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Research interests


  1. Vu Dinh, Aaron E. Darling and Frederick A. Matsen IV (2017).
    Online Bayesian phylogenetic inference: theoretical foundations via Sequential Monte Carlo.
    Systematic Biology. arxiv

  2. Mathieu Fourment, Brian C. Claywell, Vu Dinh, Connor McCoy, Frederick A. Matsen IV and Aaron E. Darling (2017).
    Effective online Bayesian phylogenetics via Sequential Monte Carlo with guided proposals.
    Systematic Biology. arxiv

  3. Cuong Nguyen, Lam Ho, Huan Xu, Vu Dinh and Binh Nguyen (2017).
    Bayesian pool-based active learning with abstention feedbacks. In review. arxiv

  4. Brian C. Claywell, Vu Dinh, Mathieu Fourment, Connor O. McCoy and Frederick A. Matsen IV.
    A surrogate function for one-dimensional phylogenetic likelihoods.
    Molecular Biology and Evolution. arxiv

  5. Owen G. Rehrauer, Vu Dinh, Bharat R. Mankani, Gregery T. Buzzard, Bradley Lucier and Dor Ben-Amotz (2016).
    Binary-complementary compressive filters for Raman spectroscopy.
    Journal of Applied Spectroscopy.

  6. Vu Dinh*, Lam Si Tung Ho*, Marc A. Suchard and Frederick A. Matsen IV (2017).
    Consistency and convergence of phylogenetic inference with species tree regularization.
    The Annals of Statistics. arxiv

  7. Vu Dinh*, Arman Bilge*, Cheng Zhang* and Frederick A. Matsen IV.
    Probabilistic path Hamiltonian Monte Carlo.
    International Conference on Machine Learning (ICML 2017). arxiv

  8. Vu Dinh, Lam Si Tung Ho, Binh T. Nguyen, Duy Nguyen.
    Fast learning rates with heavy-tailed losses.
    Advances in Neural Information Processing Systems (NIPS 2016). pdf

  9. Vu Dinh and Frederick A. Matsen IV (2016).
    The shape of the one-dimensional phylogenetic likelihood function.
    The Annals of Applied Probability. pdf

  10. Ankush Chakrabarty, Vu Dinh, Martin Corless, Ann E. Rundell, Stanislaw H. Zak and Gregery T. Buzzard (2016).
    SVM-informed explicit nonlinear model predictive control using low-discrepancy sequences.
    IEEE Transaction on Automatic Control. link

  11. Vu Dinh, Ann E. Rundell and Gregery T. Buzzard.
    Convergence of Griddy Gibbs sampling and other perturbed Markov chains.
    Journal of Statistical Computation and Simulation. 88.7 (2017): 1379-1400. link

  12. Vu Dinh*, Lam Si Tung Ho*, Nguyen Viet Cuong, Duy Nguyen and Binh T. Nguyen.
    Learning from non-iid data: fast rates for the one-vs-all multiclass plug-in classifiers.
    Theory and Applications of Models of Computation (TAMC 2015). arxiv

  13. Vu Dinh, Ann E. Rundell and Gregery T. Buzzard.
    Experimental design for dynamic identification of cellular processes.
    Bulletin of Mathematical Biology 76.3 (2014): 597-626. arxiv

  14. Vu Dinh, Ann E. Rundell and Gregery T. Buzzard.
    Effective sampling schemes for behavior discrimination in nonlinear systems.
    International Journal of Uncertainty Quantification 4.6 (2014): 535-554. link

  15. Ankush Chakrabarty, Vu Dinh, Gregery T. Buzzard, Stanislaw H. Zak and Ann E. Rundell.
    Robust explicit nonlinear model predictive control with integral sliding mode.
    American Control Conference (ACC 2014). pdf

  16. Nguyen Viet Cuong, Lam Si Tung Ho and Vu Dinh.
    Generalization and robustness of batched weighted average algorithm with V-geometrically ergodic Markov data.
    Algorithmic Learning Theory (ALT 2013). pdf

  17. Nguyen Viet Cuong, Vu Dinh and Lam Si Tung Ho.
    Mel-frequency cepstral coefficients for eye movement identification.
    IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2012).

  18. Jeffrey P. Perley, Judith Mikolajczak, Vu Dinh, Marietta L. Harrison, Gregery T. Buzzard and Ann E. Rundell.
    Systematically manipulating T-cell signaling dynamics via multiple model informed open-loop controller design.
    IEEE Conference on Decision and Control (CDC 2012).

  19. Duong Minh Duc*, Ho Si Tung Lam*, Nguyen Quang Thang* and Dinh Cao Duy Thien Vu*.
    On Harnack's inequality for non-uniformly p-Laplacian equations.
    Acta Mathematica Vietnamica. 36.2 (2011):199-214 . pdf