I regularly take PhD students. If you are interested in doing a PhD with me at LSE, please feel free to send me an email.

Contact Information

• Office:
  Columbia House, Room 5.16
  Houghton Street, London, WC2A 2AE
  
  

• Faculty webpage: Link

• Research lab website: Link

• Google scholar page: Link
  

• Email: y.chen186@lse.ac.uk
  
  

CURRICULUM VITAE

• Link
  
  

Educational Background

• PhD, Statistics, Columbia University in the city of New York, 2016
  (My Node on Math Genealogy Tree: Click Link)
  
  

Research interest

As a faculty in social statistics, my research aims at developing new statistical and machine learning methods and theory for SOCIAL DATA SCIENCE (in a nutshell, social data + data science). With the emerging of high-dimensional data in social sciences and due to the high noise level in such data, we need more computationally efficient algorithms and statistical inference methods to make more reliable and reproducible findings. I run a psychometric lab with Professor Irini Moustaki at LSE. Most of my recent works on psychometrics are with members of this lab. I also work with other colleagues at LSE as well as external collaborators on statistical and machine learning theory and methods.

Some recent research interests and selected publications.

• Factor models for high-dimensional matrices, tensors, and counting processes, with applications to large-scale educational and psychological measurement, political voting, recommendation systems (in marketing), and social media data. I work on both standard linear and non-linear factor models (also called generalised latent factor models), as well as multi-layer factor models (which becomes a deep neural network!). Selected publications on this topic:
  
  

  • Chen, Y., Li, X., and Zhang, S. (2019). Joint Maximum Likelihood Estimation for High-dimensional Exploratory Item Response Analysis. Psychometrika. 84, 124-146.

  • Chen, Y., Li, X., and Zhang, S. (2020). Structured Latent Factor Analysis for Large-scale Data: Identifiability, Estimability, and Implications. Journal of the American Statistical Association. 115, 1756-1770.

  • Zhang, H., Chen, Y. and Li, X. (2020). A Note on Exploratory Item Factor Analysis by Singular Value Decomposition. Psychometrika. 85, 358-372.

  • Chen, Y., Ying, Z. and Zhang, H. (2021). Unfolding-Model-Based Visualisation: Theory, Method and Applications. Journal of Machine Learning Research. 22, 1-51.

  • Chen, Y. and Li, X. (2022). Determining the Number of Factors in High-dimensional Generalised Latent Factor Models. Biometrika. 109, 769–782.

  • Chen, Y., Lu, Y., and Moustaki, I. (2022). Detection of Two-way Outliers in Multivariate Data and Application to Cheating Detection in Educational Tests. Annals of Applied Statistics, 16, 1718-1746.

  • Chen, Y., Li, C., Ouyang, J, and Xu, G. (2023). A Note on Statistical Inference for Noisy Incomplete 1-Bit Matrix. Journal of Machine Learning Research. 24, 1-66.

  • Liu, X., Wallin, G., Chen, Y. and Moustaki, I. (2023). Rotation to Sparse Loadings using Lp Losses and Related Inference Problems. Psychometrika. 88, 527–553.

  • Chen, Y., Li, C., Ouyang, J. and Xu, G. (2023). DIF Statistical Inference without Knowing Anchoring Items. Psychometrika. 88, 1097 - 1122.

  • Chen, Y., Li, X., Liu, J. and Ying, Z. (2024+). Item Response Theory – A Statistical Framework for Psychological Measurement: Past Developments and Future Directions(with discussions). Statistical Science. To appear.

  • Wallin, G., Chen, Y. and Moustaki, I. (2024). DIF Analysis with Unknown Groups and Anchor Items. Psychometrik. 89, 267–295.

  • Xie, Z., Chen, Y., von Davier, M., and Weng, H. (2024+). Variable Selection in Latent Regression IRT Models via Knockoffs: An Application to International Large-scale Assessment in Education. Journal of the Royal Statistical Society, Series A (Social Statistics). To appear.

  • Chen, Y. and Li, X. (2024+). A Note on Entrywise Consistency for Mixed-data Matrix Completion. Journal of Machine Learning Research. To appear.

• Parallel stream change detection and compound decision theory, with applications to item pool quality control in high-stake educational testing, finance, recommendation systems (in marketing), and signal processing in engineer. This line of research concerns multi-stream data, where each data stream (e.g., the price of one stock) may have its own change point. The goal is to detect the stream-specific change points with a controlled compound risk (e.g., false discovery/non-discovery rate). This is a new research area, as traditional change detection focuses on detecting one or multiple sequential changes, rather than parallel changes. Selected publications on this topic:
  
  

  • Chen, Y., Lee, Y-H, and Li, X. (2022). Item Quality Control in Educational Testing: Change Point Model, Compound Risk, and Sequential Detection. Journal of Educational and Behavioral Statistics. 47, 322–352.

  • Lu, Z., Chen, Y. and Li, X. (2022). Optimal Parallel Sequential Change Detection under Generalized Performance Measures. IEEE Transactions on Signal Processing. 70, 5967-5981.

  • Chen, Y. and Li, X. (2023). Compound Online Changepoint Detection in Parallel Data Streams. Statistica Sinica. 33, 453-474.
    
    

• Sequential decision theory, with applications to personalised learning, crowdsourcing, and computerised adaptive testing. This line of research combines Markov decision theory, reinforcement learning and statistical inference methods. Selected publications on this topic:
  
  

  • Chen, Y., Li, X., Liu, J, and Ying, Z. (2018). Recommendation System for Adaptive Learning. Applied Psychological Measurement. 42, 24-41.

  • Tang, X., Chen, Y., Li, X., Liu, J. and Ying, Z. (2019). A Reinforcement Learning Approach to Personalized Learning Recommendation System. British Journal of Mathematical and Statistical Psychology. 72, 108-135.

  • Li, X., Chen, Y., Chen, X., Liu, J. and Ying, Z. (2021). Optimal Stopping and Worker Selection in Crowdsourcing: An Adaptive Sequential Probability Ratio Test Framework. Statistica Sinica. 31, 519-546.

  • Chen, X., Chen, Y. and Li, X. (2022). Asymptotically Optimal Sequential Design for Rank Aggregation. Mathematics of Operations Research. 47, 2310-2332.
    
    

Publications

See my CV (link) or google scholar page (link).

Professional and Editorial Work

• Member of the editorial council of the Psychometric Society, 2024 - 2030

• Associate editor of British Journal of Mathematical and Statistical Psychology, 2022 -

• Associate editor of Psychometrika, 2019 -

• Editorial board member of Journal of Educational and Behavioural Statistics, 2021 -

• Editorial board member of Applied Psychological Measurement, 2017 -
  
  

Honors

• 2024 Psychometrics Society Best Reviewer Award
  

• 2022 Psychometrics Society Early Career Award
  

• 2020 AERA Outstanding Reviewer Award
  

• 2018 NCME Brenda H. Loyd Outstanding Dissertation Award (Photo)
  

• 2018 National Academy of Education/Spencer Postdoctoral Fellow (link)
  
  

Funding

• IEA (International Association for the Evaluation of Educational Achievement) Research and Development Funds, 2022-2023

• National Academy of Education/Spencer Postdoctoral Fellowship, 2018-2020
  
  

Experience

• Intern at Educational Testing Service, supervised by Dr. Matthias von Davier, June to July 2014
  

• Visiting Scholar at Shanghai Center for Mathematical Sciences, May to July 2016
  

• Assistant Professor in Department of Psychology and Institute for Quantitative Theory and Methods, Emory University, August 2016 - August 2018