About
Research
Teaching
Animation
WIP

Publications

  1. Q. Du and A. Guyader, "Variance estimation of Adaptive Sequential Monte Carlo" (To appear in Annals of Applied Probability), 2019. [pdf] [arXiv]

  2. Q. Du "Asymmetric Sequential Monte Carlo" (in preparation), 2019. [pdf]

  3. Q. Du and T. Lelièvre, "Estimating Committor Function with Mondrian Forests" (in preparation), 2019. [pdf]

  4. Q. Du, G. Biau, F. Petit and R. Porcher, "Wasserstein Random Forests and Applications in Heterogeneous Treatment Effects" (submitted), 2020. [arXiv]

Ph.D. thesis

"Sequential Monte Carlo and Applications in Molecular Dynamics." [Manuscript] [Slides]

Talks in seminars

  1. Groupe de Travail des Thésards du LSTA, Paris, "Introduction to Feynman-Kac particle models", 2017.

  2. Working group of young researchers: statistical physics and interactions, Paris, "Genalogical tree-typed measures in Feynman-Kac models", 2017.

  3. RESIM 2018: 12th International Workshop on Rare-Event Simulation, Stockholm, "Variance estimation of Adaptive Multilevel Splitting", 2018.

  4. Young Researchers’ Meeting in Mathematical Statistics, Paris, "Variance estimation of Adaptive Multilevel Splitting", 2018.

  5. Groupe de Travail des Thésards du LPSM, Paris, "A brief introduction to Sequential Monte Carlo", 2018.

Posters

  1. Advances in Computational Statistical Physics, Luminy, "Variance estimation for generalized adaptive multilevel splitting ", 2018.

Participation to workshops/conferences

  1. Workshop: Simulation and probability: recent trends, Rennes, 2018.

  2. LPSM: Conférence de lancement, Paris, 2018.

Codes

  1. Static Adaptive Multilevel Splitting rare-events implementation. [Github]

  2. Implementation of generalized Adaptive Multilevel Splitting with unbiased variance estimation. [Github]

  3. Visulization of Feynman-Kac interacting particle systems. (Thanks to my wife @mydu for helping me with d3.js) [Github] [Viz]

  4. Wasserstein Random Forests based on NumPy and Cython. [Github].

  5. Adaptive Multilevel Splitting implemented under Asymmetric SMC framework with efficient variance estimator. [Github].