Publications

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

+= advisee, * = corresponding author

2025

Peer-Reviewed Publications: Senior, Corresponding or Second Author

  1. Yao, T.H., Treekitkarnmongkol, W., Putluri, N., Sankaran, D., Nguyen, T., Balasenthil, S., Hurd, M.W., Chen, M., Brand, R.E., Lampe, P.D., Hu, T.Y., Maitra, A., Koay, E., Killary, A.M., Sen, S., and Kundu, S. (2025). Machine Learning-Based Multimodal Biomarkers Enable Accurate Diagnosis and Early Detection of Pancreatic Ductal Adenocarcinoma. Accepted in Scientific Reports.

  2. Chakraborty, N., Long, Q., and Kundu, S. (2025). Scalar-on-Image Regression with Spatial Interactions. Accepted in Biometrics*.

  3. Dey, A., and Kundu, S. (2025). A network analysis approach to evaluating COVID-19 vaccine acceptance in the US. Statistics in Medicine*, 44(18–19). https://doi.org/10.1002/sim.70109

Peer-Reviewed Publications: Collaborative

  1. Pirhoushiaran, M., Walker-Charles, K., Yao, T.-H., Putluri, S., Patel, N., Wang, D.H., Wang, I., Arjuna, S., Dono, A., Bueno-Alvarez, A., Nguyen, S., Balar, A., Huse, J.T., Kundu, S., Esquenazi, Y., Patel, C.B., Prabhu, S., Lang, F.F., and Ballester, L.Y. (2025). Exploring CSF MicroRNA Signatures as Diagnostic Biomarkers in Adult-type Diffuse Gliomas. Accepted in Non-coding RNA Research.

  2. Walker, C.M., Maldonado, M.G., Jacobsen, M.C., Kundu, S., Underwood, M.L., Yung, J.P., Reed, B.J., Jaffray, D., Stafford, J., Hicks, M.E., Chung, C., and Venkatesan, A.M. (2025). Initial Experience with Remote MRI Scanning Support in an Oncology Focused Practice: Opportunities for Expanded Access to Radiology Care. Accepted in Journal of Applied Clinical Medical Physics.

  3. Zaveri, S., Chen, J.H., Meas, S., Yang, Z., Kundu, S., and Lucci, A. (2025). Primary Tumor Surgery in Patients with de novo Stage IV Breast Cancer: Is there an Optimal Subgroup for Locoregional Therapy? Accepted in American Journal of Surgery.

  4. Corrigan, K., Holliday, E., Nyugen, S., Kundu, S., Reinhardt, A., Smith, L., Das, P., and You, Y. (2025). Prospective evaluation of pelvic radiation among patients with young-onset rectal cancer: Impact on patient-reported outcomes. Accepted in Colorectal Cancer.

  5. MacDonald, L., Choi, H., Kang, H.C., Diaz De Leon, A., Kundu, S., Qiao, W., Li, Z., Inguillo, I.A., Maharjan, A., Gregg, J.R., and Ward, J.F. (2025). MRI-US Volume Discrepancy as a Predictor of Targeted Biopsy Performance. Accepted in JU OpenPlus.

  6. Lam, K., Ozkizilkaya, H.I., Milton, D.R., Dono, A., Liu, Y., Kundu, S., Kumar, V.A., Johnson, J., Esquenazi, Y., Patel, C.B., and Ballester, L.Y. (2025). Molecular alterations in IDH-mutant astrocytoma: A multi-institutional retrospective study. Neuro-Oncology Advances, 7(1), vdaf088. https://doi.org/10.1093/noajnl/vdaf088

  7. Lin, T., Mori, L., Batra, A., Chakraborty, N., and Kundu, S. (2025). Utilization of Breathwork Meditation in Improving Wellness in Trainees of Speech-Language Pathology: A Prospective Cohort Study. Accepted in Perspectives of the ASHA Special Interest Groups.

  8. Amer, A., Khose, S., Pokhylevych, H., Alhasan, H., Calle, S., Liu, H., Kundu, S., and Johnson, J.M. (2025). Dynamic Contrast Enhancement Processing Comparison for Determining True Progression from Pseudoprogression in High Grade Glioma. Journal of Computer Assisted Tomography, 49(4):65661. https://doi.org/10.1097/RCT.0000000000001716

  9. Lange, S., Idel, D., Reinhardt, A., Labbate, C., Adibi, M., Kundu, S., and Matin, S. (2025). Expert consensus of variables that impact endoscopic management of upper tract urothelial carcinoma: Development of the Endometry Score. Journal of Urology, 213(4):46774.

Manuscript with Invited Revision

  1. Ming, J., and Kundu, S. (2025). Flexible Bayesian Support Vector Machines for Brain Network-based Classification. Neuroinformatics*.

Submitted Manuscripts

  1. Ma*, X., and Kundu, S. (2025). A Unified Sparse Learning Framework for Lipschitz Loss Functions with Measurement Errors.

  2. Drexler, M., Risk, B., Lah, J., Kundu, S., and Qiu, D. (2025). Deep Learning based Joint and Individual Variance Explained (DeepJIVE) for multimodal data analysis.

  3. Fu, J., Kundu, S., and Vannucci, M. (2025). A Bayesian Latent-Scale Approach for High-dimensional Network Mediation Models.

  4. 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. (2025). Heterogeneous Image-based Risk Prediction Using Distributional Data Analysis.

Manuscripts Under Preparation

  • McCullum, L., Hwang, K., Kundu, S., Subashi, E., Fuller, C., and Stafford, J. (2025). Conventional Versus Synthetic MR Generated Contrast-Weighted MRI Images: A Multi-Vendor Contrast Distortion Analysis.

  • Holliday, E., Yao, T.H., and Kundu, S. (2025). Post-Radiation Vaginal Estrogen Prescriptions for Women with Early Onset Rectal Cancer.

  • Reinhardt, A.E., and Kundu, S. (2025). Voxel-level Neuroimaging Data Harmonization via Tensors.

  • Li, W., Long, Q., and Kundu, S. (2025). Scalar-on-Function Prediction Using Noisy fMRI Data.

  • Chakraborty, M., Ha, M.J., Ma, X., and Kundu, S. (2025). Longitudinal Bayesian Tensor Response Regression for Mapping Genetic Signatures Associated with Brain Structural Changes.

2024

Peer-Reviewed Publications: Senior, Corresponding or Second Author

  1. Chen, Z., Luo, H., Kundu, S., and Vannucci, M. (2024). Tensor Decision Trees for High-Resolution Imaging Data. In 2024 IEEE International Conference on Big Data (BigData), Washington, DC, USA, pp. 8644–8646. https://doi.org/10.1109/BigData62323.2024.1082517

  2. Kundu, S., and Lukemire, J. (2024). Bayesian Non-parametric Modeling of Population of Vector Autoregressions. The Journal of Machine Learning Research*, 25(146), 1–52. http://jmlr.org/papers/v25/22-0717.html

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

  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. https://doi.org/10.1137/23M1620363

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

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). 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., and 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, 35(12):2372–2381. https://doi.org/10.1111/jce.16440

  3. Barcena, A.J.R., Ravi, P., Kundu, S., and Tappa, K. (2024). Emerging Biomedical and Clinical Applications of 3D-Printed Polylactic Acid-Based Devices and Delivery Systems. Bioengineering (Basel), 11(7):705. https://doi.org/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. https://doi.org/10.1080/20450907.2024

  5. Biermann, M., Godiers, M., Kundu, S., and Jain, A.S. (2024). The Functional Lumen Imaging Probe Contractile Response Pattern is the Best Predictor of Botulinum Toxin Response in Esophagogastric Junction Outflow Obstruction. Neurogastroenterology and Motility, 36(9):e14859. https://doi.org/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., and Ferrarotto, R. (2024). Prognostic value of tumor volume doubling time in lung-metastatic adenoid cystic carcinoma. Oral Oncology, 151:106759. https://doi.org/10.1016/j.oraloncology.2024.106759

  7. Wahid, K., Sahin, O., Kundu, S., Lin, D., Tehami, S., Fuentes, D., Gillespie, E., and 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. https://doi.org/10.1200/CCI.23.00174

  8. Biermann, M., Hersh, M., Kline, M., Fowler, H., Calderon, L., Godiers, M., Kundu, S., and 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. https://doi.org/10.1007/s00464-023-10556-2

Manuscript with Invited Revision

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

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

  3. Lyu, R., Vannucci, M., and Kundu, S. (2024). Bayesian Scalar-on-Tensor Quantile Regression for Longitudinal Neuroimaging Data in Alzheimer’s Disease. Annals of Applied Statistics*.

2023

Peer-Reviewed Publications: Senior, Corresponding or Second Author

  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. https://doi.org/10.3389/fnins.2023.1212218

  2. Kundu, S., Reinhardt, A., Song, S., and Krishnamurthy, V. (2023). Bayesian Longitudinal Tensor Response Regression for Modeling Neuroplasticity. Human Brain Mapping, 44(18):63266348. https://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. https://doi.org/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. https://doi.org/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):245–253. https://doi.org/10.1002/ajh.27168

  3. 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. https://doi.org/10.1016/j.jtct.2023.08.019

  4. 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. https://doi.org/10.1177/19714009231196471

  5. 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., and 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. https://doi.org/10.3390/cancers15123231

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

  7. Yacout, H., Smith, B.L., Foster, S., Lora, M., Niles-Carnes, L.V., Zheng, Z., Kundu, S., and 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. https://doi.org/10.1002/jac5.1792

  8. 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). https://doi.org/10.34297/AJBSR.2023.18.002480

  9. 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 Central Catheter Removal at a Large Academic Medical Center. Infectious Diseases in Clinical Practice, 31(2):e1174. https://doi.org/10.1097/IPC.0000000000001174

2022

Peer-Reviewed Publications: Senior, Corresponding or Second Author

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

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

  3. Xin+, M., Kundu, S., and Stevens, J. (2022). Semi-parametric Bayes Regression with Network Valued Covariates. Machine Learning*, 111:37333767. https://doi.org/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 Different MR Pulse Sequences in Brain Metastases. F1000Research, 11:892. https://doi.org/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: SASHIMI 2022, Lecture Notes in Computer Science, vol 13570. Springer, Cham. https://doi.org/10.1007/978-3-031-16980-9_14

  3. Singh, S., Roszik, J., Saini, N., Singh, V.K., Bavisi, K., Wang, Z., Vien, L.T., Yang, Z., Kundu, S., Davis, R.E., Bover, L., Diab, A., Neelapu, S.S., Overwijk, W.W., Rai, K., and Singh, M. (2022). B Cells Are Required to Generate Optimal Anti-Melanoma Immunity in Response to Checkpoint Blockade. Frontiers in Immunology, 13:794684. https://doi.org/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., and 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. https://doi.org/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., and Christie, J. (2022). Nausea, Vomiting and Dyspepsia Following Solid Organ Abdominal Transplant. Cureus, 14(4):e24274. https://doi.org/10.7759/cureus.24274

  6. Calderon, L.F., Kline, M., Hersh, M., Shah, K.P., Kundu, S., Tkaczuk, A., McColloch, N., and 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. https://doi.org/10.5056/jnm21197

2021

Peer-Reviewed Publications: Senior, Corresponding or Second Author

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

  2. Kundu, S., Ming+, J., and Stevens, J. (2021). Dynamic Brain Functional Networks Guided By Anatomical Knowledge. Brain Connectivity*, 11(7):529–542. https://doi.org/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. https://doi.org/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. https://doi.org/10.1080/01621459.2020.1796357

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., McCouch, 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, 15. https://doi.org/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., and 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. https://doi.org/10.1097/QAD.0000000000002788

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

2020

Peer-Reviewed Publications: Senior, Corresponding or Second Author

  1. Solis-Lemus, C.S., Ma, X.+, Hotstetter II, M., Kundu, S., Peng, Q., and Pimental, D. (2020). A Deep Learning Framework for Predicting Functional Markers in Flow Cytometry Data. In Statistical Modeling in Biomedical Research – Contemporary Topics and Voices in the Field* (Springer Nature). https://doi.org/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., and 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. https://doi.org/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). https://doi.org/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., and 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. https://doi.org/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. https://doi.org/10.3174/ajnr.A6589

2019

Peer-Reviewed Publications: Senior, Corresponding or Second Author

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

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

  3. Kundu*, S., and Suthaharan, S. (2019). Privacy-Preserving Predictive Model Using Factor Analysis for Neuroscience Applications. IEEE BigDataSecurity/HPSC/IDS (Washington, DC, USA), 67–73. https://doi.org/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. https://doi.org/10.1128/JCM.01462-19

2018

Peer-Reviewed Publications: Senior, Corresponding or Second Author

  1. Li, Z., Chang, C., Kundu, S., and Long, Q. (2018). Bayesian Generalized Biclustering Analysis via Adaptive Structured Shrinkage. Biostatistics, 21(3):610–624. https://doi.org/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. https://doi.org/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. https://doi.org/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*, 183:635–649. https://doi.org/10.1016/j.neuroimage.2018.07.045

  5. Kundu, S., Cheng, Y., Shin, M., Manyam, G., Mallick, B.K., and Baladandayuthapani, V. (2018). Bayesian Variable Selection with Structure Learning: Applications to Integrative Genomics. PLOS ONE*, 13(7):e0195070. https://doi.org/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. https://doi.org/10.1214/17-BA1086

Peer-Reviewed Publications: Collaborative

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

2016

Peer-Reviewed Publications: Senior, Corresponding or Second Author

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

Peer-Reviewed Publications: Collaborative

  1. Chokshi, F.H., Kang, J., Kundu, S., and 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. https://doi.org/10.1067/j.cpradiol.2016.03.002

2014

Peer-Reviewed Publications: Senior, Corresponding or Second Author

  1. Kundu, S., and Dunson, D. (2014). Bayesian Variable Selection in Semi-parametric Linear Models. Journal of the American Statistical Association*, 109:437–447. https://doi.org/10.1080/01621459.2014.881153

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

2013

Peer-Reviewed Publications: Senior, Corresponding or Second Author

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