I am a 3rd-year PhD student in Machine Learning at Imperial College London, supervised by Yingzhen Li. I obtained my MPhil degree in Machine Learning and Machine Intelligence from the University of Cambridge in 2020, supervised by José Miguel Hernández-Lobato. Before that, I graduated with a BSc (Hons) in Mathematics and Statistics from McMaster University.

During my PhD, I mainly work on uncertainty estimation, robustness in large-scale deep learning models, and generative models. In general, I am also interested in sequence models and probabilistic model design. I aim to develop reliable algorithms with improved uncertainty estimation, generalization, and robustness.

From June to November 2023, I worked as a research intern in the Responsible AI team at ByteDance in London, where I researched on machine learning fairness.


An up-to-date list of publications can be found on my Google Scholar profile.

*equal contribution

  • Wenlong Chen*, Yegor Klochkov*, Yang Liu. Post-hoc Bias Scoring Is Optimal For Fair Classification. ICLR 2024 (spotlight) [link]
  • Wenlong Chen, Yingzhen Li. Calibrating Transformers via Sparse Gaussian Processes. ICLR 2023 [link]
  • Andrew Campbell*, Wenlong Chen*, Vincent Stimper*, Jose Miguel Hernandez-Lobato, Yichuan Zhang. A Gradient Based Strategy for Hamiltonian Monte Carlo Hyperparameter Optimization. ICML 2021 [link]


Feel free to reach out to me at wenlong.chen21 (at) imperial.ac.uk