About Me

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

I am keen on basic research in machine learning and its scientific applications (e.g., drug discovery). My research interest lies at the intersection of probabilistic methods, deep learning, and causal inference. I aim to develop data-efficient machine learning methods that enable active data collection, robust inference and prediction, efficient data compression, and realistic generation of novel synthetic data.

Interests
  • Deep Generative Modelling
  • Bayesian Deep Learning
  • Representation Learning
  • Meta Learning
  • Causal Inference
  • Federated Learning
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 in Mathematics, First Class Honours, 2020

    University of Manchester

Publications

(2022). Meta-learning Feature Representations for Adaptive Gaussian Processes via Implicit Differentiation. arXiv preprint, arXiv:2205.02708.

PDF Cite arXiv

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

PDF Cite OpenReview

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

PDF Cite

(2020). Optimal Client Sampling for Federated Learning. NeurIPS 2020 Workshop on PPML.

PDF Cite Code Video arXiv

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

PDF Cite Code Video Springer Link arXiv

Experience

 
 
 
 
 
Doctoral Fellow - Machine Learning
Oct 2021 – Present Cambridge, England, United Kingdom
Cambridge-Tübingen PhD Fellow in Machine Learning, funded by a Cambridge Trust Scholarship and a CUED PhD Studentship.
 
 
 
 
 
Doctoral Fellow - Machine Learning
Oct 2021 – Present Tübingen, Germany
Cambridge-Tübingen PhD Fellow in Machine Learning.
 
 
 
 
 
Research Assistant - Probabilistic and Causal Machine Learning
Jan 2021 – Sep 2021 Cambridge, England, United Kingdom

Worked on two projects:

 
 
 
 
 
Research Intern - Federated Learning and Distributed Optimization
Aug 2020 – Sep 2020 Remote
Worked on communication-efficient and privacy-preserving distributed optimization algorithms for machine learning. Our research paper manuscript Optimal Client Sampling for Federated Learning has been accepted for presentation at NeurIPS 2020 Workshop on Privacy Preserving Machine Learning.
 
 
 
 
 
Research Assistant - Ensemble Deep Learning
Sep 2018 – Jun 2020 Manchester, England, United Kingdom
Worked on the EPSRC LAMBDA research project. Our research paper To Ensemble or Not Ensemble: When does End-To-End Training Fail? has been accepted for publication at ECML 2020.
 
 
 
 
 
Research Engineer Intern - Deep Learning on Edge Devices
Jun 2019 – Aug 2019 Kings Langley, England, United Kingdom
Deployed deep learning models to Neural Network Accelerators.

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, Cambridgeshire CB3 0BN, United Kingdom