About Me

I am a PhD student in Machine Learning at University of Cambridge (Machine Learning Group, Computational and Biological Learning Lab) and Max Planck Institute for Intelligent Systems (Department of Empirical Inference), under the Cambridge-Tübingen PhD Fellowship in Machine Learning. My supervisors are Professor José Miguel Hernández-Lobato, Professor Bernhard Schölkopf, and Dr Hong Ge.

I am keen on basic research in machine learning and its scientific applications. My research interest lies at the intersection of probabilistic methods, deep learning, and causal inference. I aim to develop efficient machine learning methods for robust prediction and realistic data generation.

Interests
  • Representation Learning
  • Generative Modeling
  • Multi-task/Meta Learning
  • Probabilistic Modeling
  • Causal Modeling
  • Optimization
  • AI for Science
Education
  • PhD in Machine Learning

    University of Cambridge and Max Planck Institute for Intelligent Systems

  • MPhil in Machine Learning and Machine Intelligence, Distinction, 2021

    University of Cambridge

  • BSc (Hons) in Mathematics, First Class, 2020

    University of Manchester

Publications

See also my Google Scholar profile or search on my publication page.
(2024). Diffusive Gibbs Sampling. arXiv preprint 2402.03008.

PDF Cite Code arXiv

(2023). It HAS to be Subjective: Human Annotator Simulation via Zero-shot Density Estimation. arXiv preprint 2310.00486.

PDF Cite Code arXiv

(2023). Leveraging Task Structures for Improved Identifiability in Neural Network Representations. ICML 2023 SCIS Workshop.

PDF Cite arXiv

(2022). Optimal Client Sampling for Federated Learning. TMLR 2022.

PDF Cite Code Video OpenReview arXiv

(2022). An Evaluation Framework for the Objective Functions of De Novo Drug Design Benchmarks. ICLR 2022 MLDD Workshop.

PDF Cite OpenReview

(2021). Causal Representation Learning for Latent Space Optimization. MPhil Thesis, University of Cambridge.

PDF Cite Cambridge

(2020). To Ensemble or Not Ensemble: When Does End-to-End Training Fail?. ECML 2020.

PDF Cite Code Video Springer Link arXiv

Experience

 
 
 
 
 
Research Intern - AI for Science
Mar 2024 – Present Haidian District, Beijing, China
Molecular Dynamics and Machine Learning Force Field
 
 
 
 
 
Doctoral Fellow - Machine Learning
Oct 2021 – Present Cambridge, England, United Kingdom
Cambridge-Tübingen PhD Fellow in Machine Learning
 
 
 
 
 
Doctoral Fellow - Machine Learning
Oct 2021 – Present Tübingen, Baden-Württemberg, Germany
Cambridge-Tübingen PhD Fellow in Machine Learning
 
 
 
 
 
Research Intern - Optimization
Aug 2020 – Sep 2020 Remote
Federated Learning and Distributed Optimization
 
 
 
 
 
Research Assistant - Machine Learning
Sep 2018 – Jun 2020 Manchester, England, United Kingdom
Ensemble Deep Learning
 
 
 
 
 
Research Engineer Intern - Machine Learning
Jun 2019 – Aug 2019 Kings Langley, England, United Kingdom
Efficient Deep Learning on Edge Devices

Awards

Cambridge-Tübingen PhD Fellowship in Machine Learning
The Royal Statistical Society Prize
International Mathematics Scholarship

Contact

  • wc337@cam.ac.uk
  • St Edmund’s College, Cambridge CB3 0BN, United Kingdom,