Dr Jianya Lu

School of Mathematics, Statistics and Actuarial Science (SMSAS)
Dr Jianya Lu



Jianya Lu is a lecturer at the Department of Mathematical Sciences of the University of Essex. He received his BS.s in Applied Mathematics from Henan Normal University, China, and then went on to do his MS.c in Probability Theory at Central South University, China. He obtained PhD in Mathematics from the University of Macau, Macau, China under the supervision of Dr Lihu Xu. Jianya has a wide range of interests in stochastic processes, the limit theory, stochastic algorithms, Stein’s method and other topics in probability theory and statistical learning. Recently, Jianya has focused on the methodology of stochastic algorithms, such as reinforcement learning and deep neural networks, using stochastic dynamics to provide insights into algorithm interpretation and optimization.


  • Ph.D. in Mathematics University of Macau, (2022)

  • MS.c in Probability Theory Central South University, (2018)

  • BS.s in Applied Mathematics Henan Normal University, (2015)


University of Essex

  • Lecturer in Statistics, Department of Mathematical Sciences, University of Essex (17/10/2022 - present)

Research and professional activities

Research interests

Stochastic Processes

Probability Limit Theorems and Moderate Deviations

Time Series

Stochastic Algorithms

Conferences and presentations

Distribution estimation for time series via DNN-based GANs with an application to change-point estimation

Invited presentation, Maths and Stats Research Seminars, London, United Kingdom, 21/8/2023

Approximation to stochastic variance reduced gradient Langevin dynamics by stochastic delay differential equations

Invited presentation, Swansea 2023 Probability summer workshop, Swansea, United Kingdom, 6/6/2023

Teaching and supervision

Current teaching responsibilities

  • Foundational Mathematics for Data Science (MA111)

  • Riemann Integration and Lebesgue Measure (MA213)


Journal articles (3)

Chen, P., Lu, J. and Xu, L., (2022). Approximation to Stochastic Variance Reduced Gradient Langevin Dynamics by Stochastic Delay Differential Equations. Applied Mathematics and Optimization. 85 (2)

Lu, J., Tan, Y. and Xu, L., (2022). Central limit theorem and self-normalized Cramér-type moderate deviation for Euler-Maruyama scheme. Bernoulli. 28 (2), 937-964

Jin, X., Li, X. and Lu, J., (2020). A kernel bound for non-symmetric stable distribution and its applications. Journal of Mathematical Analysis and Applications. 488 (2), 124063-124063

+44 (0) 1206 872851


3A.524, Colchester Campus

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