Yicheng Mao

PIMS Postdoctoral Fellow University of Calgary
CV
About

What I work on.

My research develops computational statistical methods for infectious disease and public health systems, where transmission dynamics, human behaviour, and surveillance processes interact. I focus on how statistical methods can support reliable inference, prediction, and data collection when the underlying system is nonlinear, partially observed, behaviourally adaptive, and computationally difficult to evaluate.

My current work spans four connected themes.

Yicheng Mao

Bayesian epidemic modelling

Inferring how diseases spread when behaviour adapts and data are partial.

Bayesian inference Simulation-based inference Epidemic models Behavioural change

Disease forecasting and AI-assisted modelling

Using machine learning and mechanistic models to forecast outbreaks and capture human behaviour during them.

Disease forecasting Machine learning Deep learning Agent-based models Large language models

Bayesian optimal experimental design

Designing experiments that learn the most from limited data when models are nonlinear and prior knowledge is uncertain.

Optimal design Discrete choice experiments Mixture experiment Partial profile design

Statistics for social and behavioural sciences

Applying choice experiments across applied domains to study how people decide between alternative tools, services, and policies.

Human-computer interaction Educational technology Organizational behaviour Consumer behaviour Health decision-making
Benben the dog
P.S. The fluffy one is Benben, my dog and occasional debugging companion.
Trajectory

Where I have been.

2026 → now
PIMS Postdoctoral Fellow
Department of Mathematics and Statistics, University of Calgary
Supervisor: Prof. Rob Deardon
2025 → 2026
Visiting Researcher
Department of Mathematics and Statistics, University of Calgary
Project: population memory mechanisms in behavioural change epidemic models
Supervisors: Prof. Rob Deardon, Prof. Lorna Deeth
2023 → 2025
PhD in Statistics
School of Business and Economics, Maastricht University
Thesis: Bayesian optimal choice designs using simulated annealing
Supervisor: Prof. Roselinde Kessels
2022 → 2023
Research Intern
King Abdullah University of Science and Technology
Project: stability analysis of gradient-based methods for compositional optimization
Supervisor: Prof. Di Wang
2021 → 2023
MSc in Statistics and Data Sciences
Faculty of Science, KU Leuven
Thesis: Color preference of fruit flies: a choice experiment approach
Supervisors: Prof. Peter Goos, Dr. Bart De Ketelaere
2017 → 2021
BSc in Accounting
Business School, Beijing Normal University
Thesis: The impact of cognition on learning from failure: a grounded theory approach
Supervisor: Prof. Wenzhou Wang
Publications

Selected work.

Published

  1. Y. Mao, R. Deardon, L. E. Deeth. Identifying memory mechanisms in Bayesian models of behavioural change during epidemics. Epidemics, 56, 100927 (2026). DOI
  2. Y. Mao, R. Deardon, L. E. Deeth. Memory mechanisms for behavioural change in Bayesian individual-level spatial epidemic models. Infectious Disease Modelling, 11(4), 1536–1553 (2026). DOI
  3. Y. Mao, R. Kessels, T. C. van der Zanden. Constructing Bayesian optimal designs for discrete choice experiments by simulated annealing. Journal of Choice Modelling, 55, 100551 (2025). DOI
  4. Y. Mao, R. Kessels. Optimal designs for mixture choice experiments by simulated annealing. Chemometrics and Intelligent Laboratory Systems, 257, 105305 (2025). DOI
  5. Y. Mao, R. Kessels, R. Mee. Beyond randomization: design and analysis of discrete choice experiments in the presence of profile order effects within choice sets. Applied Stochastic Models in Business and Industry, 41(5), e70043 (2025). DOI

Submitted

  1. Y. Mao, B. Li, M. Gan, J. Qian. Between the lines: understanding employee preferences for AI usage guidelines through discrete choice experiments. International Journal of Human–Computer Interaction Revision requested
  2. B. Li, Y. Mao, W. Niu, J. Qian. What drives employees' choices in AI training: modeling employees' preferences for AI training via a discrete choice experiment. Human Resource Development Quarterly Revision requested
  3. Y. Mao, B. Lopman, K. Koelle, M. S. Y. Lau. Subtype dynamics reveal horizon-dependent structure in influenza predictability. Journal of the Royal Society Interface Submitted Preprint
  4. Y. Mao, R. Kessels. Simulated annealing for model-robust partial profile choice designs with simultaneous optimization. Statistics in Medicine Submitted arXiv
  5. Y. Mao, R. Deardon. Neural posterior estimation for spatial individual-level epidemic models. PLOS Computational Biology Submitted arXiv
  6. Y. Mao, H. Du. Data mixing as mixture experiment: response surface methodology and optimal design for large language model pretraining. RSS: Data Science and Artificial Intelligence Submitted
  7. Y. Mao, B. Li. Choosing the future of learning: assessing preferences for educational GPTs through a discrete choice experiment. Computers & Education: Artificial Intelligence Submitted
  8. Y. Mao, B. Li, S. Chen, M. Gan, J. Qian. Let's chat about chatbots: exploring user preferences for e-commerce customer service chatbots through a discrete choice experiment. Behavior & Information Technology Submitted
  9. B. Li, Y. Mao, Y. Zhang, W. Niu, J. Qian. Coding their choices: assessing student preferences for statistical software through a discrete choice experiment. Journal of Computing in Higher Education Submitted
Teaching

Learning by playing.

I like teaching statistics through small, playable games. Abstract ideas like sampling variability or convergence become intuitive once you can watch them unfold and poke at them yourself, so I build little interactive demos that let students experiment with a concept before we formalize it.

Here are some I have made. More on the way.

Interactive demo
Law of Large Numbers
More data, closer to the truth.
Play on Astrocade

Courses

  • R Functions and Libraries (EBS1009), Maastricht UniversityInstructor
  • Writing a Master's Thesis Proposal (EBS4047), Maastricht UniversityInstructor
  • Research Methodology, Beijing Normal UniversityGuest Instructor

Workshops

  • Introduction to Discrete Choice Experiments, Maastricht UniversityInstructor

Master thesis supervision

  • Supervised 8 master's thesesMaastricht University
  • Thesis committee member for 8 studentsMaastricht University

Qualification

  • Certificate in Problem-based Learning (PBL) and Tutor TrainingMaastricht, 2024