional Lumen Imaging Probe Identifies Defective Barrier Function of the Upper Esophageal Sphincter. Journal of Neurogastroenterology and Motility, 28(3):463-473 doi:10.5056/jnm21197

2021

Peer-Reviewed Publications: Theory, Methods and Applications

  1. Kundu*, S., Min+, J., McGregor, K.M., Noecera, J. (2021). Integrative Analysis for Population of Dy- namic Networks with Covariates. NeuroImage, 236:118181 doi:10.1016/j.neuroimage.2021.118181

  2. Kundu*, S., Ming+, J., and Stevens, J. (2021). Dynamic Brain Functional Networks Guided By Anatom- ical Knowledge. Brain Connectivity, 11(7):529-542 doi:10.1089/brain.2020.0900

  3. Kundu*, S., and Risk, B.B. (2021). Bayesian Matrix Normal Graphical Models for Brain Network Estimation. Biometrics, 77(2):439-450 doi:10.1111/biom.13319

  4. Lukemire, J.D.+, Kundu*, S., Pagnoni, G., and Guo, Y. (2021). Bayesian Joint Modeling of Multiple Brain Functional Networks, Journal of the American Statistical Association, 116(534):518-530 doi: 10.1080/01621459

Peer-Reviewed Publications: Collaborative

  1. Krishnamurthy, L.C., Krishnamurthy, V., Rodriguez, A.D., McGregor, K.M., Champion, G.N., Hortman, K., Roberts, S.R., Harnish, S.M., Belagaje, S.R., Benjamin, M.L., Gopinath, K., Rosenbek, J.C., Mc- Couch1, N., Kundu, S., and Crosson, B.A. (2021). Not all lesioned tissue is equal: A new look at chronic stroke with Tissue Integrity Gradation via T2w T1w Ratio (TIGR). Frontiers in Neuroscience, Vol. 15 doi:10.3389/fnins.2021.665707

  2. Aldredge, A., Roth, G. Vaidya, A., Duarte, A.P. Kundu, S., Zheng, Z., Smith, B., Lora, M., Gruen, J., Sheth, A., Sales, J., Cantos, V.D. (2021). Preexposure prophylaxis care continuum among transgender women at a patient-centered preexposure prophylaxis program in Atlanta, Georgia. AIDS; 35(3):524-526 doi:10.1097/QAD.0000000000002788

  3. Anderson, A.M., Kundu, S., Tang, B., Vaida, F., Okwuegbuna, O., McClernon, D., Cherner, M., Cook- son, D., Crescini, M., Grant, I., Ellis, R.J., Letendre, S.L., (2021).Cerebrospinal fluid CXCL10 is as- sociated with the presence of low level CSF HIV during suppressive antiretroviral therapy. Journal of Neuroimmunology, Volume 353, 577493 doi:10.1016/j.jneuroim.2021.577493.

2020

Peer-Reviewed Publications: Theory, Methods and Applications

  1. Solis-Lemus, C. S., Ma, X.+, Hotstetter II, M., Kundu*,S., Peng, Q., Pimental, D., (2020). A Deep Learning Framework for Predicting Functional Markers in Flow Cytometry Data. Statistical Modeling in Biomedical Research doi:10.1007/978-3-030-33416-1_5

Peer-Reviewed Publications: Collaborative

  1. Beret, A., Lai, L., Xu, Y., Zheng, Z., Kundu, S., Lennox, J., Waldrop-Valverde, D., Franklin, D., Letendre, S., Anderson, A. (2020). Distinct cellular immune properties in cerebrospinal fluid are associated with cognition in HIV-infected individuals initiating antiretroviral therapy. Journal of Neuroimmunology, 344:577246 doi:10.1016/j.jneuroim.2020.577246

  2. Lyles, R. H., Cunningham, S. A., Kundu, S., Bassat, Q., Mandomando, I., Sacoor, C., Akelo, V., Onyango, D., Zielinski-Gutierrez, E., and Taylor, A. W. (2020). Extrapolating sparse gold standard cause of death designations to characterize broader catchment areas. Epidemiologic Methods, 9(1), doi: 10.1515/em-2019-0031.

  3. Krishnamurthy, V., Krishnamurthy, L.C., Drucker, J.H., Kundu, S., Ji, B., Hortman, K., Roberts, S.R., Mammino, K., Tran, S.M., Gopinath, K., McGregor, K.M., Rodriguez, A.D., Qiu, D., Crosson, B., Nocera, J.R.. (2020) Correcting Task fMRI Signals for Variability in Baseline CBF Improves BOLD- Behavior Relationships: A Feasibility Study in an Aging Model. Frontiers in Neuroscience, 14 (336), DOI=10.3389/fnins.2020.00336

  4. Hsu, D., Chokshi, F. H., Hudgins, P. A., Kundu, S., Beitler, J. J., Patel, M. R., and Aiken, A. H. (2020). Predictive Value of First Posttreatment Imaging Using Standardized Reporting in Head and Neck Cancer. Otolaryngology - Head and Neck Surgery, 41(6):1070-1075. doi:10.3174/ajnr.A6589

2019

Peer-Reviewed Publications: Theory, Methods and Applications

  1. Kundu*, S., Lukemire+ J., Wang, Y., and Guo, Y. (2019). A Novel Joint Brain Network Analysis for Lon- gitudinal Alzheimer’s Disease Data, Scientific Reports, 9(1), 19589 doi:10.1038/s41598-019-55818-z

  2. Higgins, I.+, Kundu, S., Choi, K.S., and Mayberg, H., and Guo, Y. (2019). A Differential Degree Test for Comparing Brain Networks. Human Brain Mapping, 40(15), 4518-4536 doi:10.1002/hbm.24718

  3. Kundu*, S., and Suthaharan, S., (2019). Privacy-Preserving Predictive Model Using Factor Analysis for Neuroscience Applications, IEEE 5th Intl Conference on Big Data Security on Cloud (BigDataSe- curity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS), Washington, DC, USA, 2019, pp. 67-73. doi: 10.1109/BigDataSecurity-HPSC-IDS.2019.00023

Peer-Reviewed Publications: Collaborative

  1. Sule, P., Tilvawala, R., Mustapha, T., Hassounah, H., Noormohamed, A., Kundu, S., Graviss, E., Walkup, G., Kong, Y., and Cirillo, J., (2019). Rapid Tuberculosis Diagnosis Using Reporter Enzyme Fluorescence (REF). Journal of Clinical Microbiology, 57(12):e01462-19 doi:10.1128/JCM.01462-19

Before 2018

Peer-Reviewed Publications: Theory, Methods and Applications

  1. Li, Z.,Chang, C., Kundu, S., and Long, Q. (2018). Bayesian Generalized Biclustering Analysis via Adaptive Structured Shrinkage. Biostatistics, 21(3):610-624 doi:10.1093/biostatistics/kxy081

  2. Chang, C., Kundu*, S., and Long, Q. (2018). Scalable Bayesian Variable Selection for Structured High Dimensional Data. Biometrics, 74(4):1372-1382 doi:10.1111/biom.12882.

  3. Higgins, I+., Kundu*, S. and Guo, Y. (2018). Integrative Bayesian analysis of brain functional networks incorporating anatomical knowledge. NeuroImage, 181:263-278 doi:10.1016/j.neuroimage.2018.07.015

  4. Kundu*, S., Ming+, J., Pierce, J., McDowell, J., and Guo, Y. (2018). Estimating Dynamic Brain Functional Networks Using Multi-subject fMRI Data. NeuroImage, Volume 183, Pages 635-649 doi: 10.1016/j.neuroimage.2018.07.045

  5. Kundu*, S., Cheng, Y., Shin, M., Manyam, G., Mallick, B.K., Baladandayuthapani, V. (2018). Bayesian Variable Selection with Structure Learning: Applications to Integrative Genomics. PLOS ONE, 13(7): e0195070 doi:10.1371/journal.pone.0195070.

  6. Kundu*, S., Mallick, B.K., and Baladandayuthapani, V. (2018). Efficient Bayesian Regularization for Graphical Model Selection. Bayesian Analysis, 14(2):449-476 doi:10.1214/17-ba1086doi:10.1214/ 17-BA1086

  7. Kundu*, S. and Kang, J. (2016). Semi-parametric Bayes Graphical Models Incorporating Covariates for Imaging Genetics Applications. STAT, 6(1), 322-337 doi:10.1002/sta4.119

  8. Kundu*, S., and Dunson, D. (2014). Bayesian Variable Selection in Semi-parametric Linear Models. Jour- nal of the American Statistical Association, Theory and Methods, 109, 437-447 doi:10.1080/01621459.2014.881153

  9. Kundu, S., and Dunson, D. (2014). Latent Factor Models for Density Estimation. Biometrika, 101, 641-654 doi:10.1093/biomet/asu019

  10. Gouskova, N.A., Kundu, S., Imrey, P.B., Fine, J.P. (2013). Number Needed to Treat for Time to Event Data with Competing Risks, Statistics in Medicine, 33, 181-192 doi:10.1002/sim.5922

Peer-Reviewed Publications: Collaborative

  1. Hanna TN, Kundu, S., Singh K, Horny M, Wood D, Prater A, Duszak R Jr. (2018). Emergency department imaging superusers. Emergency Radiology, 26(2):161-168 doi:10.1007/s10140-018-1659-y

  2. Chokshi, F.H., Kang, J., Kundu, S., Castillo, M. (2016). Bibliometric Analysis of Manuscript Title Characteristics Associated With Higher Citation Numbers: A Comparison of Three Major Radiology Journals, AJNR, AJR, and Radiology. Current Problems in Diagnostic Radiology, 45(6):356-360 doi:10.1067/j.cpradiol.2016.03.002