Grants & Publications

2025

  • Ahn, S., Lim, J., Jiang, W., Lee, S., & Wang, X. (2025). Structure Preserving t-SNE of Matrix Framed Data. Computational and Structural Biotechnology Journal, 27, 1614–1635.
  • Ashrafi, Ali, et al. "Analytical solute transport modeling of furrow fertigation using the STANMOD software package." Journal of Hydrology and Hydromechanics 73.2 (2025): 200-209.
  • Bagheri, Amirsalar, et al. "A hybrid time series and physics-informed machine learning framework to predict soil water content." Engineering Applications of Artificial Intelligence 144 (2025): 110105.
  • Joseph Balderas, Dong Chen, Yanbo Huang, Li Wang, and Ren-Cang Li. Smart Agricultural Technology, to appear, 2025.
  • Basset, Christelle, et al. "Review of conceptual and empirical approaches to characterize infiltration." Vadose Zone Journal 24.1 (2025): e20393.
  • Bell, B., Geyer, M., Glickenstein, D., Hamm, K., Scheidegger, C., Fernandez, A., & Moore, J. (2025). Persistent Classification: Understanding Adversarial Attacks by Studying Decision Boundary Dynamics. Statistical Analysis and Data Mining: The ASA Data Science Journal, 18(1), e11716.
  • Bosikun, K., Jamali, T., Ghanbarian, B., & Kurths, J. (2025). Complex network analysis of extreme temperature events in the Contiguous United States. Atmospheric Research, 318, 107995.
  • Chen, Junru, et al. "Refined assessment of biocrusts-induced changes in dryland soil water and heat fluxes during evaporation." Journal of Hydrology (2025): 133670.
  • Esmaeilpour, Misagh, et al. "Scale-dependent permeability in geologic formations: Renormalization group theory and finite-size scaling analysis." Advances in Water Resources (2025): 105019.
  • Ghanbarian, Behzad. "Non‐Linearity in Mean Annual Peak Flow Scaling With Upstream Basin Area: Insights From Percolation Theory." Ecohydrology 18.2 (2025): e2709.
  • Hamm, K., Moosmüller, C., Schmitzer, B., & Thorpe, M. (2025). Manifold learning in wasserstein space. SIAM Journal on Mathematical Analysis, 57(3), 2983-3029.
  • Hunt, Allen G., Ghanbarian, B., and Sahimi, M. "Inferring shallow storage properties from the analysis of percolation theory of streamflow elasticity." Science of The Total Environment 988 (2025): 179834.
  • Hunt, Allen G., et al. "Gaia: Complex systems prediction for time to adapt to climate shocks." Vadose Zone Journal 24.3 (2025): e70016.
  • Koopaei, L.J., Zamanzade, E., Parvardeh, A., and Wang, X. (2025), “Nonparametric Estimation of A Biometric Function Using Ranked Set Sampling with Tie Information”. Biometrical Journal. 67(2). DOI: 10.1002/bimj.70007.
  • Lu, Z., Xu, L., and Wang, X.* (2025), “BIT: Bayesian Identification of Transcriptional Regulators from Epigenomics-based Query Region Sets”. Nature Communications. 16, 4966. DOI: 10.1038/s41467-025-60269-4.
  • Xijun Ma, Li Wang, Leihong Zhang, Chungen Shen, and Ren-Cang Li. Multi-view partially shared subspace learning. Optimization and Engineering, to appear, 2025.
  • McLachlan, David, et al. "Applications of percolation-based effective-medium approximation to electrical conductivity in porous media with surface conduction." Journal of Hydrology (2025): 133384.
  • Millán, Humberto, et al. "Multiscale multifractal assessment of sub-monthly hydrometeorological flash events in a tropical climate." Theoretical and Applied Climatology 156.3 (2025): 1-29.
  • Mountain, Jade O., et al. "Characterizing Pore-Throat Size Distributions in Mesaverde Tight Gas Sandstones Using Generalized Normal Distribution." Transport in porous media 152.9 (2025): 69.
  • Oladoja, V., Jamali, T., Ghanbarian, B., & Kurths, J. (2025). Analyzing spatiotemporal patterns of extreme precipitations in North America using complex network theory. Journal of Hydrology, 133492.
  • Osorio, Nelsy, et al. "Predicting elastic moduli of heterogeneous porous media by percolation theory and effective‐medium approximation." Journal of Geophysical Research: Solid Earth 130.4 (2025): e2024JB030836.
  • Senevirathna, Shaluka, et al. "Modeling and scaling spontaneous imbibition with generalized fractional flow theory and non-Boltzmann transformation." SPE Journal 30.06 (2025): 3709-3724.
  • Sierant, M., Jin, S. C., Bilguvar, K., Dong, W., Jiang, W., Lu, Z., Li, B., López-Giráldez, F., Tikhonov, I., Zeng, X., Lu, Q., Choi, J., Zhang, J., Nelson-Williams, C., Knight, J., Zhao, H., Cao, J., Mane, S., Sedore, S. C., … Lifton, R. P. (2025). Genomic analysis of 11,555 probands identifies 60 dominant congenital heart disease gene. Proceedings of the National Academy of Sciences, 122(13), e2420343122.
  • Taheri, Kioumars, et al. "A synergistic approach to enhanced oil recovery by combining in-situ surfactant production and wettability alteration in carbonate reservoirs." Scientific Reports 15.1 (2025): 11688.
  • Talukder, Z., Rana, M., Hamm, K., & Islam, M. A. (2025, July). Empowering clients: Self-adaptive federated learning for data quality challenges. In 2025 IEEE International Conference on Edge Computing and Communications (EDGE) (pp. 126-136). IEEE.
  • Emily W. Van Buren, Kelsey M. Beavers, Mariah N. Cornelio, Alexia Stokes, Madison Emery, Marilyn E. Brandt, Jeffery P. Demuth, Li Wang, and Laura D. Mydlarz. Leveraging machine learning to classify and characterize gene expression patterns in two coral diseases. Discover Applied Sciences, to appear, 2025.
  • Xu, L., Zhou, G., Jiang, W., Zhang, H., Dong, Y., Guan, L., & Zhao, H. (2025). JointPRS: A data-adaptive framework for multi-population genetic risk prediction incorporating genetic correlation. Nature Communications, 16, 3841.
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2024

  • Ahn, S., Wang, X., Moon, C., and Lim, J. (2024), “New Scheme of Empirical Likelihood Method for Ranked Set Sampling: Applications to Two One-Sample Problems”. International Statistical Review. DOI: 10.1111/insr.12589.
  • Baguley, J.G., Rostami, M.A., Baldrighi, E., Bang, H.W., Dyer, L.A., & Montagna, P.A. (2024). Harpacticoid copepods expand the scope and provide family-level indicators of the Deepwater Horizon oil spill deep-sea impacts. Marine Pollution Bulletin, 202(116343).
  • Barth, J., Yang, Y., Xiao, G., and Wang, X.* (2024), “MetaNorm: Incorporating Meta-analytic Priors into Normalization of NanoString nCounter Data”. Bioinformatics. 40(1), btae024. DOI: 10.1093/bioinformatics/btae024.
  • Hamm, K., & Korzeniowski, A. (2024). On Wasserstein distances for affine transformations of random vectors. Foundations of Data Science, 6(4), 468-491.
  • Lu, Z., Xiao, X., Zheng, Q., Wang, X., and Xu, L. (2024), “Assessing Computational Methods for Predicting Transcriptional Regulators with Query Gene Sets”, Briefings in Bioinformatics. 25(5), bbae366. DOI: 10.1093/bib/bbae366.
  • Park, S., Kim, J., Wang, X., and Lim, J. (2024), “Variable Selection in Bayesian Multiple Instance Regression using Shotgun Stochastic Search”. Computational Statistics and Data Analysis. 196. DOI: 10.1016/j.csda.2024.107954.
  • Wang, B., Dohopolski, M., Bai, T., Wu, J., Hannan, R., Desai, N., Garant, A., Yang, D., Nguyen, D., Lin, M.-H., Timmerman, R., Wang, X., Jiang, S. (2024), “Performance Deterioration of Deep Learning Models after Clinical Deployment: A Case Study with Auto-segmentation for Definitive Prostate Cancer Radiotherapy”. Machine Learning: Science and Technology. 5(2), 025077. DOI: 10.1088/2632-2153/ad580f.
  • Xiong, D., Park, S., Lim, J., Wang, T., and Wang, X.* (2024), “Bayesian Multiple Instance Classification Based on Hierarchical Probit Regression”. The Annals of Applied Statistics.18(1), 80-99. DOI: 10.1214/23-AOAS17.
  • Zhu, J., Wang, Y., Chang, W.Y., Malewska, A., Napolitano, F., Gahan, J.C., Unni, N., Zhao, M., Yuan, R., Wu, F., Yue, L., Guo, L., Zhao, Z., Chen, D.Z., Hannan, R., Zhang, S., Xiao, G., Mu, P., Hanker, A. B., Strand, D., Arteaga, C. L.,  Desai, N.,  Wang, X., Xie, Y., and Wang, T.  (2024), “Mapping Cell-to-cell Interaction from Spatially Resolved Transcriptomics Data”. Nature Methods. 21, 1830–1842. DOI: 10.1038/s41592-024-02408-1.

2023

  • Aselisewine, W. and Pal, S. (2023), “On the integration of decision trees with mixture cure model”. Statistics in Medicine, 42(23), 4111–4127.
  • Cheng, Y., Xia, Y., and Wang, X.* (2023), “Bayesian Multi-task Learning for Medicine Recommendation Based on Online Reviews”. Bioinformatics. 39(8), btad491. DOI: 10.1093/bioinformatics/btad491.
  • Dasilva, A., Saulo, H., Vila, R., Fiorucci, J. A., and Pal, S. (2023), “Parametric quantile autoregressive moving average models with exogenous terms”. Statistical Papers. DOI:10.1007/s00362-023-01459-4 (to appear).
  • Dulko-Smith, B., Ojeda-May, P., Ådén, J., Wolf-Watz, M., and Nam, K.* (2023), “Mechanistic basis for a connection between the catalytic step and slow opening dynamics of adenylate kinase”. J. Chem. Inf. Model., 63, 1556-1569.
  • Farleigh, K., A. Ascanio, M.E. Farleigh, D.R. Schield, D.C. Card, M. Leal, T.A. Castoe, T. Jezkova, J.A. Rodriguez-Robles (2023), “Signals of differential introgression in the genome of natural hybrids of Caribbean anoles” Molecular Ecology, 2023,6000-60017
  • Hamm, K.*, Henscheid, N., and Kang, S. (2023), “Wassmap: Wasserstein Isometric Mapping for Image Manifold Learning”. SIAM Journal on Mathematics of Data Science, 5(2), 475-501.
  • Hamm, K* (2023), “Generalized Pseudoskeleton Decompositions”. Linear Algebra and its Applications, 664, 236-252.
  • Hoang, L. Q., Pal, S., Liu, Z., Senkowsky, J., and Tang, L. (2023), “A time-dependent survival analysis for early prognosis of chronic wounds by monitoring wound alkalinity”. International Wound Journal, 20(5), 1459–1475.
  • Kim, S, Chen, W., Sun Mitchell, S. (2023) “Temporal Relationships in Dementia Family Dyadic Communication: Sequential Analysis.” Western Institute of Nursing Conference Proceeding. (Accepted)
  • Liang, X, Guo, Z.C., Wang, L., Li, R.C., Lin, W.W. (2023). “Nearly Optimal Stochastic Approximation for online Principal Subspace Estimation”, 66, 1087-1122.
  • Nam, K.*, Shao, Y., Major, D. T., and Wolf-Watz, M. (2023) “Perspectives on computational enzyme modeling: From mechanisms to design and drug development”. ACS Omega, under revision.
  • Nam, K.*, Arattu Thodika, A. R., Grundström, C., Sauer, U. H., and Wolf-Watz, M. (2023), “Elucidating dynamics of adenylate kinase from enzyme opening to ligand release”. J. Chem. Inf. Model. Accepted.
  • Nam, K.*, Tao, Y., and Ovchinnikov, V. (2023), “.Molecular Simulations of Conformational Transitions within the Insulin Receptor Kinase Reveal Consensus Features in a Multistep Activation Pathway”. J. Phys. Chem. B, 127, 5789-5798.
  • Nam, K.* and Wolf-Watz, M.* (2023), “Protein dynamics; The future is bright and complicated”. Struct. Dyn., 10, 014301.
  • Nie, J.W.*, Wang L., and Zheng, Z.Q. (2023). "Low rank tensor decompositions and approximations." Journal of the Operations Research Society of China, 1-27.
  • Pal, S. and Aselisewine, W. (2023), “A semiparametric promotion time cure model with support vector machine”. Annals of Applied Statistics, 17(3), 2680–2699.
  • Pal, S., Peng, Y., Aselisewine, W., and Barui, S. (2023), “A support vector machine-based cure rate model for interval censored data”. Statistical Methods in Medical Research, 32(12), 2405-2422.
  • Pal, S. and Roy, S. (2023), “On the parameter estimation of Box-Cox transformation cure model”. Statistics in Medicine, 42(15), 2600–2618.
  • Pal, S., Peng, Y., and Aselisewine, W. (2023), “A new approach to modeling the cure rate in the presence of interval censored data”. Computational Statistics. DOI:10.1007/s00180-023-01389-7 (to appear).
  • Pal, S. (2023), “A new cure model with discrete and multiple exposures”. Communications in Statistics-Simulation and Computation (accepted).
  • Pal, S. and Aselisewine, W. (2023), “Machine learning-based cure model in engineering reliability”. In: Developments in Reliability Engineering, Chapter 19 (Eds., M. Ram). Elsevier (accepted).
  • Pal, S. (2023), “Cure rate models”. In International Encyclopedia of Statistical Science Second Edition (Eds., M. Lovric), Springer Berlin, Heidelberg (accepted).
  • Pan, X., Van, R., Pu, J.*, Nam, K.*, Mao, Y.*, and Shao, Y.* (2023), “Free Energy Profile Decomposition Analysis for QM/MM Simulations of Enzymatic Reactions”. J. Chem. Theory Comput., 19, 8234-8244.
  • Robben, M., Ramesh, B., Pau, S., Meletis, D., Luber, J. and Demuth, J.P., 2023. scRNA-seq reveals novel genetic pathways and sex chromosome regulation in Tribolium spermatogenesis. bioRxiv, pp.2023-07.
  • Rostami, M.A., Balmaki, B., Dyer, L.A., Allen, J.M., Sallam, M.F., & Frontalini, F. (2023). Efficient pollen grain classification using pre-trained Convolutional Neural Networks: A comprehensive study. Journal of Big Data, 10, 151.
  • Rostami, M.A., Frontalini, F., Armynot du Châtelet, E., Francescangeli, F., Alves Martins, M.V., De Marco, R., Dinelli, E., Tramontana, M., Dyer, L.A., Abraham, R., Bout-Roumazeilles, V., Delattre, M., & Spagnoli, F. (2023). Understanding the distributions of benthic Foraminifera in the Adriatic Sea with gradient forest and structural equation models. Applied Sciences, 13(2), 794.
  • Roy, S. and Pal, S. (2023), “Optimal personalized therapies in colon cancer induced immune response using a Fokker-Planck framework”. In: Mathematics and Computer Science Volume 2, Chapter 3 (Eds., S. Ghosh, M. Niranjanamurthy, K. Deyasi, B. Basu Mallik and S. Das), pp.33-47. Scrivener-Wiley.
  • Shen Y, Kioumourtzoglou MA, Wu H, Spiro A, Vokonas P, Navas-Acien A, Baccarelli AA, Gao F. (2023). Cohort Network: a knowledge graph towards data dissemination and knowledge-driven discovery for cohort studies. Environmental Science & Technology 57, 8236-8244. Featured as supplemental cover paper.
  • Smith, C.F., C.M. Modahl, D. Ceja-Galindo, K.Y. Larson, S.P. Maroney, L. Bahrabadi, N.P. Brandehoff, B.W. Perry, M.C. MaCabe, D. Petras, B. Lamonte, J.J. Calvete, T.A. Castoe, S.P. Mackessy, K.C. Hansen, A.J. Saviola (2023), “Assessing target specificity of the small molecule inhibitor Marimastat to snake venom toxins: a novel application of thermal proteome profiling”. bioRxiv, 2023.10. 25.564059
  • Smith, C., Z.L. Nikolakis, B.W. Perry, D.R. Schield, N. Balchan, J. Parker, K.C. Hansen, A.J. Saviola, T.A. Castoe and S.P. Mackessy (2023), “Snakes on a plain: complex biotic and abiotic factors determine venom variation in North America’s widest-ranging rattlesnake”. BMC Biology 21,136
  • Smith, C., Z.L. Nikolakis, B.W. Perry, D.R. Schield, J.M. Meik, A.J. Saviola, T.A. Castoe, J. Parker, and S.P. Mackessy, (2023). “The best of both worlds: rattlesnake hybrid zones generate complex combinations of divergent venom phenotypes that retain high toxicity”. Biochimie 213,176-189
  • Smith, C, N.P. Brandehoff, L. Pepin, M.C. McCabe, T.A. Castoe, S.P. Mackessy, T. Nemkov, K.C. Hansen, A.J. Saviola, (2023). “Feasibility of detecting snake envenomation biomarkers from dried blood spots”. Analytical Science Advances, 4,26-36.
  • Treszoks, J. and Pal, S. (2023), “On the estimation of interval censored destructive negative binomial cure model”. Statistics in Medicine, 42(28), 5113-5134.
  • Wang, B., Dohopolski, M., Lin, M.H., Wu, J., Bai, T., Nguyen, D., Wang, X., and Jiang, S. (2023), “Deep Learning-Based Quality Assurance for Auto-Segmentation Masks in Radiotherapy”, International Journal of Radiation Oncology, Biology, Physics, 117 (2), e489-e490, ISSN 0360-3016. DOI: 10.1016/j.ijrobp.2023.06.1719.
  • Wang, K., Yang, Y., Wu, F., Song, B., Wang, X., Wang, T. (2023), “Comparative Analysis of Dimension Reduction Methods for Cytometry by Time-of-Flight Data”. Nature Communications. 14, 1836.DOI: 10.1038/s41467-023-37478-w.
  • Wang, G., Cheng, Y., Xia, Y., Ling, Q., and Wang, X. (2023), “A Bayesian Semi-supervised Approach to Key Phrase Extraction with Only Positive and Unlabeled Data”. INFORMS Journal on Computing. 5(3), 675-691. DOI: 10.1287/ijoc.2023.1283.
  • Wang, L., Zhang , L.H. , and Li, R.C. (2023). "Trace ratio optimization with an application to multi-view learning." Mathematical Programming 201(1), 97-131.
  • Wang, L.*, Li, R.C., Lin, W.W. (2023). “ Multiview Orthonormalized Partial Least Squares: Regularizations and Deep Extensions”. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 34(8), 4371 – 4385.
  • Westfall, A.K., S.S. Gopalan, B.W. Perry, R.H. Adams, A.J. Saviola, S.P. Mackessy, and T.A. Castoe, (2023), “Single-cell heterogeneity in snake venom expression is hardwired by co-option of regulators from progressively activated pathways”. Genome Biology and Evolution, 15,evad109.
  • Zhang, M., Barth, J., Lim, J., and Wang, X. (2023), “Bayesian Estimation and Testing in Random-Effects Meta-analysis of Rare Binary Events Allowing for Flexible Group Variability”. Statistics in Medicine. 42(11), 1699-1721. DOI:10.1002/sim.9695.
  • Zhang, Y., Zhang, C., Hua, W., Wang, X., Zhang, M., Palmer, K., and Chen, M. (2023), “An Expectation–Maximization Algorithm for Estimating Proportions of Deletions among Bacterial Populations with Application to Study Antibiotic Resistance Gene Transfer in Enterococcus Faecalis”. Marine Life Science & Technology, 5:28–43. DOI: 10.1007/s42995-022-00144-z.
  • Zhang, M., Xiao, O.Y., Lim, J. and Wang, X. (2023), “Goodness-of-fit Testing for Meta-Analysis of Rare Binary Events”. Scientific Reports. 13, 17712. DOI: 10.1038/s41598-023-44638-x.
  • Zhang, L., Wang, L., Liu, T.M., and Zhu, D.J. (2023), "Disease2Vec: Representing Alzheimer’s Disease Progression via Disease Embedding Tree", Pharmacological Research, to appear.

2022

  • C. Anand, P.D. Maia, J. Torok, C. Mesias, and A. Raj, The effects of microglia on tauopathy progression can be quantified using Nexopathy in silico (Nexis) models, Scientific Reports (2022), 12: 21170, pp. 1-14.
  • S. Pandya, P. D. Maia, B. Freeze, R.A.L. Menke, K. Tallbot, M.R. Turner, and A. Raj, Modelling seeding and neuroanatomical spread of pathology in amyotrophic lateral sclerosis, NeuroImage (2022), 251, pp. 1-12.
  • C. Silva, P. D. Maia, L. M. Stolerman, V. Rolla, L. Velho, Predicting dengue outbreaks in Brazil with manifold learning on climate data, Expert Systems with Applications (2022), 192, pp.1-13.
  • J. Torok, C. Mesias, P. D. Maia, and A. Raj, Matrix Inversion and Subset Selection (MISS): A novel pipeline for quantitative mapping of diverse cell types across the murine brain, PNAS (2022), 119 (14) e2111786119.
  • Yang, Y., Wang, K., Lu, Z., Wang, T.*, Wang, X.*, “Cytomulate: Accurate and Efficient Simulation of CyTOF data”. Genome Biology. 24, 262. DOI: 10.1186/s13059-023-03099-1.