معرفی
Mark van der Wilk is an Associate Professor in the Department of Computer Science at the University of Oxford and a Tutorial Fellow at Hertford College. His research focuses on machine learning, particularly Bayesian methods, equivariance in neural networks, and applications in science and industry. He leads a research group addressing challenges in generalization, architecture design, and efficient learning.
Education: PhD in Machine Learning from the University of Cambridge (2017), undergraduate degree in Engineering. Previously held academic positions at Imperial College London and worked in industry with Dr. James Hensman.
Research Interests: Automatic discovery of inductive biases, architecture optimization, Bayesian deep learning, causal inference, and applications in bioengineering and chemistry. Collaborates with industry and academia on practical machine learning solutions.
Awards: Best Paper Award at NeurIPS/ICML conferences, Qualcomm Innovation Fellowship (PhD). Active in open-source contributions, including GPflow and GPflux libraries.
Key Projects: Development of Competitive Amplification Networks for molecular diagnostics, turbulence testing for LLMs, and sparse Gaussian process approximations. Supervises PhD students focusing on alignment of mathematical and practical machine learning challenges.
