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V. Baladandayuthapani

VEERABHADRAN (VEERA) BALADANDAYUTHAPANI, PH.D.

CONTACT INFORMATION

Department of Biostatistics

Room 4208,

1415 Washington Heights Ann Arbor, Michigan 48109

734-764-5702 [phone]

734-763-2215 [fax]

veerab@umich.edu 

Website: bayesrx.com

GitHub: bayesrx

PROFESSIONAL APPOINTMENTS AND ACADEMIC EXPERIENCE

2025+         Jeremy M.G. Taylor Collegiate Professor

2024+        Chair

        Department of Biostatistics, School of Public Health

2018+        Professor (tenured)

Department of Biostatistics, School of Public Health (primary)

Gilbert S. Omenn Department of Computational Medicine and Bioinformatics (joint)

2024+                                  Associate Director, Quantitative Data  Sciences

2020+                                  Director, Cancer Data Science Shared Resource

  Rogel Cancer Center

University of Michigan Medical School | Rogel Cancer Center

2017 – 2018        Professor (tenured)

2012 – 2017        Associate Professor (tenured) and Institute Faculty Scholar (2014-2017)

2005 – 2012        Assistant Professor (tenure-track)

Department of Biostatistics

The University of Texas M. D. Anderson Cancer Center, Houston, TX

2005 – 2018        Assistant/Associate/Full Professor (adjunct)

Department of Statistics, Texas A&M and Rice University, TX

Department of Biostatistics, UT School of Public Health, Houston, TX

2000 – 2005        Instructor, Graduate Research Assistant and Predoctoral trainee,

Training Program in Bioinformatics, Dept. of Statistics, Texas A&M University

 EDUCATION

2005        Ph.D., Statistics, Dept. of Statistics, Texas A&M University

2000        M.A., Statistics, Dept. of Biostatistics, University of Rochester, Rochester, NY

1999        B.Sc. (Honors), Mathematics, Indian Institute of Technology (IIT), Kharagpur, India

VISITING/OTHER APPOINTMENTS

2010        Visiting Assistant Professor

School of Mathematics, Statistics & Actuarial Science University of Kent, Canterbury, UK

Department of Statistics and Oxford Centre for Gene Function University of Oxford, Oxford, UK

RESEARCH INTERESTS

THEORY AND METHODS: Bayes; big data, health data science, functional data, graphical models, integrative           modeling, machine learning, nonlinear/nonparametric models, spatial data, statistical computing

APPLICATIONS: Cancer, spatial biology, high-throughput genomics, epigenomics, transcriptomics and proteomics, high-resolution neuro- and cancer- imaging, clinical trials, precision medicine

HONORS AND AWARDS        

Jeremy M. G. Taylor Collegiate Professorship, University of Michigan, 2025

     Andrei Yakovlev Colloquium Speaker, University of Rochester, 2025

Fellow,  American Association for Advancement in Science, 2022

Young Alumni Achiever Award, Indian Institute of Technology,  Kharagpur, 2020

Myrto Lefkopoulou Distinguished Lectureship, Harvard School of Public Health, 2019

H. O. Hartley Award, Dept. of Statistics, Texas A&M University, 2018 

Annual Theodore G. Ostrom Lecturer, Washington State University, 2018

Fellow, American Statistical Association, 2016

Elected Member, International Statistical Institute, 2016

Faculty Scholar Award, UT MD Anderson Cancer Center, 2014

Keynote Speaker, Genome Engineering for Cancer Treatment, Canberra, Australia, 2017

Young Researcher Award, International Indian Statistical Association, 2015

Highlighted newsworthy oral presentation at the Radiological Society of North America Annual Meetings, 2015

Selected participant, SAMSI Innovations Lab in Big Data and Precision Medicine, 2015

Biometrics Editor’s Invited Paper, Joint Statistical Meetings, 2007

New Researchers Presentation, Ninth Case Studies in Bayesian Analysis Meeting, 2007

Best Graduate Student Presentation, Student Research Week, Texas A&M University, 2004

AUF Graduate Endowment Fellowship, Texas A&M University, 2003

Emanuel Parzen Graduate Research Fellowship Award, Texas A&M University, 2003.

R. L. Anderson Best Student Paper Award, SRCOS, 2003

Best Student Poster, Conference of Texas Statisticians, Texas A&M University, 2003

SPES Student Scholarship, Joint Statistical Meetings, Atlanta, 2001

Dean’s Graduate Merit Scholarship, Texas A&M University, 2000

Mobile Aggie Merit Fellowship, Texas A&M University, 2000

Graduate Fellowship, University of Rochester, 1999

PROFESSIONAL MEMBERSHIPS

Fellow, American Association for the Advancement of Science (AAAS)

Fellow, American Statistical Association (ASA)

Elected Member, International Statistical Institute (ISI)

Institute of Mathematical Statistics (IMS)

International Biometric Society Eastern North American Region (ENAR) International Society for Bayesian Analysis (ISBA)

International Indian Statistical Association (IISA)

BOOKS

  1. Mallick, B. K., Gold, D. L. and Baladandayuthapani, V. (2009). Bayesian Methods for Gene Expression Data. Wiley, U.K.

PUBLICATIONS

* = student/postdoctoral trainee

  1. Baladandayuthapani, V., Mallick, B. K., and Carroll, R.J. (2005). Spatially adaptive Bayesian penalized regression splines (p-splines). Journal of Computational and Graphical Statistics, 14, 378-394. https://doi.org/10.1198/106186005X47345

  1. Phadke, A. P., de la Concha-Bermejillo, A., Wolf, A. M., Andersen, P. R., Baladandayuthapani, V., and Collison, E. W. (2006). Pathogenesis of a Texas feline immunodeficiency virus isolate: an emerging subtype of clade b, Veterinary Microbiology, 115, 64-76. https://doi.org/10.1016/j.vetmic.2006.02.012

  1. Nehete, P. N., Nehete, B. P., Hill, L., Manuri, P. R., Baladandayuthapani, V. , Feng, L., Simmons, J., and Sastry, K. J. (2007) Selective induction of cell-mediated immunity by prophylactic vaccination with a conserved HIV-1 envelope peptide-cocktail for protection of rhesus macaques from chronic SHIVKU2 infection. Virology, 370(1):130-41. https://doi.org/10.1016/j.virol.2007.08.022

  1. Aggrawal, B. B., Sethi, G., Baladandayuthapani, V., Krishnan, S., and Sishodia, S. Targeting cell signaling pathways for drug discovery: an old lock needs a new key (2007). Journal of Cellular Biochemistry, 102(3):580-92. https://doi.org/10.1002/jcb.21500

  1. Baladandayuthapani, V., Mallick, B. K., Hong, M. Y., Lupton, J. R., Turner, N. D. and Carroll, R. J. (2008). Bayesian hierarchical spatially correlated functional data analysis with application to colon carcinogenesis. Biometrics. 64, 64-73 [Biometrics Editor’s Invited Paper for JSM 2007].  https://doi.org/10.1111/j.1541-0420.2007.00846.x

  1. Hong, M. Y., Baladandayuthapani, V., Li, Y., Carroll, R. J., Turner, N. D., and Lupton, J. R. (2008) Coordinated p27 Kip1 expression as a function of distance between crypts - potential inter-crypt signaling. The FASEB Journal, 22:865.4.

  1. Zhang, N., Ge, G., Meye, R., Sethi, S., Basu, D., Pradhan, S., Zhao, Y.J., Li, X.N., Cai, W. W., El-Naggar, K. A., Baladandayuthapani, V., Kittrell, F. S., Rao, P. H., Medina, D., and Pati, D. (2008).  Overexpression  of Separase induces aneuploidy and mammary tumorigenesis. Proceedings of National Academy of Sciences, USA 105(35):13033-8. https://doi.org/10.1073/pnas.080161010

  1. Nanda, U., Eisen, S. J., and Baladandayuthapani, V. (2008) Undertaking an art-survey to compare patient vs. student art preferences. Journal of Environment Behavior, 40, 269-301. https://doi.org/10.1177/0013916507311

  1. Wang J., Xu, J., and Baladandayuthapani, V. (2009) Contrast sensitivity of digital imaging display systems: contrast threshold dependency on object type and implications for monitor quality assurance and quality control in PACS. Medical Physics, 36(8) 3682-3292. https://doi.org/10.1118/1.3173816

  1. Zhou L., Huang J. Z., Martinez J. G., Maity A., Baladandayuthapani, V. and Carroll R. J. (2010) Reduced rank mixed effects models for spatially correlated hierarchical functional data. Journal of the American Statistical Association, 105 (49) 390-400. https://doi.org/10.1198/jasa.2010.tm08737

  1. Baladandayuthapani, V., Ji. Y., Talluri, R., Nieto-Barajas, L. E. and Morris J. S. (2010) Bayesian Random Segmentation Models to Identify Shared Copy Number Aberrations for Array CGH Data. Journal of American Statistical Association 105(492): 1358-1375. https://doi.org/10.1198/jasa.2010.ap09250

  1. Morris J. S., Baladandayuthapani, V., Herrick R. C. and Gutstein H. Automated functional mixed models and isomorphic basis-space modeling, with application to proteomics data. Annals of Applied Statistics 5(2A):894-923. https://doi.org/10.1214/10-AOAS407

  1. Lin, Y.X., Baladandayuthapani, V., Bonato, V., and Do, K.A. (2010) Estimating shared copy number aberrations for array CGH data: the linear-median method. Cancer Informatics, 9 229-249. https://doi.org/10.4137/CIN.S5614

  1. Navas, M.*, Ordonez C., and Baladandayuthapani, V.. (2010) On the computation of stochastic search variable selection in linear regression with UDFs. Proc. IEEE ICDM Conference, p. 941-946, 2010. 941-946. https://doi.org/10.1109/ICDM.2010.79 

  1. Navas, M.*, Ordonez C. and Baladandayuthapani, V. (2011) Fast PCA and Bayesian variable selection for large data sets based on SQL and UDFs. Proc. ACM KDD Workshop on Large-scale Data Mining: Theory and Applications (LDMTA, KDD Conference Workshop)

  1. Bonato V.*, Baladandayuthapani, V., Broom, B. M., Sulman E. P., Aldape K. D and Do, K.A. (2010)

Bayesian ensemble methods for survival prediction in gene expression data. Bioinformatics, 27(3):359-67. https://doi.org/10.1093/bioinformatics/btq660 

  1. Jones, R. J., Baladandayuthapani, V., Neelapu, S., Sharma, R. ,Fayad, L., Romaguera, J. E., Wang, M., Yang, D. and Orlowski, R. Z. (2011) HDM-2 inhibition suppresses expression of ribonucleotide reductase subunit M2, and synergistically enhances gemcitabine-induced cytotoxicity in mantle cell lymphoma. Blood, 118(15): 4140-9. https://doi.org/10.1182/blood-2011-03-340323 

  1. Thompson, P. A., Brewster A., Broom B., Do, K. A., Baladandayuthapani, V.,  Edgerton, M.,  Hahn, K., Murray, J., Sahin, A., Tsavachidis, S., Wang, Y., Zhang, L., Hortobagyi, G., Mills, G. and Bondy, M. (2011) Selective genomic copy number imbalances and probability of recurrence in early- stage breast cancer. PLos One, 6(8): e23543. https://doi.org/10.1371/journal.pone.0023543 

  1. Zheng, Y., Yang, J., Qian, J., Zhang, L., Lu, Y., Li, H., Lan, Y., Liu, Z., He, J., Hong, S., Thomas, S., Shah, J., Baladandayuthapani, V., Kwak, L. W., and Yi. Q. (2012) Novel phosphatidylinositol 3-kinase inhibitor NVP-BKM120 induces apoptosis in myeloma cells and shows synergistic anti-myeloma activity with dexamethasone. Journal of Molecular Medicine, 90(6): 695-706. https://doi.org/10.1007/s00109-011-0849-9 

  1. Hrafnkelsson, B., Morris J. S., and Baladandayuthapani, V.. Spatial modeling of annual minimum and maximum temparture in Iceland. Meteorology and Atmospheric Physics, 116: 43-61.

  1. Garrett, C. R., Hassabo, M. H., Bhadkamkar, N. A., Wen, S., Baladandayuthapani, V., Kee, K. K., and Hassan, M. H. (2012) Survival advantage observed with the use of metformin in patients with type II diabetes and colorectal cancer. British Journal of Cancer, 106(8): 1374-1378. https://doi.org/10.1038/bjc.2012.71 

  1. Garrett, C.R., George, B., Viswanathan, C., Bhadkamkar, N.A., Wen, S., Baladandayuthapani, V., You, Y.N., Kopetz, E.S., Overman, M.J., Kee, B.K., and Eng, C. (2012) Survival benefit associated with surgical  oophorectomy in patients with colorectal cancer metastatic to the ovary. Clinical Colorectal Cancer, 11(3) 191-4. https://doi.org/10.1016/j.clcc.2011.12.003 

  1. Prasad, S., Yadav, V.R., Sung, B., Reuter, S., Kannappan, R., Deorukhkar, A., Diagaradjane, P., Wei, C., Baladandayuthapani, V.,  Krishnan, S., Guha, S., and Aggarwal, B.B. (2012) Ursolic acid inhibits growth and metastasis of human colorectal cancer in an orthotopic nude mouse model by targeting multiple cell signaling pathways: chemosensitization with capecitabine. Clinical Cancer Research, 18(18): 4942-53. https://doi.org/10.1158/1078-0432.ccr-11-2805 

  1. Jones, R.J., Bjorklund, C.C., Baladandayuthapani, V., Kuhn, D.J. and Orlowski, R.Z. (2012) Drug resistance to inhibitors of the human double minute-2 E3 ligase is mediated by point mutations of p53, but can be overcome with the p53 targeting agent RITA. Molecular Cancer Therapy, 11(10): 2243-53. https://doi.org/10.1158/1535-7163.mct-12-0135 

  1. Kuhn, D.J., Berkova, Z., Jones, R.J., Woessner, R., Bjorklund, C.C., Ma, W., Davis, R.E., Lin, P., Wang, H., Madden, T.L., Wei, C., Baladandayuthapani, V., Wang, M., Thomas, S.K., Shah, J.J., Weber, D.M. and Orlowski, R.Z. (2012) Targeting the insulin-like growth factor-1 receptor to overcome bortezomib resistance in preclinical models of multiple myeloma. Blood, 120(16): 3260-3270. https://doi.org/10.1182/blood-2011-10-386789 

  1. Garcia-Alvarado, C.*, Ordonez, C., and Baladandayuthapani, V. (2012) Querying external source code files of programs connecting to a relational database. Proc. ACM Ph.D. Workshop on Information and Knowledge Management (PIKM, CIKM Conference Workshop).

  1. Jennings, E.*, Morris, J.S., Carroll, R., Manyam G, and Baladandayuthapani, V. (2012) Hierarchical Bayesian methods for integration of various types of genomics data. IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS. https://doi.org/10.1109/GENSIPS.2012.6507713 

  1. Gregory, K.*, Coombes, K.R.C, Momin, A, Girard, L., Byers, L., Lin, S., Peyton, M., Heymach, J., Minna, J., and Baladandayuthapani, V. (2012) Latent feature decompositions for integrative analysis of diverse high- throughput genomic data. IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS.

  1. Srivastava, S.*, Wang, W., Zinn, P., Colen R., and Baladandayuthapani, V.. (2012) Multi-platform genomic data using hierarchical Bayesian relevance vector machines IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS.

  1. Lu, C., Stewart, D.J., Lee, J.J., Ji, L., Ramesh, R., Jayachandran, G., Nunez, M.I., Wistuba, I.I., Erasmus, J.J., Hicks, M.E., Grimm, E.A., Reuben, J.M., Baladandayuthapani, V., Templeton, N.S., McMannis, J.D., and Roth, J.A. (2012) Phase I clinical trial of systemically administered TUSC2(FUS1)-nanoparticles mediating functional gene transfer in humans. PLoS One, 7(4): e34833. https://doi.org/10.1371/journal.pone.0034833 

  1. Wang, W.*, Baladandayuthapani, V.**, Morris, J.S., Broom, B.M., Manyam, G., and Do, K.A. (2012) iBAG: integrative Bayesian analysis of high-dimensional multiplatform genomics data. Bioinformatics, 29(2): 149-159. https://doi.org/10.1093/bioinformatics/bts655 

  1. Rawal, S., Chu, F., Zhang, M., Park, H.J., Nattamai, D., Kannan, S., Sharma, R., Delgado, D., Chou, T., Lin, H.Y., Baladandayuthapani, V... Luong, A., Vega, F., Fowler, N, Dong C., Davis, R.E. and Neelapu, S.S. (2013) Cross talk between follicular Th cells and tumor cells in human follicular lymphoma promotes immune evasion in the tumor microenvironment. Journal of Immunology, 190(12): 6681-93. https://doi.org/10.4049/jimmunol.1201363 

  1. Ordonez, C., Garcia, J.,  Garcia-Alvarado, C., Cabrera, W., Baladandayuthapani, V., and Quraishi, Q. (2013) Data mining algorithms as a service in the cloud exploiting relational database systems Proc. ACM SIGMOD Conference.

  1. Pemmaraju, N., Tanaka, M.F., Ravandi, F., Lin, H., Baladandayuthapani, V.,  Rondon, G., Giralt, S.A., Chen, J., Pierce, S., Cortes, J., Kantarjian, H., Champlin, R.E., De Lima, M., and Qazilbash, M.H. (2013) Outcomes in patients with relapsed or refractory acute promyelocytic leukemia treated with or without autologous or allogeneic hematopoietic stem cell transplantation. Clinical Lymphoma, Myeloma and Leukemia 13(4):485-92. https://doi.org/10.1016/j.clml.2013.02.023 

  1. Matusevich, D.*, Ordonez, C., and Baladandayuthapani, V.. (2013) A fast convergence clustering algorithm merging MCMC and EM methods. Proc. ACM CIKM Conference.

  1. Cabrera, W.*, Ordonez, C., Matusevich, D. S., and Baladandayuthapani, V. (2013) Bayesian variable selection for linear regression in high dimensional microarray data. Proc. ACM DTMBIO Workshop (CIKM Conference Workshop).

  1. Olivares, R.*, Rao, A,, Rao, G., Morris, J. S. and Baladandayuthapani, V. (2013) Integrative analysis of multi-modal correlated imaging-genomics data in glioblastoma. IEEE Genomic Signal Processing and Statistics (GENSIPS). https://doi.org/10.1109/GENSIPS.2013.6735914 

  1. Bhadra, A. and Baladandayuthapani, V. (2013) Integrative sparse Bayesian analysis of high-dimensional multi-platform genomic data in glioblastoma. IEEE Genomic Signal Processing and Statistics. https://doi.org/10.1109/GENSIPS.2013.6735913 

  1. Jennings, E.M.*, Morris, J.S., Carroll, R.J., Manyam ,G.C., and Baladandayuthapani, V. (2013) Bayesian methods for expression-based integration of various types of genomics data. EURASIP Journal on Bioinformatics and Systems Biology, 2013(1): 13. https://doi.org/10.1186/1687-4153-2013-13 

  1. Srivastava, S.*, Wang, W., Manyam, G., Ordonez, C., and Baladandayuthapani, V. (2013) Integrating multi- platform genomic data using hierarchical Bayesian relevance vector machines. EURASIP Journal on Bioinformatics and Systems Biology, 2013(1): 9. https://doi.org/10.1186/1687-4153-2013-9 

  1. Wang, W.*, Baladandayuthapani, V.**, Holmes, C.C., and Do, K.A. (2013) Integrative network-based Bayesian analysis of diverse genomics data. BMC Bioinformatics, 14 Suppl 13:S8. https://doi.org/10.1186/1471-2105-14-s13-s8 

  1. Phillip, C.J., Zaman, S., Shentu, S., Balakrishnan, K., Zhang, J., Baladandayuthapani, V., Taverna, P., Redkar, S., Wang, M., Stellrecht, C.M., and Gandhi, V. (2013) Targeting MET kinase with the small-molecule inhibitor amuvatinib induces cytotoxicity in primary myeloma cells and cell lines. Journal of Hematology and Oncology, Dec 10, 6:92. https://doi.org/10.1186/1756-8722-6-92 

  1. Talluri, R.*, Baladandayuthapani, V., and Mallick, B.K. (2014) Bayesian sparse graphical models and their mixtures. STAT, 3(1): 109-125. https://doi.org/10.1002/sta4.49 

  1. Bailey, A.M., Zhan, L., Maru, D., Shureiqi, I., Pickering, C.R., Kiriakova, G., Izzo, J., He, N., Wei, C., Baladandayuthapani, V., Liang, H., Kopetz, S., Powis, G., and Guo, G.L. (2014) FXR silencing in human colon cancer by DNA methylation and KRAS signaling. American Journal of Physiology, Gastrointestinal and Liver Physiology, 306(1): G48-58. https://doi.org/10.1152/ajpgi.00234.2013 

  1. Westin, J.R., Chu, F., Zhang, M., Fayad, L.E., Kwak L.W., Fowler, N., Romaguera, J., Hagemeister, F., Fanale, M., Samaniego, F., Feng, L., Baladandayuthapani, V., Wang, Z., Ma, W., Gao, Y., Wallace, M., Vence, L.M., Radvanyi, L., Muzzafar, T., Rotem-Yehudar, R., Davis, R.E., and Neelapu, S.S. (2014) Safety and activity of PD1 blockade by pidilizumab in combination with rituximab in patients with relapsed follicular lymphoma: a single group, open-label, phase 2 trial. Lancet Oncology, 15(1): 69-77. https://doi.org/10.1016/s1470-2045(13)70551-5 

  1. Ajani, J.A., Wang, X., Song, S., Suzuki, A., Taketa, T., Sudo, K., Wadhwa, R., Hofstetter, W.L., Komaki, R., Maru, D.M., Lee, J.H., Bhutani, M.S., Weston, B., Baladandayuthapani, V., Yao, Y.,  Honjo, S., Scott, A.W., Skinner,  H.D., Johnson, R.L., and Berry, D. (2013) ALDH-1 expression levels predict response or resistance to preoperative chemoradiation in resectable esophageal cancer patients. Molecular Oncology, 8(1): 142-9. https://doi.org/10.1016/j.molonc.2013.10.007 

  1. Bjorklund C.C., Baladandayuthapani, V., Lin, H.Y., Jones, R.J., Kuiatse, I., Wang, H., Yang, J., Shah, J.J., Thomas, S.K., Wang, M., Weber, D.M., and Orlowski, R.Z. (2013) Evidence of a role for CD44 and cell adhesion in mediating resistance to lenalidomide in multiple myeloma: therapeutic implications. Leukemia, 28: 373-383. https://doi.org/10.1038/leu.2013.174 

  1. Zhang, L.*, Baladandayuthapani, V.**, Mallick, B. K., Thompson, P. A., Bond, M. L., and Do, K. A. (2014) Bayesian hierarchical structured variable selection methods with application to MIP studies in breast cancer. Journal of Royal Statistical Society, Series C, 63(4): 595-620. https://doi.org/10.1111/rssc.12053 [Winner of SBSS best student paper award]

  1. Gregory, K.*, Coombes, K.R.C, Momin, A. and Baladandayuthapani, V.(2014) Latent feature decompositions for integrative analysis of diverse high-throughput genomic data. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 11(6):984-94. https://doi.org/10.1109/GENSIPS.2012.6507746 

  1. El-Mabhouh, A.A., Ayres, M.L., Shpall, E.J., Baladandayuthapani, V., Keating, M.J., Wierda,W.G., Gandhi, V. (2014) Evaluation of bendamustine in combination with fludarabine in primary chronic lymphocytic leukemia cells. Blood, 123(23): 3780-9. https://doi.org/10.1182/blood-2013-12-541433 

  1.  Pemmaraju, N., Shah, D., Kantarjian, H., Orlowski R.Z., Nogueras Gonzlez G.M., Baladandayuthapani, V., Jain, N., Wagner, V., Garcia-Manero, G., Shah, J., Ravandi, F., Pierce, S., Takahashi, K., Daver. N., Nazha, A., Verstovsek, S., Jabbour, E., De Lima, M., Champlin, R., Cortes, J. and Qazilbash, M.H. (2014) Characteristics and outcomes of patients with multiple myeloma who develop therapy-related myelodysplastic syndrome, chronic myelomonocytic leukemia, or acute myeloid leukemia. Clinical Lymphoma, Myeloma and Leukemia, 15(2): 110-4. https://doi.org/10.1016/j.clml.2014.07.001 

  1. Wu, W., Merriman, K., Nabaah, A., Seval, N., Seval, D., Lin, H., Wang, M., Qazilbash, M.H., Baladandayuthapani, V., Berry, D., Orlowski, R.Z., Lee, M.H. and Yeung, S.C. (2014) The association of diabetes and anti-diabetic medications with clinical outcomes in multiple myeloma. British Journal of Cancer, 111(3): 628-36. https://doi.org/10.1038/bjc.2014.307 

  1. Nute, J.L., Le Roux, L., Chandler, A.G., Baladandayuthapani, V., Schellingerhout, D., and Cody, D.D. (2014) Differentiation of low-attenuation intracranial hemorrhage and calcification using dual-energy computed tomography in a phantom system. Investigative Radiology, 50(1): 9-16. https://doi.org/10.1097/rli.0000000000000089 

  1. Fowler, N.H., Davis, R.E., Rawal, S., Nastoupil, L., Hagemeister, F.B., McLaughlin, P., Kwak, L.W., Romaguera, J.E., Fanale, M.A., Fayad, L.E., Westin, J.R., Shah, J., Orlowski, R.Z., Wang, M., Turturro, F., Oki, Y., Claret, L.C., Feng, L., Baladandayuthapani, V., Muzzafar, T., Tsai, K.Y., Samaniego, F., and Neelapu, S.S. (2014). Safety and activity of lenalidomide and rituximab in untreated indolent lymphoma: an open-label, phase 2 trial. Lancet Oncology, 15(12): 1311-8. https://doi.org/10.1016/s1470-2045(14)70455-3 

  1. Voo, K.S., Foglietta, M., Percivalle, E., Chu, F., Nattamai, D., Harline, M., Lee, S.T., Bover, L., Lin, H.Y., Baladandayuthapani, V., Delgado, D., Luong, A., Davis, R.E., Kwak, L.W., Liu, Y.J., and Neelapu, S.S. (2014) Selective targeting of Toll-like receptors and OX40 inhibit regulatory T-cell function in follicular lymphoma. International Journal of Cancer, 135(12): 2834-46. https://doi.org/10.1002/ijc.28937 

  1. Guha S., Ji, Y., and  Baladandayuthapani, V. (2014) Bayesian disease classification using copy number data.

Cancer Informatics, 13(Suppl 2): 83-91. https://doi.org/10.4137/cin.s13785 

  1. Ni, Y.*, Stingo, F.C., and Baladandayuthapani, V. (2014) Integrative Bayesian network analysis of genomic data. Cancer Informatics, 13 (Suppl 2): 39-48. https://doi.org/10.4137/cin.s13786 

  1. Gregory, K.B., Carroll, R.J., Baladandayuthapani, V., and Lahiri, S. (2014) A two-sample test for equality of means in high dimension. Journal of the American Statistical Association Theory and Methods, 110(510): 837-849. https://doi.org/10.1080/01621459.2014.934826 

  1. Zhang, L.*, Morris, J.S., Zhang, J., Orlowski, R. and Baladandayuthapani, V. (2014) Bayesian joint selection of genes and pathways: applications in multiple myeloma genomics. Cancer Informatics, 13(Suppl 2): 113-23. https://doi.org/10.4137/cin.s13787 

  1. Kuiatse, I., Baladandayuthapani, V., Lin, H.Y., Thomas, S.K., Bjorklund, C.C., Weber, D.M., Wang, M., Shah, J.J., Zhang, X., Jones, R.J., Ansell, S.M., Yang, G., Treon, S.P., and Orlowski, R.Z. (2015) Targeting the spleen tyrosine kinase with fostamatinib as a strategy against Waldenstroms macroglobulinemia. Clinical Cancer Research, 21(11): 2538-45. https://doi.org/10.1158/1078-0432.ccr-14-1462 

  1. Baladandayuthapani, V., Talluri, R., Ji, Y., Coombes, K., Hennessy, B., Davies, M., and Mallick B. K. (2015) Bayesian sparse graphical models for classification with application to protein expression data. Annals of Applied Statistics, 8(3): 1443-1468. https://doi.org/10.1214/14-aoas722 

  1. Ordonez, C., Garcia-Alvarado, C., and Baladandayuthapani, V. (2015) Bayesian variable selection in linear regression in one pass for large data sets. ACM Transactions on Knowledge Discovery from Data (TKDD), 9(1): 1-14.

  1. Ha, M.J.*, Baladandayuthapani, V. and Do, K.A. (2015) Prognostic gene signature identification using causal structural learning: applications in kidney cancer. Cancer Informatics, 14 (Suppl 1): 23-35. https://doi.org/10.4137/cin.s14873 

  1. Ni, Y.*, Stingo, S. and Baladandayuthapani, V. (2015) Bayesian non-linear directed acyclic graphical models for gene regulatory networks. Biometrics, 71(3): 585-595. [Winner of Laplace award for best Bayesian paper, JSM 2014]

  1. Nieto-Barajas, L.E., Ji, Y., and Baladandayuthapani, V. (2016) A semiparametric Bayesian model for comparing DNA copy numbers. Brazilian Journal of Probability and Statistics, 30(3): 345-365. http://doi.org/10.1214/15-BJPS283 

  1. Ha, M.J.*, Baladandayuthapani, V.** and Do, K.A. (2015) DINGO: Differential Network Analysis in Genomics. Bioinformatics, 31(21): 3413-20. https://doi.org/10.1093/bioinformatics/btv406 

  1. Wait, J.M., Cody, D., Jones, A.K., Rong, J., Baladandayuthapani, V., and Kappadath, S.C.  (2015) Performance evaluation of material decomposition with rapid-kilovoltage-switching dual-energy CT and implications for assessing bone mineral density. American Journal of Roentgenology, 204(6):1234-41. https://doi.org/10.2214/ajr.14.13093 

  1. Zhang, S., Lu, Z., Mao, W., Ahmed, A.A., Yang, H., Zhou, J., Jennings, N., Rodriguez-Aguayo, C., Lopez-Berestein, G., Miranda, R., Qiao, W., Baladandayuthapani, V,, Li, Z., Sood, A.K., Liu, J., Le, X.F., and Bast, R.C., (2015) CDK5 regulates paclitaxel sensitivity in ovarian cancer cells by modulating AKT activation, p21Cip1- and p27Kip1-mediated G1 cell cycle arrest and apoptosis. PLoS One, 10(7): e0131833. https://doi.org/10.1371/journal.pone.0131833 

  1. Dai, B., Yan, S., Lara-Guerra, H., Kawashima, H., Sakai, R., Jayachandran, G., Majidi, M., Mehran, R., Wang, J., Bekele, B.N., Baladandayuthapani, V., Yoo, S.Y., Wang, Y., Ying,  J., Meng, F., Ji, L., and Roth, J.A. (2015) Exogenous restoration of TUSC2 expression induces responsiveness to erlotinib in wildtype epidermal growth factor receptor (EGFR) lung cancer cells through context specific pathways resulting in enhanced therapeutic efficacy. PLoS One, 10(6): e0123967. https://doi.org/10.1371/journal.pone.0123967 

  1. Sathyan, P., Zinn, P., Marisetty, A., Liu, B,, Kamal, M, Singh, S., Bady, P., Baladandayuthapani. V., and Hegi,, M., and Majumder, S. (2015) Mir-21-sox2 axis delineates glioblastoma subtypes with prognostic impact. Journal of Neuroscience, 35(45): 15097-112. https://doi.org/10.1523/jneurosci.1265-15.2015 

  1. Azadeh, S.*, Hobbs, B., Moeller, F., Nielsen, D., and Baladandayuthapani, V.**. (2015) Integrative Bayesian analysis of neuroimaging-genetic data with application to cocaine dependence. Neuroimage,,125: 813-824. https://doi.org/10.1016/j.neuroimage.2015.10.033 

  1. Roszik, J., Haydu, L., Joon, A., Siroy, A.E., Stingo, F.C., Baladandayuthapani, V., Hess, K.R., Tetzlaff, M., Wargo, J., Chen, K., Forget, M., Haymaker, C.L., Chen, J.Q., Meric-Bernstam, F., Eterovic, A.K., Mills Shaw, K., Mills, G., Gershenwald, J., Hwu, P., Futreal, A.P., Bernatchez, C., Radvanyi, L.G., Lazar, A., Davies, M.A. and Woodman, S.E. (2015). A novel algorithm determines tumor mutation load and predicts immune-therapy clinical outcome using a small set of gene mutations. BMC Medicine, 14(1):168. https://doi.org/10.1186/s12916-016-0705-4 

  1. Azadeh, S.*, Hobbs, B., Moeller, F., Nielsen, D., and Baladandayuthapani, V. (2016) Integrative Bayesian analysis of neuroimaging-genetic data through hierarchical dimension reduction. IEEE International Symposium on Biomedical Imaging, 2016: 824-828. https://doi.org/10.1109/isbi.2016.7493393 

  1. Zhang, X., Baladandayuthapani, V., Lin, H., Mulligan, G.,  Barlogie, B., Davis, R. E., Ma. W. C., Wang, Z., Yang, L., and Orlowski, R. Z. (2016) Tight junction protein 1 modulates proteasome capacity and proteasome inhibitor sensitivity in multiple myeloma through EGFR/JAK1/STAT3 signaling. Cancer Cell, 29(5): 639-652. https://doi.org/10.1016/j.ccell.2016.03.026 
  2. Wang, H., Baladandayuthapani, V., Wang Z., Lin, H., Berkova, Z., Davis R. E., Yang, L. and Orlowski R. Z. (2016) Truncated protein tyrosine phosphatase receptor type O suppresses AKT signaling through IQ motif containing GTPase activating protein 1 and confers sensitivity to bortezomib in multiple myeloma. British Journal of Hematology, 8(69): 113858-11387. https://doi.org/10.18632/oncotarget.23017 
  3. Lee, H.C., Wang, H., Baladandayuthapani, V., Lin, H., He, J., Jones, R. J., Kuiatse, I., Gu, D., Wang, Z., Brien, S. O., Keats, J., Yang, J., Davis,R. E., and Orlowski, R. Z. (2016) RNA polymerase I inhibition with CX-5461 as a novel therapeutic strategy to target c-MYC in multiple myeloma. British Journal of Hematology, 177(1): 80-94. https://doi.org/10.1111/bjh.14525 

  1. Zoh, R, Mallick, B, K, Ivanov, I, Baladandayuthapani, V, Manyam, G., Chapkin, R., Lampe, J., and Carroll, R. J. (2016) PCAN: Probabilistic correlation analysis of two non-normal data sets. Biometrics, 72(4): 1348-1368. https://doi.org/10.1111/biom.12516 

  1. Kim, S.*, Baladandayuthapani, V., and Lee, J. J. (2016) Prediction oriented marker selection (PROMISE)

with application to high dimensional regression. Statistics in Biosciences, 9(1): 217-245. https://doi.org/10.1007/s12561-016-9169-5 

  1. Saha, A.*, Banerjee, S., Narang, S., Rao, G., Martinez, J., Rao, A.U.K., and Baladandayuthapani, V. (2016) DEMARCATE: Density-based magnetic resonance image clustering for assessing tumor heterogeneity in cancer. Neuroimage: Clinical, 12: 132-43. https://doi.org/10.1016/j.nicl.2016.05.012 

  1. Mikell, J.K., Mahvash, A., Siman, W., Baladandayuthapani, V., Mourtada, F., and Kappadath, S.C. Selective internal radiation therapy with yttrium-90 glass microspheres: biases and uncertainties in absorbed dose calculations between clinical dosimetry models (2016). International Journal of Radiation Oncology, Biology and Physics, 96(4): 888-896. https://doi.org/10.1016/j.ijrobp.2016.07.021 

  1. Xiaobo, C., Majidi, M., Feng, M., Shao, R., Wang, J., Zhao, Y.,  Baladandayuthapani,  V.,  Song,  J., Fang, B.,  Ji, L., Mehran, R., and Roth. J.A. (2016) TUSC2(FUS1)-erlotinib induced vulnerabilities in epidermal growth factor receptor(EGFR) wildtype non-small cell lung cancer(NSCLC) targeted by the repurposed drug auranofin. Scientific Reports, 15(6): 35741. https://doi.org/10.1038/srep35741 

  1. Noren, D., Long, B., Norel, R., Rhissorrakrai, K., Hess, K., Hu, C., Bisberg, A., Schultz, ., Engquist, A., Liu, L., Lin, X., Chen, G., and Xie, H., Hunter, G., Boutros, P., Stepanov, O., DREAM 9 AML-OPC Consortium (includes Baladandayuthapani, V.. ), Norman, T., Friend, S., Stolovitzky, G., Kornblau, S., and Qutub, A. A. (2016) A crowdsourcing approach to developing and assessing prediction algorithms for AML prognosis. PLOS Computational Biology, 12(6): e1004890. https://doi.org/10.1371/journal.pcbi.1004890 

  1. Guha, S. and Baladandayuthapani, V. (2016) Nonparametric variable selection, clustering and prediction for high-dimensional regression. Electronic Journal of Statistics, (10) 3374-3424. https://doi.org/10.48550/arXiv.1407.5472 

  1. Zhang, L.*, Baladandayuthapani, V., Zhu, H., Baggerly, K. A., Majewski, T., Czerniak, B. A., and Morris, J. S. (2016) Functional CAR models for large spatially correlated functional datasets. Journal of the American Statistical Association – Theory and Methods, 111(514) 772-786. https://doi.org/10.1080/01621459.2015.1042581 

  1. Bock, F., Lu, G., Srour, S.A., Gaballa, S., Lin, H.Y., Baladandayuthapani, V., Honhar, M., Stich, M., Shah, N.D., Bashir, Q., Patel, K., Popat, U., Hosing, C., Korbling, M., Delgado, R., Rondon, G., Shah, J.J., Thomas, S.K., Manasanch, E.E., Isermann, B., Orlowski, R.Z., Champlin, R.E., and Qazilbash, M.H. (2016) Outcome of patients with multiple myeloma and CKS1B gene amplification after autologous hematopoietic stem cell transplantation. Biology of Blood and Marrow Transplantation, 22(12): 2159-2164. https://doi.org/10.1016/j.bbmt.2016.09.003 

  1. Baljevic, M, Baladandayuthapani. V Lin, H.Y, Partovi, C.M, Berkova,, Zaman, S, and Gandhi, V. V. and Orlowski, R.Z, (2017) Phase II study of the c-MET inhibitor ARQ 197 (Tivantinib) in patients with relapsed or relapsed/refractory multiple myeloma. Annals of Hematology, 96(6): 977-985. https://doi.org/10.1007/s00277-017-2980-3 

  1. Lin, J.S., Fuentes, D., Chandler, A., Prabhu,, S., Weinberg., J., Baladandayuthapani. V ., Hazle, JD, and Schellingerhout, D. (2017) Performance assessment for brain magnetic resonance imaging registration methods. Investigative Radiology, 38(5) 973-980. https://doi.org/10.3174/ajnr.a5122 

  1. Gentile, E., Xiaobo, C., Majidi, M., Feng, M., Shao, R., Wang, J., Zhao, Y., Baladandayuthapani.  V., Song.  J., Fang, B., Mehran, R., Roth, J.A., and Ji, L. (2017) Cationic liquid crystalline nanoparticles for the delivery of synthetic RNAi-based therapeutics. Oncotarget, 8(29):48222-4823. https://doi.org/10.18632/oncotarget.18421 

  1. Wadhwa, R., Wang,  X., Baladandayuthapani, V., Liu, B., Shiozaki, H., Shimodaira, Y., Lin, Q., Elimova, E., Hofstetter, W.L., Swisher, S.G., Rice, D.C., Maru, D.M., Kalhor, N., …., Berry, D., Song, S., and Ajani, J.A. (2017) Nuclear expression of Gli-1 is predictive of pathologic complete response to chemoradiation in trimodality treated oesophageal cancer patients. British Journal of Cancer, 117(5): 648-655. https://doi.org/10.1038/bjc.2017.225 

  1. Zhang, Y., Linder, M., Shojaie, A., Ouyang, Z., Shen, R., Baggerly, K. A., Baladandayuthapani, V., and Zhao H. (2017) Dissecting pathway disturbances using network topology and multi-platform genomics data. Statistics in Biosciences, (10): 86-106. http://dx.doi.org/10.1007/s12561-017-9193-0 

  1. Yu, K., Zhang, Y., Yuc, Y., Huang, C., Liu, R., Li, T., Yang, T., Morris, J.S., Baladandayuthapani, V.,

and Zhu, H. (2017) Radiomic analysis in prediction of human papilloma virus status. Clinical and Translational Radiation Oncology, (7): 49-54. https://doi.org/10.1016/j.ctro.2017.10.001 

  1. Ye, X., Wang, R., Bhattacharya, R., Boulbes, D., Fan, F., Xia, L., Adoni, H., Ajami, N., Wong, M., Smith, D., Petrosino, J., Venable, S., Qiao, W., Baladandayuthapani. V., Maru, D., and Ellis, L.M. (2017) Fusobacterium nucleatum subspecies animalis influences pro-inflammatory cytokine expression and monocyte activation in human colorectal tumors. Cancer Prevention Research, 10(7): 398-409. https://doi.org/10.1158/1940-6207.capr-16-0178 

  1. Morris, J. S., and Baladandayuthapani, V. (2017) Statistical modeling, structured learning, and integration in bioinformatics. Statistical Modeling, 17(4-5): 245-289. https://doi.org/10.1177/1471082x17698255 Discussion Paper with Rejoinder

  1. Ni, Y.*, Stingo, S., and Baladandayuthapani, V. Sparse multi-dimensional graphical models: a Bayesian unified framework. (2017) Journal of the American Statistical Association – Theory & Methods, 112(518): 779-793. https://doi.org/10.1080/01621459.2016.1167694 

  1. Zhu, B., Song, N., Shen, R., Arora, A., Machiela M., Song, L., Landi, M., Ghosh, D., Chatterjee, N., Baladandayuthapani, V., and Zhao, H. (2017) Integrating clinical and multiple omics data for prognostic assessment across human cancers. Scientific Reports, 7(1): 16954. https://doi.org/10.1038/s41598-017-17031-8 

  1. Bharath K, Kambadur, P., Dey D., Rao. A, and Baladandayuthapani, V.. Statistical tests for large tree-structured data (2017). Journal of the American Statistical Association – Theory & Methods, 112(520): 1733-1743. https://doi.org/10.1080/01621459.2016.1240081 

  1. Shoemaker, K., Hobbs, B.P., Bharath, K., Ng, C.S., and Baladandayuthapani, V. (2018) Tree-based methods for characterizing tumor density heterogeneity. Pacific Symposium of Biocomputing, 23: 216-227.
  2. Class, C.A, Ha, M.J., Baladandayuthapani, V., and Do, K.A. (2018) iDINGO - Integrative differential network analysis in genomics with shiny application. Bioinformatics, 34(7): 1243-1245. https://doi.org/10.1093/bioinformatics/btx750 

  1. Kundu, S.*, Cheng, Y., Shin, M., Manyam G., Mallick, B., and Baladandayuthapani, V. (2018) Bayesian variable selection with structure learning: applications in integrative genomics. Plos One, 13(7). https://doi.org/10.1371/journal.pone.0195070 

  1. Bhadra, A., Rao A., and Baladandayuthapani, V. (2018) Inferring network structure in non-normal and mixed discrete-continuous genomic data. Biometrics, 74(1): 185-195. https://doi.org/10.1111/biom.12711 

  1. Lee, W., Miranda, M., Rauch, P., Baladandayuthapani, V., Fazio, M., Downs, C., and Morris, J. S. (2018) Semipara- metric functional mixed models for longitudinal functional data with application to glaucoma data. Journal of the American Statistical Association – Applications & Case Studies, 114(526): 495-513. https://doi.org/10.1080/01621459.2018.1476242 

  1. Kappadath, S. C., Mikell, J., Balagopal, A., Baladandayuthapani, V., and Mahvash, A. (2018). Hepato- cellular carcinoma tumor dose         response after 90Y-radioembolization with glass microspheres using 90Y-SPECT/CT-based voxel dosimetry. International Journal of Radiation Oncology Biology Physics, 102(2): 451-461. https://doi.org/10.1016/j.ijrobp.2018.05.062 

  1. Zhang,  X., Lee,  H. C.,  Shirazi, F.,  Baladandayuthapani, V., and Orlowski, R. Z. (2018). Protein targeting chimeric molecules specific for bromodomain and extra-terminal motif family proteins are active against  pre-clinical  models of multiple myeloma. Leukemia, 32:2224-2. https://doi.org/10.1038/s41375-018-0044-x 

  1. Ruder, D., Papadimitrakopoulou, V., Shien, K., Behrens, C., Baladandayuthapani, V., and Izzo, J. G. (2018). Concomitant targeting of the mTOR/MAPK pathways: novel therapeutic strategy in subsets of RICTOR/KRAS-altered non-small cell lung cancer. Oncotarget,  9(74): 33995-34008. https://doi.org/10.18632/oncotarget.26129 

  1. Zinn, P. O., Singh, S. K., Kotrotsou, A., Hassan, I., Baladandayuthapani, V., and Colen, R. R. (2018). 100 toward the co-clinical glioblastoma treatment paradigm radiomic machine learning identifies glioblastoma gene expression in patients and corresponding xenograft tumor models. Neurosurgery, 65(Suppl 1): 80. https://doi.org/10.1093/neuros/nyy303.100 

  1. Elhalawani, H., Lin, T. A., Volpe, S., Mohamed, A.S.R., White, A.L., Zafereo, J., Wong, A.J., Berends, J.E., AboHashem, S., Williams, B., Aymard, J.M., … Zhang, Y., Zhu, H., Morris, J.S.,  Baladandayuthapani, V., Shumway, J.W., Ghosh, A., Pohlman, A., Phoulady, H.A., Goyal, V., Canahuate, G., Marai, G.E., Vock, D., Lai, S.Y., Mackin, D.S., Court, L.E., Freymann, J., Farahani, K., Kaplathy-Cramer, J., and Fuller, C. D. (2018). Machine learning applications in head and neck radiation oncology: lessons from open-source radiomics challenges. Frontiers in Oncology, 8:294. https://doi.org/10.3389/fonc.2018.00294 

  1. Meraz, I. M., Majidi, M., Cao, X., Lin, H., Baladandayuthapani, V., and Roth, J. A. (2018). TUSC2 immunogene therapy synergizes with anti-PD-1 through enhanced proliferation and infiltration of natural killer cells in syngeneic kras-mutant mouse lung cancer models. Cancer Immunology Research, 6(2): 163-177. https://doi.org/10.1158/2326-6066.cir-17-0273 

  1. Davenport, C. A., Maity, A., and Baladandayuthapani, V. (2018). Functional interaction–based nonlinear models with application to multiplatform genomics data. Statistics in Medicine, 37(18): 2715-2733. https://doi.org/10.1002/sim.7671 

  1. Ni, Y.*, Stingo, S. and Baladandayuthapani, V. (2018) Bayesian graphical regression. Journal of the American Statistical Association – Theory & Methods, 114(525): 184-197. https://doi.org/10.1080/01621459.2017.1389739 

  1. Ha, M., Banerjee, S., Akbani, R., Liang, H., Mills, G., Do, K.A., and Baladandayuthapani, V. (2018) Personalized integrated network modeling of the cancer proteome atlas. Nature Scientific Reports, 8(1): 14924. https://doi.org/10.1038/s41598-018-32682-x 

  1. Zinn, P. O., Singh, S., Kotrotsou, A., Hassan, I., Baladandayuthapani, V., and Colen, R. R. (2018).  A coclinical radiogenomic validation study: conserved magnetic resonance radiomic appearance of periostin-expressing glioblastoma in patients and xenograft models. Clinical cancer research: an official journal of the American Association for Cancer Research, 24(24): 6288-6299. https://doi.org/10.1158/1078-0432.ccr-17-3420 

  1. Ni, H., Shirazi, F., Baladandayuthapani, V., Lin, H., and Orlowski, R. Z. (2018). Targeting myddosome signaling in Waldenstrom’s macroglobulinemia with the interleukin-1 receptor- associated kinase 1/4 inhibitor R191. Clinical cancer research: an official journal of the American Association for Cancer Research, 24(24): 6408-6420. https://doi.org/10.1158/1078-0432.ccr-17-3265 

  1. Bharath, K., Kurtek, S., Rao, A., and Baladandayuthapani, V. (2018). Radiologic image-based statistical shape analysis of brain tumours. Journal of the Royal Statistical Society. Series C: Applied Statistics, 67(5): 1357-1378. https://doi.org/10.1111/rssc.12272 

  1. Kundu, S.*, Baladandayuthapani, V., and Mallick, B. K. (2019) Bayes regularized graphical model estimation in high dimensions, Bayesian Analyses, arXiv preprint arXiv:1308.3915. https://doi.org/10.1093/biostatistics/kxj008 

  1. Marisetty, A. L., Lu, L., Veo, B. L., Liu, B, Baladandayuthapani, V., and Majumder, S. (2019). REST-DRD2 mechanism impacts glioblastoma stem cell-mediated tumorigenesis. Neuro-oncology, 21(6): 775-785. https://doi.org/10.1093/neuonc/noz030 

  1. Ye, J. C.,  Chen, L.,  Chen, J.,  Parkin, B.,  Polk, A.,  Kandarpa, M.,  Cole, C. E.,  Campagnaro, E. L., Robinson, D., Wu, Y.-M., Talpaz M., Yesil J., Leif, P., Chinnaiyan, A., Baladandayuthapani V. (2019) Aneuploidy is associated with inferior survival in relapsed refractory multiple myeloma patients. Blood, 134(1):4360. https://doi.org/10.1182/blood-2019-124135 

  1. Banerjee, S.*, Akbani, R., and Baladandayuthapani, V. (2019). Spectral clustering via sparse graph structure learning with application to proteomic signaling networks in cancer. Computational Statistics and Data Analysis, 132:46-69. https://doi.org/10.1016/j.csda.2018.08.009 

  1. Gates, E.D.H., Lin, J. S., Weinberg, J. S., Hamilton, J., Baladandayuthapani, V., and Schellingerhout,

        D. (2019). Guiding the first biopsy in glioma patients using estimated Ki-67 maps derived from MRI:

conventional versus advanced imaging. Neuro-oncology, 21(4): 527-536. https://doi.org/10.1093/neuonc/noz004 

  1. Ni, Y.*, Stingo, S., and Baladandayuthapani, V. Bayesian hierarchical varying-sparsity model with application to cancer proteogenomics. (2019) Journal of American Statistical Association – Applications & Case Studies, 114(525): 48-60. https://doi.org/10.1080/01621459.2018.1434529 

  1. Manasanch,E.E., Han, G., Mathur, R., Qing, Y., Zhang, Z., Lee, H., Weber, D.M., Amini, B., Berkova, Z., Eterovic, K., Zhang, S., Zhang, J., Song, X., Mao, X., Morgan, M., Feng, L., Baladandayuthapani, V., Futreal, A., Wang, L., Neelapu, S.S., and Orlowski, R.Z. (2019). A pilot study of pembrolizumab in smoldering myeloma: report of the clinical, immune, and genomic analysis. Blood Advances, 3(18): 2712. https://doi.org/10.1182/bloodadvances.2019000881 

  1. Gates, E.D.H., Lin, J.S., Weinberg, J.S., Prabhu, S.S., Hamilton, J., Hazle, J.D., Fuller, G.N., Baladandayuthapani, V., Fuentes, D.T., and Schellingerhout, D. (2020). Imaging-based algorithm for the local grading of glioma. American Journal of Neuroradiology, 41(3): 400-407. https://doi.org/10.3174/ajnr.a6405 

  1. Reuben, A., Zhang, J., Chiou, S.H., Gittelman, R.M., Li, J., Lee, W.C., Fujimoto, J., Behrens, C., Liu, X., Wang, F., Quek, K., Wang, C., Kheradmand, F., Chen, R., Chow, C.W., Lin, H., Bernatchez, C., Jalali, A., Hu, X., Wu, C.J., Eterovic, A.K., Parra, E.R., Yusko, E., Emerson, R., Benzeno, S., Vignali, M., Wu, X., Ye, Y., Little, L.D., Gumbs, C., Mao, X., Song, X., Tippen, S., Thornton, R.L., Cascone, T., Snyder, A., Wargo, J.A., Herbst, R., Swisher, S., Kadara, H., Moran, C., Kalhor, N., Zhang, J., Scheet, P., Vaporviyan, A.A., Sepesi, B., Gibbons, D.L., Robins, H., Hwu, P., Heymach, J.V., Sharma, P., Allison, J.P., Baladandayuthapani, V., Lee, J.J., Davis, M.M., Wistuba. I.I., Futreal, P.A., and Zhang, J. (2020). Comprehensive T cell repertoire characterization of non-small cell lung cancer. Nature Communications, 11(1):603. https://doi.org/10.1038/s41467-019-14273-0 

  1. Maity, A.K.*, Bhattacharya, A., Mallick, B., and Baladandayuthapani, V (2020). Bayesian data integration and variable selection for pan-cancer survival prediction using protein expression data. Biometrics, 76(1): 316-325. https://doi.org/10.1111/biom.13132 

  1. Yang, H*, Baladandayuthapani. V, Rao. A. and Morris, J.S (2020). Quantile Function on Scalar Regression Analysis for Distributional Data. Journal of American Statistical Association – Applications & Case Studies. 115 (529): 90-106. https://doi.org/10.1080/01621459.2019.1609969 

  1. Liu, Q.*, Ha, M. J., Bhattacharya, R., Garmire, L., and Baladandayuthapani, V (2020). Network-based matching of patients and targeted therapies for precision oncology. Pacific Symposium of Biocomputing 25:623-634

  1. Guha, N., Baladandayuthapani, V. and Mallick, B. K. (2020). Quantile graphical models: Bayesian approaches. Journal of Machine Learning Research 21(79):1−47, 2020.

  1. Das, P.*, Peterson, C., Do, K., Akbani, R. and Baladandayuthapani, V. (2020) NExUS: Bayesian simultaneous network estimation across unequal sample sizes. Bioinformatics, 3 (36), 798-804. https://doi.org/10.1093/bioinformatics/btz636 

  1. Zhang, Y.*, Morris, J. S., Rao, A., and Baladandayuthapani, V. (2020) Radio-iBAG: Radiogenomics - integrative Bayesian analysis of high dimensional multiplatform genomics data. Annals of Applied Statistics, 3(13), 1957-1988. [link]

  1. Ha, M. J.*, Stingo F. C., and Baladandayuthapani, V. (2020) Bayesian structure learning in multilayered genomic networks. Journal of American Statistical Association – Applications & Case Studies, https://doi.org/10.1080/01621459.2020.1775611

  1. Bhattacharyya, R., Ha, M. J., Liu, Q., Akbani, R., Liang, H., Baladandayuthapani, V. (2020) Personalized network modeling of the pan-cancer patient and cell line interactome. Journal of Clinical Oncology - Clinical Cancer Informatics, 4, 399-411. https://doi.org/10.1200/cci.19.00140 

  1. Ray D., Salvatore M., Bhattacharyya R., Wang L., Du J., Mohammed S., Purkayastha S., Halder A., Rix A., Barker D., Kleinsasser M., Zhou Y., Bose D., Song P., Banerjee M., Baladandayuthapani V., Ghosh P., Mukherjee B. (2020) Predictions, role of interventions and effects of a historic national lockdown in India's response to the COVID-19 pandemic: data science call to arms. Harvard Data Science Review. https://doi.org/10.1200/cci.19.00140

  1. Gates E. D. H., Weinberg J. S., Prabhu S. S, Lin J. S., Hamilton J., Hazle J. D., Fuller G. N., Baladandayuthapani V., Fuentes D. T., Schellingerhout D. (2021) Estimating local cellular density in glioma using MR imaging data. American Journal of Neuroradiology, 42(1):102-108. https://doi.org/10.3174/ajnr.a6884 

  1. Tanaka I, Dayde D, Tai MC, Mori H, Solis LM, Tripathi SC, Fahrmann JF, Unver N, Parhy G, Jain R, Parra ER, Murakami Y, Aguilar-Bonavides C, Mino B, Celiktas M, Dhillon D, Casabar JP, Nakatochi M, Stingo F, Baladandayuthapani V, Wang H, Katayama H, Dennison JB, Lorenzi PL, Do KA, Fujimoto J, Behrens C, Ostrin EJ, Rodriguez-Canales J, Hase T, Fukui T, Kajino T, Kato S, Yatabe Y, Hosoda W, Kawaguchi K, Yokoi K, Chen-Yoshikawa TF, Hasegawa Y, Gazdar AF, Wistuba II, Hanash S, Taguchi A (2022) SRGN-triggered aggressive and immunosuppressive phenotype in a subset of TTF-1-negative lung adenocarcinomas. Journal of National Cancer Institute. https://doi.org/10.1093/jnci/djab183

  1. Qazilbash MH, Saini NY, Soung-Chul C, Wang Z, Stadtmauer E, Baladandayuthapani V, Lin H, Tross B, Honhar M, Rao SS, Kim K, Popescu M, Szymura SJ, Zhang T, Anderson AJ, Bashir Q, Shpall EJ, Orlowski RZ, Levine BL, Kerr N, Garfall A, Cohen AD, Vogl DT, Dengel K, June CH, Champlin RE, Kwak LW (2022). A randomized phase II trial of idiotype vaccination and adoptive autologous t-cell transfer in multiple myeloma patients. Blood. https://doi.org/10.1182/blood.2020008493

  1. Mohammed, S.*, Bharath, K., Kurtek, S, Rao, A, and Baladandayuthapani, V. (2021) RADIOHEAD: radiogenomic analysis incorporating tumor heterogeneity in imaging through densities. Annals of Applied Statistics.

  1. Wang, Z., Baladandayuthapani, V, Kaseb, A., Hassan, M. M., Wang, W., Morris J. S. (2022) Bayesian edge regression in undirected graphical models to characterize interpatient heterogeneity in cancer. JASA– Applications & Case Studies doi.org/10.1080/01621459.2021.2000866

  1. Saha, A.*, Ha, M., Baladandayuthapani, V. (2022). A Bayesian framework for calibrating individualized therapeutic index in pharmacogenomic studies. Annals of Applied Statistics 16(4): 2055-2082 . https://doi.org/10.1214/21-AOAS1550 

  1. Ni, Y., Baladandayuthapani, V., Vannucci, M. and Stingo F. C. (2021). Bayesian graphical models for modern biological applications (with discussion). Statistical Methods & Applications.  https://link.springer.com/article/10.1007/s10260-021-00572-8

  1. Ni, Y., Baladandayuthapani, V., Vannucci, M. and Stingo F. C. (2022). Rejoinder to the discussion of “Bayesian graphical models for modern biological applications” Statistical Methods & Applications.  https://link.springer.com/article/10.1007/s10260-022-00634-5

  1. Bhattacharyya, R., Banerjee S., Mohammed S. and Baladandayuthapani, V. (2022+). Spatial network-based modeling of COVID-19 dynamics: early pandemic spread in India. Journal of Indian Statistical Association (in press)

  1. Das, P.**, Peterson, C., Ni. Y., Rueben. A., Zhang. J., Do, K., and Baladandayuthapani, V.(2022+) Bayesian hierarchical quantile regression with application to characterizing the immune architecture of lung cancer.  Biometrics. (in press) https://doi.org/10.1111/biom.13774 

  1. Panigrahi, S., Mohammed, S.**, Rao, A. and Baladandayuthapani, V. (2022+) Integrative Bayesian models using post-selective inference: a case study in radiogenomics. Biometrics (in press). https://doi.org/10.1111/biom.13740 

  1. Yao, T-H*, Wu Z., Bharath, K., Li, J. and Baladandayuthapani, V. (2022+). Probabilistic learning of treatment trees in cancer. Annals of Applied Statistics. (in press)

  1. Acharyya, S.**, Zhou X., and Baladandayuthapani, V. (2022). SpaceX: gene co-expression network estimation for spatial transcriptomics. Bioinformatics  (in press) [Winner of Early Career Paper Award from Biometrics Section of ASA, JSM 2022] https://doi.org/10.1093/bioinformatics/btac645 

  1. Desai, N.*, Morris, J.S., and Baladandayuthapani, V. (2022). NETCELLMATCH: multiscale network-based matching of cancer cell lines to patients using graphical wavelets. Chemistry & Biodiversity https://doi.org/10.1002/cbdv.202200746

  1. Bhattacharyya, R.*, Burman, A., Singh, K., Banerjee, S., Maity, S., Auddy, A., Rout, S.K., Lahoti, S., Panda, R and Baladandayuthapani, V. (2022+). Role of multi-resolution vulnerability indices in COVID-19 spread: a case study in India. BMJ Open https://doi.org/10.1101/2021.07.19.21260791 

  1. Bhattacharyya, R.*, Henderson, N., and Baladandayuthapani, V. (2022+). BaySyn: Bayesian evidence synthesis for multi-system multiomic integration. In Pacific Symposium on Biocomputing, Vol. 28, No. 2023. http://dx.doi.org/10.1136/bmjopen-2021-056292 

  1. Ni, Y., Stingo F. C. and Baladandayuthapani, V. (2022). Bayesian covariate-dependent gaussian graphical models with varying structure. Journal of Machine Learning Research. 23(242):1−29 https://www.jmlr.org/papers/v23/21-0102.html 

 

  1. Morikawa A, Li J, Ulintz P, Cheng X, Apfel A, Robinson D, Hopkins A, Kumar-Sinha C, Wu YM, Serhan H, Verbal K, Thomas D, Hayes DF, Chinnaiyan AM, Baladandayuthapani V, Heth J, Soellner MB, Merajver SD, Merrill N. (2023). Optimizing Precision Medicine for Breast Cancer Brain Metastases with Functional Drug Response Assessment. Cancer Res Commun. 2023 Jun 21;3(6):1093-1103. doi: 10.1158/2767-9764.CRC-22-0492

  1. Chekuo, T., Stingo, F.C., Mohammed S.*, Rao, A., and Baladandayuthapani, V. (2023). A Bayesian group selection with compositional responses for analysis of radiologic tumor proportions and their genomic determinants. Annals of Applied Statistics. 2023 Dec 17(4): 3013-3034. https://doi.org/10.1214/23-aoas1749

  1. Mohammed, S.*, Kurtek, S., Bharath, K., Rao, A., Baladandayuthapani, V. (2023). Tumor radio- genomics with Bayesian layered variable selection. IEEE Transactions in Medical Imaging. 2023 Dec 90(102964). https://doi.org/10.1016/j.media.2023.102964

  1. Osher, N.*, Kang, J., Krishnan, S., Rao, A. and Baladandayuthapani, V. (2023). SPARTIN: a Bayesian Method for the Quantification and Characterization of Cell Type Interactions in Spatial Pathology Data. Frontiers in Genetics. 2023 May 18:14:1175603. https://doi.org/10.3389/fgene.2023.1175603

  1. Masotti M, Osher N*, Eliason J, Rao A, Baladandayuthapani V. (2023). DIMPLE: An R-Package to Quantify, Visualize and Model spatial cellular interactions from Multiplex Imaging with Distance Matrices. Cell Patterns. 2023 Dec 4(12): 100879. https://doi.org/10.1016/j.patter.2023.100879

  1. Gu C., Baladandayuthapani V., and Guha S. (2023)  Nonparametric Bayes Differential Analysis of Multigroup DNA Methylation Data. Bayesian Analysis DOI: 10.1214/23-BA1407

  1. Zakharia Y, Singer E, Acharyya S, Garje R, Joshi M, Peace D, Baladandayuthapani V, Lalancette C, Kryczek I, Zou W, Alva A (2024). Durvalumab and guadecitabine in advanced clear cell renal cell carcinoma: results from the phase Ib/II study BTCRC-GU16-04. Nature Communications. 2024 Feb 15(972). https://doi.org/10.1038/s41467-024-45216-z

  1. Khalatbari S, Baladandayuthapani V, Kaciroti N, Samuels E, Bugden J, Spino C. Developing a Bayesian workshop for full-time staff statisticians. J Clin Transl Sci. 2024 Jun 4;8(1):e105. doi: 10.1017/cts.2024.558. PMID: 39655005; PMCID: PMC11626572.

  1. Whitehead CE, Ziemke EK, Frankowski-McGregor CL, Mumby RA, Chung J, Li J, Osher N, Coker O, Baladandayuthapani V, Kopetz S, Sebolt-Leopold JS. A first-in-class selective inhibitor of EGFR and PI3K offers a single-molecule approach to targeting adaptive resistance. Nat Cancer. 2024 Aug;5(8):1250-1266. doi: 10.1038/s43018-024-00781-6. Epub 2024 Jul 11. PMID: 38992135; PMCID: PMC11357990.

  1. Bhattacharyya, R.*, Henderson, N., and Baladandayuthapani, V (2024). Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Genomic Data. Journal of American Statistical Association (under revision) Journal of the American Statistical Association, 119(548), 2533–2547. https://doi.org/10.1080/01621459.2024.2388909

  1. Chakraborty, M., Baladandayuthapani, V., Bhadra, A. and Ha, M. J. (2024). Bayesian Robust Learning in Chain Graph Models for Integrative Pharmacogenomics. Annals of Applied Statistics 18, 3274–3296. [doi link]

  1. Choi J*, Baladandayuthapani V, and Kang J (2024) Latent spatial dirichlet allocation, NeurIPS BDU Workshop.

  1. Mohammed, S., Masotti, M., Osher, N., Acharyya, S., and Baladandayuthapani, V. (2024). Statistical Analysis of Quantitative Cancer Imaging Data. Statistics and Data Science in Imaging, 1(1). https://doi.org/10.1080/29979676.2024.2405348

  1. Yao TH*, Ni Y, Bhadra A, Kang J, Baladandayuthapani V. (2025) Robust Bayesian graphical regression models for assessing tumor heterogeneity in proteomic networks, Biometrics, In Press.

  1. Liu Q* , Li G., and Baladandayuthapani V (2025+). Pan-cancer drug response prediction using integrative principal component regression. Statistics in Biosciences, In Press.

  1. Desai, N*., Baladandayuthapani, V., Shinohara, R. T., & Morris, J. S. (2025). Covariance Assisted Multivariate Penalized Additive Regression (CoMPAdRe). Journal of Computational and Graphical Statistics, 1–10. https://doi.org/10.1080/10618600.2024.2407453

  1. Desai, N*, Baladandayuthapani V, Shinohara, R; Morris, J. S. (2025+) Connectivity Regression, Biostatistics, In Press

  1. Chen L* , Acharyya S.*, Luo C., Ni Y. and Baladandayuthapani V (2025+). GraphR: Probabilistic Graphical Modeling under Heterogeneity (accepted in Cell Reports Methods)

INVITED EDITORIALS

  1. Jiang, H., An, L., Baladandayuthapani, V., and Auer, P.L. Classification, predictive modelling, and statistical analysis of cancer data (a). (2014) Cancer Informatics, 13(Supple 2): 1-3.

BOOK CHAPTERS

  1. Baladandayuthapani, V., Ray, S., and Mallick, B. K. (2005). Bayesian Methods for DNA Microarray Data Analysis. In Rao C. R. and Dey D. K. (eds.) Handbook in Statistics Bayesian Statistics: Modeling and Computation. Elseiver: Amsterdam.

  1. Baladandayuthapani, V., Holmes, C. C., Mallick, B. K. and Carroll, R. J. (2006). Modeling Nonlinear Gene Interactions using Bayesian MARS. In Do K. A.,  Mu¨eller P. and Vannucci M. (eds.)   Bayesian Inference for Gene Expression and Proteomics. Cambridge University Press.

  1. Rossell, D*, Baladandayuthapani. V, and Johnson, V. E. (2008). Bayes Factors Based on Test Statistics under Order Restrictions. In Hoijtink H., Klugkist I. , Boelen P (eds) Bayesian Evaluation of Informative Hypotheses in Psychology. Springer.

  1. Wang, W*. Baladandayuthapani. V , Holmes, C. C. and Do, K-A. (2013) Bayesian graphical models for integrating multiplatform genomics data. In: Advances in Statistical Bioinformatics: Models and Integrative Inference for High-Throughput Data.

  1. Baladandayuthapani. V, Wang X, Mallick BK, Do K-A. (2014). Bayesian functional mixed models for survival responses with application to prostate cancer. In: Recent Advances in Applied Statistics: Slected Papers from the 2013 ICSA Applied Statistics Symposium.

  1. Jennings EM*, Morris JS, Manyam G, Carroll R J., Baladandayuthapani. V (2015) Bayesian models for flexible integrative analysis of multi-platform genomics data. In: Integrating omics data: statistical and computational methods (in press)

  1. Ni, Y*, Marcetti, G, Baladandayuthapani. V and Stingo, F. C. (2015) Bayesian approaches for large biological networks. In Nonparametric Bayesian Inference in Biostatistics. Editors: Peter Mueller and Riten Mitra

  1. Guha, S, Banerjee, S*, Gu, C, and Baladandayuthapani. V. (2015) Nonparametric Variable Selection, Clustering and Prediction for Large Biological Datasets In Nonparametric Bayesian Inference in Biostatistics. Editors: Peter Mueller and Riten Mitra

SELECTED PREPRINTS

  1. Zhang L., Baladandayuthapani, V and Morris, J.S. Bayesian functional graphical models. Journal of American Statistical Association (under second revision)
  2. Sagar, K., Ni, Y., Baladandayuthapani, V. and Bhadra, A. Individualized Inference using Bayesian Quantile Directed Acyclic Graphical Models. Journal of Machine Learning Research (submitted) arXiv:2210.08096
  3. Yao, T.*, Wu, Z., Bharath, K., Baladandayuthapani, V.. Geometry-driven Bayesian Inference for Ultrametric Covariance Matrices. https://doi.org/10.48550/arXiv.2401.11515 (under revision in  JRSS-B).
  4. Osher N*, Kang J., and  Baladandayuthapani, V. Spatially Structured Regression for Non-conformable Spaces: Integrating Pathology Imaging and Genomics Data in Cancer (submitted to Annals of Applied Statistics)

GRANT SUPPORT (CURRENT/FUNDED)

  1. Principal Investigator (MPI with J. Morris), Bayesian Network-Based Integrative Genomics Methods for Precision Medicine, National Institutes of Health, R01 CA 244845, 2/1/2021-1/31/2026

  1. Core Director, Cancer Data Sciences Shared Resource, Cancer Center Support Grant, National Institutes of Health (PI: Eric Fearon) P30 CA 046592, 6/01/2023 - 5/31/2028

  1. Core Director, Data Analyses Core, Genetics and Genomics of Leiomyosarcoma (LMS): Improved Understanding of Cancer Biology and New Approaches to Diagnosis and Treatment, National Institutes of Health, PI: Laurence Baker, Scott Schuetze, P50 CA 272170, 7/2022-6/2027

  1. Co-Investigator, ALOX15 Regulation of Colon Cancer Invasiveness via P13P-Linoleic Acid Metabolism, National Institutes of Health (PI: Imad Shureiqi) R01 CA 266223, 9/2021-8/2026

  1. Co-Investigator, PARTNERSHIP: Developing a Dietary Approach in the Management of Inflammatory Bowel Disease, USDA, PI: Grace Chen, USDA-NIFA-AFRI-007692, 1/2022-12/2024

  1. Co-Investigator, Michigan Center for Translational Cancer Proteogenomics, National Institutes of Health, PI: Arul Chinnaiyan, Saravana Dhanasekaran, Alexey Nesvizhskii, U24 CA 271037, 4/2022-3/2027

 GRANT SUPPORT (COMPLETED IN LAST 3 YEARS)

  1. Co-Investigator, 2018.050 IMPACT: Immunotherapy in Patients with Metastatic Cancers and CDK12 Mutations, Bristol-Myers Squibb, (PI: Ajjai Alva), CA209-8JJ, 8/2018-7/2023
  2. Co-Investigator, 2017.053 UM/BTCRC/AZ/Astex, Hoosier Cancer Foundation, (PI: Ajjai Alva) BTCRCGU 16043, 1/2018-1/2023
  3. Principal Investigator, Collaborative Research: New Bayesian Nonparametric Paradigms of Personalized Medicine for Lung Cancer, National Science Foundation, 13-570, 9/2015-8/2021

  1. Principal Investigator, (MPI with MJ Ha), Proteomic-based Integrated Subject-Specific Networks in Cancer, National Institutes of Health/National Cancer Institute, R21, 4/2018- 5/2021

  1. Principal Investigator, (MPI with Bani Mallick) Bayesian Graphical Models for Integration of Omics Data, R01 CA194391-01, National Institutes of Health/National Cancer Institute, 12/2015-11/2021

  1. Co-Investigator, Bayesian Methods for Complex, High Dimensional Functional  Data  in  Cancer  Research, R01 CA 178744, National Institutes of Health/National Cancer Institute, (PI: Jeffrey Morris), 9/2015-8/2020

RESEARCH ADVISING/MENTORING

PROFESSIONAL ACTIVITIES AND ACADEMIC SERVICE

EDITORIAL ACTIVITIES

Editorial Board(s)

Journal Reviewer

Annals of Applied Statistics, Australian and New Zealand Journal of Statistics, Biometrics, Canadian Journal of Statistics , Cancer Informatics, Communications in Statistics – Theory and Methods, Com- putational Statistics and Data Analysis, Human Heredity, Journal of American Statistical Association - Theory & Methods and Applications & Case Studies, Journal of Computational and Graphical Statistics, Journal of Multivariate Analysis, Journal of the Royal Statistical Society - Series B and C, Statistics in Medicine, Genetic Epidemiology, Blood, AISTATS

Book Chapter Reviewer: Elsevier: Amsterdam; Chapman & Hall/CRC

TEACHING

University of Michigan

University of Texas Graduate School of Biomedical Sciences and Rice University

Texas A&M University

Other

ACADEMIC PRESENTATIONS

National or International Conferences and Workshops (invited)

  1. Keynote Speaker, 13th International Conference on Intelligent Biology and Medicine (forthcoming)
  2. Summer school on Bayesian modeling, computation and applications, Vietnam 2025 (forthcoming)
  3. Joint Statistical Meetings, 2025 (forthcoming)
  4. MiRcore Summer Camp for High School students (2024)
  5. Eastern North American Region Spring Meetings, 2024
  6. Joint Statistical Meetings, 2024
  7. Bayesian Biostatistics Conference, Utrecht, 2023
  8. International Society for Clinical Biostatistics Conference, Milan, 2023
  9. Eastern North American Region Spring Meetings, 2023
  10. Joint Statistical Meetings, 2023
  11. Statistics Workshop, King Abdullah University Of Science And Technology, Saudi Arabia, 2022
  12. Big Data Summer Institute, University of Michigan, 2022
  13. Statistics for Oncology (Stat4Onc), University of Chicago, 2022
  14. Statistical Methods in Genetic/Genomic Studies Workshop, Singapore, 2022 (virtual)
  15. Eastern North American Region Spring Meetings, 2022
  16. Joint Statistical Meetings, American Statistical Association, 2022
  17. Eastern North American Region Spring Meetings, 2021 (virtual)
  18. Joint Statistical Meetings, American Statistical Association, 2020 (virtual)
  19. Eastern North American Region Spring Meetings, 2020 (virtual)
  20. Computational Medicine Conference, University of Pittsburgh, 2019
  21. International Conference on Bayesian Nonparametrics, Oxford, UK, 2019
  22. Workshop on Biomedical Big Data and Biostatistics, Chengdu, China, 2019
  23. Cells to Society Seminar, University of Michigan, 2019
  24. Conference Board of the Mathematical Sciences (CBMS) Conference: Elastic Functional and Shape Data Analysis, Columbus, 2018
  25. Joint Statistical Meetings, American Statistical Association, Vancouver, 2018
  26. International Conference on Big Data and Information Analytics, Houston, 2018
  27. Keynote Speaker, Genome Engineering for Cancer Treatment, Canberra, Australia, 2017
  28. ERCIM/CMS Stats, London, 2017
  29. IISA Annual Meeting, Hyderabad, 2017
  30. Workshop on Applications-Driven Geometric Functional Data Analysis, Tallahassee, 2017
  31. ISI World Statistics Congress, Morocco, 2017
  32. Southern Regional Council on Statistics, Jekyll Island, 2017
  33. American Association for the Advancement of Science Annual Meeting, 2017
  34. 2nd Seattle Symposium on Health Care Data Analytics, 2016
  35. SIAM Conference on Uncertainty Quantification (UQ16), Lausanne, Switzerland, 2016
  36. Joint Statistical Meeting, Chicago, 2016
  37. ISNPS Meeting, Avignon, France, 2016
  38. ISBA World Meeting, Sardinia, 2016
  39. ICSA Meeting, Shanghai, 2016
  40. International Indian Statistical Association Conference, Pune, India, 2015
  41. Alan Gelfand’s 70th Birthday Conference, 2015
  42. International Society for Non-Parametric Statistics (ISNPS) meeting, Graz, Austria
  43. Panel on Big Data, ISNPS Meeting, Austria, 2015
  44. Joint Statistical Meeting, Seattle, 2015
  45. Asian Regional Section of the IASC meeting, Singapore, 2015,
  46. iBRIGHT conference, Houston, 2015
  47. Institute of Applied Statistics Sri Lanka (IASSL) Conference, Colombo, Sri Lanka, 2014
  48. Joint Statistical Meetings, Boston, 2014
  49. International Biometric Society Conference, Florence, Italy, 2014
  50. Bioinformatics: Opening Workshop, SAMSI, 2014
  51. International Bayesian Meeting, Cancun, Mexico, 2014
  52. ISBIS 2014 and SLDM Meeting, Durham, North Carolina, 2014
  53. Eastern North American Region Spring Meetings, 2014
  54. Bayesian Biostatistics and Bioinformatics Conference, Houston, Texas, 2014
  55. STATISTICS 2013, C R Rao Institute, Hyderabad, India, 2013
  56. ICSA/ISBS Joint Statistical Conference, Washington, DC, 2013
  57. Joint Statistical Meetings, Montreal, Canada, 2013
  58. Latent Gaussian Models, Reykjavik, Iceland, 2013
  59. Bayesian methods in Biostatistics and Bioinformatics, IRB, Barcelona, Spain, 2012
  60. IEEE International Workshop on Genomic Signal Processing and Statistics, Washington DC, 2012
  61. Biotechnology and Bioinformatics Symposium, Provo, UT, 2012
  62. Interface 2012, Houston, Texas, 2012
  63. International Society for Bayesian Analysis (ISBA) World Meeting, 2012
  64. Joint Statistical Meetings, San Diego, 2012
  65. Eastern North American Region Spring Meetings, Washington D.C., 2012
  66. New Grantee Workshop, National Cancer Institute, 2011
  67. Joint Statistical Meetings, Miami, 2011
  68. 8th Workshop on Bayesian Nonparametrics, Veracruz, 2011
  69. IISA Conference, Raleigh, NC, 2011
  70. Eastern North American Region Spring Meetings, Miami, 2011
  71. Eighth ICSA International Conference, Guangzhou, China, 2011
  72. Eastern North American Region Spring Meetings, New Orleans, 2010
  73. Frontier of Statistical Decision Making and Bayesian Analysis, San Antonio, 2010
  74. International Indian Statistical Association (IISA) Conference, Vishakapatnam, 2010
  75. Frontiers of Interface between Statistics and Sciences, Hyderabad, 2010
  76. Joint Statistical Meetings, Washington D.C, 2009
  77. Bayesian Biostatistics Conference, UT MD Anderson Cancer Center, 2009
  78. 9th World Conference of the International Society for Bayesian Analysis (ISBA), 2008
  79. Southern Regional Council on Statistics Summer Research Conference (SRCOS), 2008
  80. International Indian Statistical Association (IISA) Conference, Storrs 2008
  81. International Conference on Statistical Paradigms, ISI Kolkata, 2008
  82. Joint Statistical Meetings, Salt Lake City, Utah, 2007
  83. International Indian Statistical Association (IISA) Conference, Cochin 2007
  84. Eastern North American Region Spring Meetings, Tampa, 2006
  85. International Biometric Conference in Montre`al, Que´bec, Canada, 2006

Academic Departments (invited)

  1. Department of Biostatistics, Brown University, 2025 (forthcoming)
  2. Andrei Yakovlev Colloquium, Department of Biostatistics, University of Rochester, 2024
  3. Department of Biostatistics, Virginia Commonwealth University, 2023
  4. Epidemiology & Biostatistics Cancer Imaging Research Center, WUSTL, 2023
  5. Division of Biostatistics, University of Pennsylvania, 2022
  6. Department of Bioinformatics, Tools & Technology Seminar, University of Michigan, 2021 (virtual)
  7. Michigan Institute for Data Science, 2020 (virtual)
  8. Department of Statistics, Texas A&M University, 2020 (virtual)
  9. Myrto Lefkopoulou Distinguished Lectureship, Harvard University, 2019
  10. Department of Statistics & Probability, Michigan State University, 2019
  11. Department of Statistics & Data Science, UT Austin, 2019
  12. University of Michigan Precision Health Seminar, 2019
  13. Cancer Biology/Cancer Genetics Program Meeting, University of Michigan, 2019
  14. Department of Computational Medicine and Bioinformatics, University of Michigan, 2018
  15. Department of Statistics, Virginia Tech University, 2018
  16. Annual Theodore G. Ostrom Lecture, Washington State University, 2018
  17. CSIRO Research Group, Brisbane, Australia, 2017
  18. Department of Biostatistics, University of Michigan, 2017
  19. Department of Biostatistics, Columbia University, 2017
  20. Department of Biostatistics, Fred Hutchinson Cancer Center, 2015
  21. Department of Statistics, Rutgers University, 2015
  22. UT MD Anderson Grand Rounds, 2015
  23. Department of Biostatistics, Memorial Sloan Kettering Cancer Center, 2015
  24. Department of Biostatistics, Columbia University, 2015
  25. Department of Statistics, Purdue University, 2013
  26. Department of Statistics & Computer Science, C.R.Rao Institute of Mathematics, Hyderabad, India, 2012
  27. Public Health Foundation of India, New Delhi, 2012
  28. Department of Statistics, North Carolina State University, Raleigh, NC, 2012
  29. Department of Biostatistics, UT School of Public Health, Houston, TX, 2012
  30. Department of Electrical Engineering, Texas Tech University, Lubbock, TX, 2012
  31. Department of Statistics, University of Texas at Austin, TX, 2012
  32. Department of Biostatistics, University of California, Davis, 2012
  33. Department of Statistics, University of Connecticut, 2011
  34. Machine learning group, Eli Lilly and Company, 2011 (via web broadcasting)
  35. School of Mathematics, Statistics & Actuarial Science, University of Kent, UK, 2010
  36. Department of Statistics, University of Oxford, UK, 2010
  37. Department of Management Science and Statistics, University of Texas at San Antonio, 2008
  38. Department of Epidemiology, UT MD Anderson Cancer Center, 2008
  39. Department of Biostatistics, UT School of Public Health, Houston 2008
  40. Department of Statistics, Rice University, 2008
  41. Department of Computer Science, University of Houston, 2008
  42. Department of Statistics, University of Missouri - Columbia, 2008
  43. Indian School of Business, Hyderabad, India, 2007
  44. Department of Statistics, University of Puerto Rico - Mayaguez Campus, 2006
  45. School of Medicine, University of Puerto Rice - San Juan Campus, 2006
  46. Richard F. Barry Mathematics & Statistics Colloquium, Old Dominion University, 2005
  47. Department of Statistics, University of Kentucky, 2005
  48. Department of Biostatistics, Section on Statistical Genetics, University of Alabama, 2005
  49. Department of Statistics, Michigan State University, 2005
  50. Department of Biostatistics, University at Buffalo The State University of New York, 2005
  51. Division of Biostatistics, University of Minnesota, 2005
  52. Department of Statistics, University of California, Riverside, 2005
  53. Department of Biostatistics & Applied Mathematics, M. D. Anderson Cancer Center, 2005
  54. Department of Integrative Studies , Arizona State University West, 2005
  55. Statistics and Data Mining Research, Bell Laboratories, 2005
  56. Department of Statistical Science, Southern Methodist University, 2005
  57. Department of Statistics, Texas A&M University, 2004

Academic Presentations (contributed)

  1. Joint Statistical Meetings, Denver, Colorado 2008
  2. Ninth Case Studies in Bayesian Analysis Meeting, Pittsburgh, 2007
  3. Joint Statistical Meetings, Seattle, Washington 2006
  4. Joint Statistical Meetings, Toronto, Canada 2004
  5. International Workshop on Bayesian Data Analysis, Santa Cruz, 2003
  6. Joint Statistical Meetings, San Francisco, 2003
  7. Summer Research Conference in Statistics (SRCOS), Jekyll Island, 2003
  8. Conference of Texas Statisticians, Texas A&M University, 2003
  9. Department of Biostatistics, University of Rochester, 2000