Chenkai Wang (王晨凯)

Research Assistant
Department of Computer Science and Engineering
Southern University of Science and Technology
Email: wangck2022 [at] mail.sustech.edu.cn
Office: 619-B, School of Engineering (South), SUSTech
Last updated: June 25, 2025
[Curriculum Vitae]
Research Interests
  • Financial Simulation Intelligence
  • Fintech
  • Machine Learning
  • Network Science
About Me

I am Chenkai Wang, currently a research assistant in the Financial Market Simulation Lab within the Department of Computer Science and Engineering at the Southern University of Science and Technology (SUSTech). I have the good fortune of being advised by Prof. Peng Yang. I earned my M.Sc. in Mathematics (with distinction) in 2024 from the Department of Statistics and Data Science at SUSTech, also under Prof. Peng Yang's supervision. I received my B.S. in Statistics from the same department, where I studied core statistical concepts and explored network science under the valuable mentorship of Prof. Yifang Ma.

I am passionate about cross-disciplinary research in financial market simulation and network science, and I am beginning to explore generative models. Within agent-based modeling, my past work focused on improving simulation fidelity, which refers to enhancing the similarity between simulated data and real A-share market data, ensuring more accurate and reliable modeling outcomes.

Currently, I am seeking a Ph.D. position. I'm always open to new opportunities, collaborations, or suggestions—you're more than welcome to write me an email to connect (or make friends)! Let's explore the exciting world together!

Preprints
[IEEE Transactions]
In Preparation
Junjie Zhao, Chengxi Zhang, Chenkai Wang, Peng Yang
[IROS]
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2025)
Xinyi Yuan, Zhiwei Shang, Zifan Wang, Chenkai Wang, Zhao Shan, Meixin Zhu, Chenjia Bai, Weiwei Wan, Kensuke Harada, Xuelong Li
[HTML] [PDF]
[IEEE Transactions]
IEEE Transactions on Computational Social Systems, doi: 10.1109/TCSS.2025.3574236
Chenkai Wang, Junji Ren, Peng Yang
[HTML] [PDF]
Honors and Awards
  • 2024: Outstanding Graduates Honor, SUSTech
  • 2023: Outstanding Graduate Student, SUSTech
  • 2023: Excellent Student Cadre, SUSTech
  • 2021: National Encouragement Scholarship, SUSTech
  • 2021: Provincial Second Prize in The Chinese Mathematics Competitions, Chinese Mathematical Society
  • 2020: National Encouragement Scholarship, SUSTech
  • 2020: Provincial Third Prize in The Chinese Mathematics Competitions, Chinese Mathematical Society
Education
[Southern University of Science and Technology]
Supervisor: Prof. Peng Yang (SUSTech)
M.S., major in Mathematics (with distinction), Department of Statistics and Data Science
Thesis: High-fidelity Calibration of Financial Market Simulation with Multivariate Time Series Data
Nanshan, Shenzhen, China
[Southern University of Science and Technology]
Supervisor: Prof. Yifang Ma (SUSTech)
Nanshan, Shenzhen, China
Work Experience
[Southern University of Science and Technology]
Supervisor: Prof. Peng Yang (SUSTech)
Nanshan, Shenzhen, China
Projects
[Advanced Network Science]
Advisor: Prof. Yanqing Hu (SUSTech)
  • Page Rank, Trust Rank, and Spam Farm in Graphs [HW 1 & 2].
  • Small World Phenomena and the Greedy Algorithm [HW 3].
  • ER Network and Giant Component [HW 4].
  • Community Structure, Spectral Analysis, and its Generalization [HW 5].
  • Added constraints to Jon Kleinberg's network model, proved the corresponding expected delivery time, and implemented greedy algorithm in Python to validate results [Final Project].
[HW 1 & 2] [HW 3] [HW 4] [Pre of HW 4] [HW 5] [Final Project]
[Semi-parametric Regression]
Advisor: Prof. CHEN XIN (SUSTech)
Key Words: n=71, p=4088 regression problem; feature selection method, model ensemble
Applied feature selection methods (e.g., Lasso, Elastic Net, LARs), Principal Component Regression, and Random Forest to fit 71 data pairings. Used Lasso and LARs to select features for linear model fitting. Combined the 71 models using ensemble techniques and evaluated the final model based on MSE and error range to determine the best approach.
[Report]
[Sample Survey]
Advisor: Prof. CHEUNG Siu Hung (SUSTech, retired)
Key Words: Stratified Sampling; Sleep Conditions; Study Conditions
Conducted a comprehensive survey to analyze study conditions, comparing in-school and online learning in terms of assignments, reviews, sleep, exercise, and class engagement.
[Report]
Teaching Assistants
Southern University of Science and Technology:

  • MA204: Mathematical Statistics, 2023 Spring, rated excellent by the lecturer: Prof. Guoliang Tian
  • MA212: Probability and Statistics, 2023 Spring, rated excellent by the lecturer: Prof. GABRIELLE JING
  • STA217: Introduction to Data Science, 2023 Fall, rated excellent by the lecturer: Prof. Yifang Ma