Publications

Links provided in titles. The ones that I served as Corresponding Author are marked with star.

Preprints

39. N. Ning* Robust Iterative Learning Hidden Quantum Markov Models

38. T. Chien, N. Ning, S. Huang Hysteretic Multivariate Bayesian Structural GARCH Model with Soft Information

37.  N. Ning* Metropolis-adjusted Subdifferential Langevin Algorithm

36. N. Ning* VT-MRF-SPF: Variable Target Markov Random Field Scalable Particle Filter

35. H. Gu, J. Li (my PhD student, co-first author), W. Sun, M. Li, K. Leung, J. Wu, H. Yuan, M. McKay, B. Yang, M. Wang, N. Ning, L. Poon* Optimizing Global Genomic Surveillance for Early Detection of Emerging SARS-CoV-2 Variants

34. N. Ning* Quantum Expander Mixing Lemma and its Converse

33. N. Ning* and J. Qiu, The mbsts package: Multivariate Bayesian Structural Time Series Models in R.

32. H. Li and N. Ning* Stochastic Differential Equations Driven by G-Brownian Motion with Mean Reflections

31.  N. Ning, J. Wu, and X. Xu, Solutions and Stochastic Averaging for Delay-path-dependent Stochastic Variational Inequalities in Infinite Dimensions

30. H. Li and N. Ning* Propagation of chaos for doubly mean reflected BSDEs

Peer-Reviewed Journal Publications 

2022-Present Assistant Professor @TAMU

29. N. Ning and J. Wu, Well-posedness and Propagation of Chaos for McKean-Vlasov Stochastic Variational Inequalities, Journal of Theoretical Probability, Volume 39, Issue 1, 2026.

28. N. Ning and C. Victor An Assessment of Ensemble Kalman Filter and Azouani-Olson-Titi Algorithms for Continuous Data Assimilation: A Comparative Study, Communications in Computational Physics, Accepted. 

27. L. Sun, Z. Huang, C. Chiu  and N. Ning* Detecting Structural Shifts and Estimating Change-Points in Interval-Based Time Series, Statistics and Computing, Volume 35, Article number 127, 2025.

26. H. Soroushi, S. Abbasi, Y. Du, N. Ning, Y. Lei* Temporal Interference Stimulation: Mechanisms, Optimization, Validation, and Clinical Prospects—A Comprehensive Review, WIREs Computational Statistics, Volume 17, Issue 3, 2025.

25. H. Li and N. Ning* Doubly Reflected Backward SDEs Driven by G-Brownian Motions and Fully Nonlinear PDEs with Double Obstacles, Stochastics and Partial Differential Equations: Analysis and Computations, 2025.

24. N. Ning*, Convergence of Dirichlet Forms for MCMC Optimal Scaling with General Target Distributions on Large Graphs, Annals of Applied Probability, Volume 35, Issue 2, 2025.

23. B. Lin, Y. Dai (my PhD student), L. Zou* and N. Ning* Modeling the Impacts of Governmental and Human Responses on COVID-19 Spread Using Statistical Machine Learning, International Journal of Digital Earth, Vol 17, Issue 1, 2024.

22. B. Lin, L. Zou, M. Yang, B. Zhou, D. Mandal, J. Abedin, H. Cai and N. Ning Understanding Human-COVID-19 Dynamics using Geospatial Big Data: A Systematic Literature Review, Annals of GIS, Vol 30, Issue 4, 513–533, 2024.

21. J. Li (my PhD student), E. Ionides*, A. King, M. Pascualand, and N. Ning* Inference on spatiotemporal dynamics for coupled biological populations, Journal of the Royal Society Interface, Vol 21, 216, 2024.

20. N. Ning* and E. Ionides, Systemic Infinitesimal Over-dispersion on General Stochastic Graphical Models, Statistics and Computing, Volume 34, Article number 147, 2024.

19. B. Ning and N. Ning* Spike and Slab Bayesian Sparse Principal Component Analysis, Statistics and Computing, Volume  34, Article number 118, 2024.

18. N. Ning, J. Wu and J. Zheng, One-Dimensional McKean-Vlasov Stochastic Variational Inequalities and Coupled BSDEs with Locally Holder Noise Coefficients, Stochastic Processes and their Applications, Vol. 171, 104315, 2024. 

17. E. Ionides, N. Ning and J. Wheeler (alphabetical order), An Iterated Block Particle Filter for Inference on Coupled Dynamic Systems with Shared and Unit-specific Parameters,  Statistica Sinica, Vol. 34, 1-22, 2024.

16. N. Ning*, Bayesian Feature Selection in Joint Quantile Time Series Analysis, Bayesian Analysis, Vol. 20(1): 185-211, 2025.

15. N. Ning* and E. Ionides, Iterated Block Particle Filter for High-dimensional Parameter Learning: Beating the Curse of Dimensionality, Journal of Machine Learning Research, 24(82):1−76, 2023. 

14. N. Ning and J. Wu,  Multi-dimensional Path-dependent Forward-backward Stochastic Variational Inequalities, Set-Valued and Variational Analysis, 31, 2, 2023.

2019-2022 Postdoc training @Umich

13. N. Ning* and W. Liu, Reconstructibility of a General DNA Evolution Model, Discrete Mathematics, Vol. 345, Issue 6, 2022.

12. N. Ning*, E. Ionides and Y. Ritov, Scalable Monte Carlo Inference and Rescaled Local Asymptotic Normality, Bernoulli, Vol. 27, No. 4, 2532-2555, 2021.

11. W. Liu and N. Ning*, Phase Transition of the Reconstructability of a General Symmetric Model with Different In-community and Out-community Mutations on an Infinite Tree, SIAM Journal on Discrete Mathematics, 35(2), 1381–1417, 2021.

10. N. Ning and J. Wu, Well-posedness and Stability Analysis of Two Classes of Generalized Stochastic Volatility Models, SIAM Journal on Financial Mathematics, Vol. 12, No. 1, pp. 79–109, 2021.

9. K. Ding and N. Ning*, Markov Chain Approximation and Measure Change for Time-inhomogeneous Stochastic Processes, Applied Mathematics and Computation, Volume 392, 1 March 2021.

8. J. Qiu, R. Jammalamadaka and N. Ning*, Multivariate time series analysis from a Bayesian machine learning perspective, Annals of Mathematics and Artificial Intelligence, 88(10), pp 1061-1082, 2020.

2018-2019 Postdoc training @UW Seattle

7. J. Qiu, W. Liu and N. Ning*, The Evolution of Regional Innovation with Spatial Knowledge Spillover: Convergence or Divergence?, Networks and Spatial Economics, 20, pp 179-208, 2020.

6. W. Liu and N. Ning*, Information Reconstruction on an Infinite Tree for a $4\times 4$-state Asymmetric Model with Community Effects, Journal of Statistical Physics, Vol. 177, Issue 3, pp 438–467, 2019.

5. R. Jammalamadaka, J. Qiu and N. Ning*, Predicting a Stock Portfolio with the Multivariate Bayesian Structural Time Series Model: Do News or Emotions Matter?, International Journal of Artificial Intelligence, Vol. 17, Number 2, pp 81-104, 2019.

4. W. Liu and N. Ning*, Large Degree Asymptotics and the Reconstruction Threshold of the Asymmetric Binary Channels, Journal of Statistical Physics, Vol. 174, Issue 6, pp 1161–1188, 2019.

2014-2018 PhD study @UCSB

3. J. P. Fouque and N. Ning, Uncertain Volatility Models with Stochastic Bounds, SIAM Journal on Financial Mathematics, 9(4), 1175–1207, 2018.

2. W. Liu, R. Jammalamadaka and N. Ning, The Tightness of the Kesten-Stigum Reconstruction Bound of Symmetric Model with Multiple Mutations, Journal of Statistical Physics, Vol. 170, Issue 3, pp 617–641, 2018. 

1. J. Qiu, R. Jammalamadaka and N. Ning, Multivariate Bayesian Structural Time Series Model, Journal of Machine Learning Research, 19(68):1-33, 2018.