Researcher (2023.10-now) under the supervision of Gabor Lugosi at the Statistics Group in the Barcelona School of Economics, Universitat Pompeu Fabra, Barcelona, Spain.

Ph.D. student in Mathematics (2019.09-now) under the supervision of Nick Wormald and Mikhail Isaev at the Discrete Mathematics Group, School of Mathematics, Monash University, Melbourne, Australia.

M.S. in Computer Science (2016.09-2019.06), Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.

B.S. in Physics (2012.09-2016.06) (Mount Everest Project, rank 1st, 1st-gen student), Sichuan University, Sichuan, China.

Exchange (2014.09-2015.06), University of Washington, Seattle, the U.S.A.

(7). Cumulant expansion for counting Eulerian orientations [arXiv] with Mikhail Isaev, Brendan McKay. Submitted.

(6). Generalization bounds for learning under graph-dependence: A survey [arXiv] with Massih-Reza Amini. Submitted.

5. Extremal independence in discrete random systems [arXiv] with Mikhail Isaev, Igor Rodionov, Maksim Zhukovskii. To appear in Annales de l'Institut Henri Poincare B: Probability and Statistics.

4. Asymptotic linearity of binomial random hypergraphs via cluster expansion under graph-dependence [arXiv] Advances in Applied Mathematics, 2022. DOI: 10.1016/j.aam.2022.102378.

3. When Janson meets McDiarmid: Bounded difference inequalities under graph-dependence [arXiv] Statistics & Probability Letters, 2022. DOI: 10.1016/j.spl.2021.109272.

2.
Extreme value theory for triangular arrays of dependent random variables,
with Mikhail Isaev,
Igor Rodionov,
Maksim Zhukovskii.
[Slides
by Igor Rodionov]
Russian Mathematical Surveys, 2020.
DOI: 10.1070/rm9964.
Uspekhi Matematicheskikh Nauk, 2020.
DOI: 10.4213/rm9964.

1.
McDiarmid-type inequalities for graph-dependent variables and stability bounds
[arXiv]
[PDF]
[URL]
[BibTeX]
[Slides]
with Xingwu Liu,
Yuyi Wang,
Liwei Wang.
Advances in Neural Information Processing Systems 32 (NeurIPS 2019).
Spotlight: 164 out of 6,743 submissions (2.43%)

0. Thesis: the probability of non-existence of small substructures in moderately sparse random objects via clusters and cumulants. (Submitted, under examination).

Tutor, Monash University:

MAT9004 Mathematical foundations for data science and AI (2020 S1/S2, 2021 S1/S2, 2022 S1/S2, 2023 S1)

MAT1830 Discrete mathematics for computer science (2020 S1, 2021 S1, 2022 S1, 2023 S1)

MTH3241 Random processes in the sciences and engineering (2022 S1, 2023 S1)

MTH3260 Statistics of stochastic processes (2022 S2)

MTH3230 Time series and random processes in linear systems (2021 S2, 2022 S2)

MTH2222 Mathematics of uncertainty (2023 S1)

MTH2232 Mathematical statistics (2022 S2)

SCI1022 Introduction to scientific coding (Python) (2021 S2, 2022 S1)

Teaching assistant, Institute of Computing Technology, CAS: Advanced algorithms (2019 Spring), Probabilistic method and random graphs (2018 Autumn)