Research interests: I fell in love with stochastic processes when I was introduced to this topic in Steve Krone's class at the University of Idaho, where I was working on my MS in Mathematics. During the same time Paul Joyce showed me the beauty of mathematical statistics. A bit later I worked with Paul and his evolutionary biology collaborator Jack Sullivan on a phylogenetics project, which was my first introduction to statistical inference for stochastic processes. Later at UCLA I was lucky to have worked with my PhD advisor Marc Suchard, who shares my love of statistics and stochastic modeling. My early research was in statistical phylogenetics and I remain active in this area. At UCLA, Marc showed me how phylogenetics can be applied to interesting problems in infectious disease epidemiology. Later, during my first faculty job at the University of Washington, I became interested in infectious disease epidemiology more broadly and now my main research interests are in data integration for Bayesian inference of transmission model parameters, nowcasting, and forecasting. Betz Halloran was instrumental during this stage of my research interests evolution, because she recruited me to teach Markov chain Monte Carlo in the Summer Institute in Statistics and Modeling in Infectious Diseases (SISMID) and encouraged Jon Wakefield and me to collaborate and to participate in the Center for Inference and Dynamics of Infectious Diseases (CIDID). During my first sabbatical, which I spent at the Fred Hutchinson Cancer Research Center, Erick Matsen got me involved in computational immunology and since then we have been working on and off on statistical methods to analyze high throughput sequence data of B-cell receptors. At the University of Washington and then here at UC Irvine, I worked a bit on fitting mechanistic models of hematopoiesis to various types of experimental data. Mathematically and statistically, these systems biology applications are not too far from my infectious disease projects.

Preprints (highlighted are student collaborators):

  1. Chi PB and Minin VM. Phylogenetic least squares estimation without genetic distances, arXiv:2311.12717.
  2. Martinez Lomeli LDJ, Ngo MN, Wakefield J, Shahbaba B, Minin VN. Statistical implications of relaxing the homogeneous mixing assumption in time series Susceptible-Infectious-Removed models, arXiv:2112.03186.

Book Chapters (highlighted are student collaborators):

  1. Wakefield J, Dong TQ, Minin VN. Spatio-temporal analysis of surveillance data, Handbook of Infectious Disease Data Analysis, edited by Held L, Hens N, O'Neill PD, and Wallinga J, 455 – 476, 2019, arxiv:1711.00555.
  2. Dhar A and Minin VN. Maximum likelihood methods for phylogenetic inference, Encyclopedia of Evolutionary Biology, edited by Kliman P (Phylogenetic Methods section edited by Kubatko L), 499 – 506, 2016, PDF.
  3. Palacios JA, Gill MS, Suchard MA, and Minin VN. Bayesian nonparametric phylodynamics, in Bayesian Phylogenetics: Methods, Algorithms, and Applications, edited by Chen MH, Kuo L, Lewis PO, 2014.

Commentary and Discussions (highlighted are student collaborators):

  1. Faulkner JR, Magee AF, Shapiro B, Minin VN. Rejoinder for discussion on "Horseshoe-based Bayesian nonparametric estimation of effective population size trajectories," Biometrics, 27, 695 – 699, 2020, PDF.
  2. Minin VN, Fintzi J, Lomeli LJM, Wakefield J. Discussion of "Dynamic Bayesian Influenza Forecasting in the United States with Hierarchical Discrepancy", Bayesian Analysis, 14, 301 – 306, 2019, PDF.
  3. Kypraios T and Minin VN. Introduction to the special section on inference for infectious disease dynamics, Statistical Science, 33, 1 – 3, 2018, PDF.

Refereed Articles (highlighted are student and postdoc collaborators):

  1. 2024 and in press
  2. Goldstein HI, Parker DM, Jiang S, Minin VM. Semiparametric inference of effective reproduction number dynamics from wastewater pathogen surveillance data, arXiv:2308.15770, Biometrics, in press, software: concRt.
  3. Bayer D, Goldstein I, Fintzi J, Lumbard K, Ricotta E, Warner S, Busch LM, Strich JR, Chertow DS, Parker DM, Boden-Albala B, Dratch A, Chhuon R, Quick N, Zahn M, Minin VM. Semi-parametric modeling of SARS-CoV-2 transmission using tests, cases, deaths, and seroprevalence data, arXiv:2009.02654, Annals of Applied Statistics, in press, software: semi_parametric_COVID_19_OC_model.
  4. Goldstein HI, Wakefield J, Minin VM. Incorporating testing volume into estimation of effective reproduction number dynamics, arXiv:2208.04418, Journal of the Royal Statistical Society: Series A, 187, 436 – 453, software: improving_rt.
  5. Magee AF, Karcher MD, Matsen FA, Minin VM. How trustworthy is your tree? Bayesian phylogenetic effective sample size through the lens of Monte Carlo error, arXiv:2109.07629, Bayesian Analysis, 19, 565 – 593, software: treess.
  6. 2023
  7. Awasthi N, Minin VM, Huang J, Chow D, Xu J. Fitting a stochastic model of intensive care occupancy to noisy hospitalization time series, arXiv:2203.00229, Statistics in Medicine, 42, 5189 – 5206.
  8. Baker CR, Barilar I, de Araujo LS, Rimoin AW, Parker DM, Boyd R, Tobias JL, Moonan PK, Click ES, Finlay A, Oeltmann JE, Minin VN, Modongo C, Zetola NM, Niemann S, Shin SS. Use of high-resolution geospatial and genomic data to characterize recent tuberculosis transmission, Botswana, medRxiv 2022.04.13.22273731, Emerging Infectious Diseases, 29, 977 – 987.
  9. Tang M, Dudas G, Bedford T, Minin VN. Fitting stochastic epidemic models to gene genealogies using linear noise approximation, arXiv:1902.08877, Annals of Applied Statistics, 17, 1 – 22, software: LNAphyloDyn. With a shorter version of this paper, Mingwei Tang won 2019 ASA Section on Bayesian Statistical Science student paper award.
  10. Parker DM, Medina C, Bohl J, Lon C, Chea S, Lay S, Kong D, Nhek S, Man S, Doehl JSP, Leang R, Kry H, Rekol H, Oliveira F, Minin VM, Manning JE. Determinants of exposure to Aedes mosquitoes: a comprehensive geospatial analysis in peri-urban Cambodia, Acta Tropica, 239, 106829.
  11. 2022
  12. Goldstein I, Bayer D, Barilar I, Kizito B, Matsiri O, Modongo C, Zetola NM, Niemann S, Minin VM, Shin SS. Using genetic data to identify transmission risk factors: statistical assessment and application to tuberculosis transmission, PLoS Computational Biology, 18: e1010696, software: kopanyo_tp_code.
  13. Fintzi J, Wakefield J, Minin VN. A linear noise approximation for stochastic epidemic models fit to partially observed incidence counts, Biometrics, 78, 1530 – 1541. software: stemr. With this paper, Jon Fintzi won 2020 ASA Section on Statistics in Epidemiology Norman Breslow Prize.
  14. 2021
  15. Parker DM, Bruckner T, Vieira VM, Medina C, Minin VN, Felgner PL, Dratch A, Zahn M, Bartell SM, Boden-Albala B. Epidemiology of the early COVID-19 epidemic in Orange County, California: comparison of predictors of test positivity, mortality, and seropositivity, Emerging Infectious Diseases, 27, 2604 – 2618.
  16. Martinez Lomeli L, Iniguez A, Shahbaba B, Lowengrub JS, Minin VN. Optimal experimental design for mathematical models of hematopoiesis, Journal of the Royal Society Interface, 18: 20200729, arxiv:2004.09065, software: python notebooks.
  17. 2020
  18. Magee AF, Höhna S, Vasylyeva TI, Leaché AD, Minin VN. Locally adaptive Bayesian birth-death model successfully detects slow and rapid rate shifts, PLoS Computational Biology, 16(10): e1007999, bioRxiv 853960; doi:, software: RevBayes scripts.
  19. Karcher MD, Suchard MA, Dudas G, Minin VN. Estimating effective population size changes from preferentially sampled genetic sequences, PLoS Computational Biology, 16(10): e1007774, arXiv:1903.11797, software: BEAST (development branch, example XML files).
  20. Dhar A, Ralph DK, Minin VN, Matsen FA. A Bayesian phylogenetic hidden Markov model for B cell receptor sequence analysis, PLoS Computational Biology, 16(8): e1008030, arXiv:1906.11982, PDF, software: linearham.
  21. Faulkner JR, Magee AF, Shapiro B, Minin VN. Horseshoe-based Bayesian nonparametric estimation of effective population size trajectories, Biometrics, 76, 677 – 690, arXiv:1808.04401, PDF, software: R package spmrf and RevBayes implementation. Biometrics Co-Editors recognized this paper as one the best published in the journal during 2020 – 2022 and selected it for presentation in the Biometrics Showcase session at the the International Biometric Conference 2022.
  22. Fourment M, Magee AF, Whidden C, Bilge A, Matsen FA, Minin VN. 19 dubious ways to compute the marginal likelihood of a phylogenetic tree topology, Systematic Biology, 69, 209 – 220, arXiv:11811.11804, software: marginal-experiments.
  23. 2019
  24. Xu J, Koelle S, Guttorp P, Wu C, Dunbar CE, Abkowitz JL, Minin VN. Statistical inference in partially observed stochastic compartmental models with application to cell lineage tracking of in vivo hematopoiesis, Annals of Applied Statistics, 13, 2091 – 2119, arxiv:1610.07550, PDF, software: branchCorr.
  25. Oaks JR, Cobb KA, Minin VN, Leaché AD. Marginal likelihoods in phylogenetics: a review of methods and applications, Systematic Biology, 68, 681 – 697, arXiv:1805.04072.
  26. Feng J, Shaw DA, Minin VN, Simon N, Matsen FA. Survival analysis of DNA mutation motifs with penalized proportional hazards, Annals of Applied Statistics, 13, 1268 – 1294, arXiv:1711.04057, software: samm.
  27. 2018
  28. Chattopadhyay S, Chi PB, Minin VN, Berg DE, Sokurenko EV. Recombination-independent rapid convergent evolution of the gastric pathogen Helicobacter pylori, BMC Genomics, 19, 835.
  29. Dhar A, Davidsen K, Matsen FA, Minin VN. Predicting B cell receptor substitution profiles using public repertoire data, PLoS Computational Biology, 14, e1006388, arXiv:1802.06406, software: SPURF.
  30. DeWitt WS, Mesin L, Victora GD, Minin VN, Matsen FA. Using genotype abundance to improve phylogenetic inference, Molecular Biology and Evolution, 35, 1253 – 1265, arxiv:1708.08944, PDF, software: gctree.
  31. Ho LST, Xu J, Crawford FW, Minin VN, Suchard, MA. Birth(death)/birth-death processes and their computable transition probabilities with statistical applications, Journal of Mathematical Biology, 76, 911 – 944, arXiv:1603.03819, PDF, software: MultiBD.
  32. Faulkner JR and Minin VN. Locally adaptive smoothing with Markov random fields and shrinkage priors, Bayesian Analysis, 13, 225 – 252, arXiv:1512.06505, PDF, software: bnps. With a shorter version of this paper, Jim Faulkner was a runner-up (Distinguished Student Paper Award) in 2015 WNAR student written paper competition.
  33. 2017
  34. Fintzi J, Cui X, Wakefield J, Minin VN. Efficient data augmentation for fitting stochastic epidemic models to prevalence data, Journal of Computational and Graphical Statistics, 26, 918 – 929, arxiv:1606.07995, PDF, software: BDAepimodel. With a shorter version of this paper, Jon Fintzi won 2016 WNAR student written paper competition.
  35. Hardin WR, Li R, Xu J, Shelton AM, Alas GCM, Minin VN, Paredez AR. Myosin-independent cytokinesis in Giardia utilizes flagella to coordinate force generation and direct membrane trafficking, Proceedings of the National Academy of Sciences, USA, 114, E5854 – E5863, PDF.
  36. Dhar A and Minin VN. Calculating higher-order moments of phylogenetic stochastic mapping summaries in linear time, Journal of Computational Biology, 24, 377 – 399, arxiv:1609.07844, PDF, software: phylomoments.
  37. Karcher M, Palacios JA, Lan S, Minin VN. phylodyn: an R package for phylodynamic simulation and inference, Molecular Ecology Resources, 17, 96 – 100, arXiv:1610.05817, PDF, software: phylodyn.
  38. 2016
  39. Koepke AA, Longini, IM, Halloran ME, Wakefield J, Minin VN. Predictive modeling of cholera outbreaks in Bangladesh, Annals of Applied Statistics, 10, 575 – 595, arXiv:1402.0536, PDF, software: bayessir. With a shorter version of this paper, Amanda Koepke won a 2015 Young Investigator Award from ASA Statistics in Epidemiology Section.
  40. Linkem CW, Minin VN, Leaché AD. Detecting the anomaly zone in species trees and evidence for a misleading signal in higher-level skink phylogeny (Squamata: Scincidae), Systematic Biology, 65, 465 – 477, bioRxiv, PDF.
  41. Karcher M, Palacios JA, Bedford T, Suchard MA, Minin VN. Quantifying and mitigating the effect of preferential sampling on phylodynamic inference, PLoS Computational Biology, 12, e1004789, arXiv:1510.00775, PDF, software: phylodyn.
  42. 2015
  43. Xu J, Guttorp P, Kato-Maede M, Minin VN. Likelihood-based inference for discretely observed birth-death-shift processes, with applications to evolution of mobile genetic elements, Biometrics, 71, 1009 – 1021, arXiv:1411.0031, PDF; supplement, software: bdsem. With a shorter version of this paper, Jason Xu won ASA Biometrics section student travel award to attend JSM 2015.
  44. Lan S, Palacios JA, Karcher, M, Minin VN, Babak Shahbaba. An efficient Bayesian inference framework for coalescent-based nonparametric phylodynamics, Bioinformatics, 31, 3282 – 3289, arXiv:11412.0158, PDF, supplement, software: phylodyn.
  45. Xu J and Minin VN. Efficient transition probability computation for continuous-time branching processes via compressed sensing, Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 952 – 961. (arXiv:1503.02644), PDF, supplement, software: bdsem.
  46. Chi PB, Chattopadhyay S, Lemey P, Sokurenko EV, Minin VN. Synonymous and nonsynonymous distances help untangle convergent evolution and recombination, Statistical Applications in Genetics and Molecular Biology, 14, 375 – 389, arXiv:1410.1263, PDF, supplement, software: synDss.
  47. McCoy CO, Bedford T, Minin VN, Robins H, Matsen FA. Quantifying evolutionary constraints on B-cell affinity maturation, Philosophical Transactions of the Royal Society B: Biological Sciences, 370, 20140244, arXiv:1403.3066, PDF, supplement, software: startreerenaissance.
  48. Lange JM, Hubbard RA, Inoue LYT, Minin VN. A joint model for multistate disease processes and random informative observation times, with applications to electronic medical records data, Biometrics, 71, 90 – 101, UW Biostatistics Working Paper 401, PDF, supplement, software: cthmm.
  49. 2014
  50. Pankey MS, Minin VN, Imholte GC, Suchard MA, Oakley TH. Predictable transcriptome evolution in the convergent and complex bioluminescent organs of squid, Proceedings of the National Academy of Sciences, USA, 111:E4736 – E4742. PDF, supplement, software: indorigin.
  51. Irvahn J and Minin VN. Phylogenetic stochastic mapping without matrix exponentiation, Journal of Computational Biology, 21:676 – 690, arXiv:1403.5040, PDF, supplement, software: phylomap.
  52. Chi PB, Duncan A, Kramer PA, Minin VN. Heritability estimation of osteoarthritis in the pig-tailed macaque (Macaca nemestrina) with a look toward future data collection, PeerJ, 2, e373, PeerJ PrePrints 2:e284v1, PDF. With a shorter version of this paper, Peter Chi was a runner-up in 2012 WNAR student written paper competition.
  53. Leaché AD, Fujita MK, Minin VN, Bouckaert RR. Species delimitation using genome-wide SNP data, Systematic Biology, 63:534 – 542, bioRxiv:10.1101/123456, PDF, software: BFD*, issue cover.
  54. Crawford FW, Minin VN, Suchard MA. Estimation for general birth-death processes, Journal of the American Statistical Association, 109:730 – 747, arXiv:1111.4954v1, PDF.
  55. 2013
  56. Doss CR, Suchard MA, Holmes I, Kato-Maeda M, Minin VN. Fitting birth-death processes to panel data with applications to bacterial DNA fingerprinting, Annals of Applied Statistics, 7, 2315 – 2335, arXiv:1009.0893, PDF, supplement, software: DOBAD.
  57. Leaché AD, Palacios JA, Minin VN, Bryson RW. Phylogeography of the Trans-Volcanic bunchgrass lizard (Sceloporus bicanthalis) across the highlands of southeastern Mexico, Biological Journal of the Linnean Society, 110, 852 – 865, PDF.
  58. Lange JM and Minin VN. Fitting and interpreting continuous-time latent Markov models for panel data, Statistics in Medicine, 32, 4581 – 4595, UW Biostatistics Working Paper 382, PDF, supplement, software: cthmm. With a shorter version of this paper, Jane Lange won 2012 WNAR student written paper competition.
  59. Irvahn J, Chattopadhyay S, Sokurenko EV, Minin VN. rbrothers: R package for Bayesian multiple change-point recombination detection, Evolutionary Bioinformatics, 9:235 – 238, PDF, software: rbrothers.
  60. Palacios JA and Minin VN. Gaussian process-based Bayesian nonparametric inference of population trajectories from gene genealogies, Biometrics, 69, 8 – 18, arXiv:1112.4138, PDF, supplement.
  61. 2012
  62. Lemey P, Minin VN, Bielejec F, Kosakovsky Pond SL, Suchard MA. A counting renaissance: Combining stochastic mapping and empirical Bayes to quickly detect amino acid sites under positive selection, Bioinformatics, 28, 3248 – 3256, PDF, software: BEAST.
  63. Ryu S, Goodlett DR, Noble WS, Minin VN. A statistical approach to peptide identification from clustered tandem mass spectrometry data, 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW), 648 – 653, PDF.
  64. Palacios JA and Minin VN. Integrated nested Laplace approximation for Bayesian nonparametric phylodynamics, Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 726 – 735, PDF.
  65. Sawaya SM*, Lennon D*, Buschiazza G, Gemmell N, Minin VN. Measuring microsatellite conservation in mammalian evolution with a phylogenetic birth-death model, Genome Biology and Evolution, 4, 636 – 647, PDF, supplement, software: McSMAC. * - joint first authors
  66. Kemal K, Kitchen C, Burger H, Foley B, Klimkait T, Hamy F, Anastos, K, Petrovic K, Minin VN, Suchard MA, Weiser B. Recombination between variants from genital tract and plasma: Evolution of multidrug-resistant HIV-1, AIDS Research and Human Retroviruses, 28, 1766 – 1774, PDF.
  67. 2011
  68. Minin VN, O'Brien JD, Seregin A. Imputation estimators partially correct for model misspecification, Statistical Applications in Genetics and Molecular Biology, 10, 1 – 24, an earlier version with a beta-binomial example: arXiv:0911.0939v1, PDF.
  69. 2009
  70. Chattopadhyay S, Weissman SJ, Minin VN, Russo TA, Dykhuizen DE, Sokurenko EV. High frequency of hotspot mutations in core genes of Escherichia coli due to short-term positive selection, Proceedings of the National Academy of Sciences, USA, 106, 12412 – 12417, PDF.
  71. O'Brien JD*, Minin VN*, and Suchard MA. Learning to count: robust estimates for labeled distances between molecular sequences, Molecular Biology and Evolution, 26, 801 – 814, PDF, software: markovjumps. * - joint first authors
  72. 2008
  73. Minin VN and Suchard MA. Fast, accurate and simulation-free stochastic mapping, Philosophical Transactions of the Royal Society B: Biological Sciences, 363, 3985 – 3995, PDF, an earlier version with more details.
  74. Minin VN, Bloomquist EW, and Suchard MA. Smooth skyride through a rough skyline: Bayesian coalescent-based inference of population dynamics, Molecular Biology and Evolution, 25, 1459 – 1471, PDF, software: BEAST.
  75. Minin VN and Suchard MA. Counting labeled transitions in continuous-time Markov models of evolution, Journal of Mathematical Biology, 56, 391 – 412, PDF.
  76. 2007
  77. Rajaram ML, Minin VN, Suchard MA, and Dorman KS. Hot and cold: spatial fluctuation in HIV-1 recombination rates, Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, 707 – 714, PDF.
  78. Minin VN, Dorman KS, Fang F, and Suchard MA. Phylogenetic mapping of recombination hot-spots in HIV via spatially smoothed change-point processes, Genetics, 175, 1773 – 1785, PDF, highlighted article.
  79. Fang F, Ding J, Minin VN, Suchard MA, and Dorman KS. cBrother: Relaxing parental tree assumptions for Bayesian recombination detection, Bioinformatics, 23, 507 – 508, PDF, software: cBrother.
  80. 2005
  81. Minin VN, Dorman KS, Fang F, and Suchard MA. Dual multiple change-point model leads to more accurate recombination detection, Bioinformatics, 21,3034 – 3042, PDF, software: DualBrothers.
  82. Abdo Z, Minin VN, Joyce P, and Sullivan J. Accounting for uncertainty in the tree topology has little effect on the decision theoretic approach to model selection in phylogeny estimation, Molecular Biology and Evolution, 22, 691 – 703, PDF.
  83. 2004
  84. Liu X, Minin V, Huang Y, Selingson D, and Horvath S. Statistical methods for analyzing tissue microarray data, Journal of Biopharmaceutical Statistics, 14, 671 – 685, PDF.
  85. 2003
  86. Minin V, Abdo Z, Joyce P, and Sullivan J. Performance-based selection of likelihood models for phylogeny estimation, Systematic Biology, 52, 674 – 683, PDF, software: DTModSel.
Last modified: May, 2024