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
  • Probabilistic Modelling
  • Deep Learning
  • Representation Learning
  • Meta Learning
  • Causal Inference
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

See also my Google Scholar profile.
Wenlin Chen, Samuel Horváth, Peter Richtárik (2022). Optimal Client Sampling for Federated Learning. TMLR 2022.

PDF Cite Code Video OpenReview arXiv

Wenlin Chen, Austin Tripp, José Miguel Hernández-Lobato (2022). Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction. arXiv preprint, arXiv:2205.02708.

PDF Cite arXiv

Austin Tripp, Wenlin Chen, José Miguel Hernández-Lobato (2022). An Evaluation Framework for the Objective Functions of De Novo Drug Design Benchmarks. ICLR 2022 Workshop on MLDD.

PDF Cite OpenReview

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

PDF Cite

Andrew Webb, Charles Reynolds, Wenlin Chen, Henry Reeve, Dan Iliescu, Mikel Luján, Gavin Brown (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
Department of Engineering, University of Cambridge
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
Department of Empirical Inference, Max Planck Institute for Intelligent Systems
Oct 2021 – Present Tübingen, Germany
Cambridge-Tübingen PhD Fellow in Machine Learning.
 
 
 
 
 
Research Assistant - Probabilistic and Causal Machine Learning
Department of Engineering, University of Cambridge
Jan 2021 – Sep 2021 Cambridge, England, United Kingdom
 
 
 
 
 
Research Intern - Federated Learning and Distributed Optimization
CEMSE, King Abdullah University of Science and Technology (KAUST)
Aug 2020 – Sep 2020 Remote
 
 
 
 
 
Research Assistant - Ensemble Deep Learning
Department of Computer Science, University of Manchester
Sep 2018 – Jun 2020 Manchester, England, United Kingdom
 
 
 
 
 
Research Engineer Intern - Deep Learning on Edge Devices
Imagination Technologies
Jun 2019 – Aug 2019 Kings Langley, England, United Kingdom

Awards

CUED PhD Studentship
Department of Engineering, University of Cambridge May 2021
Cambridge Trust Scholarship
Cambridge Commonwealth, European and International Trust Apr 2021
Cambridge-Tübingen PhD Fellowship in Machine Learning
University of Cambridge and Max Planck Institute for Intelligent Systems Feb 2021
The Royal Statistical Society Prize
The Royal Statistical Society (RSS) Jul 2020
International Mathematics Scholarship
Department of Mathematics, University of Manchester Aug 2019

Contact

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