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Michael Hutchinson

Phd Student in Statistical Machine Learning at the University of Oxford

Statistics Department, University of Oxford

Hi I’m Michael! I’m interested in machine learning, particularly the Bayesian flavour.

Recently I have been working on Equivariance in Deep Learning, and various COIVD-19 statistical modelling efforts.

Previously I’ve worked on Architecture Search of Bayesian Neural Networks, and Differential Privacy for Federated and Continual Bayesian Learning.

Broadly I’m interested in statistically principled machine learning. I’m working on pushing the theoretical boundaries of this, and help make it useful in the real world!

My interests aren’t completely settled however, and I’m always keen to explore new areas. Reinforcement Learning is the next on my todo list.

I recently started a PhD course at the University of Oxford through the StatML course, supervised by Yee Whye Teh and Max Welling. Before that I completed a Masters of Engineering at the University of Cambridge, supervised by Dr Rich E. Turner.

Interests

  • Bayesian Machine Learning
  • Machine Learning with Guarantees
  • Climbing and Hockey
  • Reading Fiction and Philosophy
  • Making a positive difference in the world!

Education

  • PhD in Statistical Machine Learning, 2019-2023

    University College, University of Oxford

  • MEng in Information and Computer Engineering, 2018-2019

    Christs College, University of Cambridge

  • BA in Engineering, 2015-2018

    Christs College, University of Cambridge

Recent Posts

Machine Learning Summer School 2020

A collection of things related to me for, and things from, the 2020 Virtual MLSS

Differential Privacy, Approximate Bayesian Inference and Distributed Learning

Learning Private, Bayesian Machine Learning Models in the Federating Learning Context

Recent Publications

For a more complete list click here

LieTransformer: Equivariant self-attention for Lie Groups

Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural Processes

Age groups that sustain resurging COVID-19 epidemics in the United States

Technical Document 3: Effectiveness and Resource Requirements of Test, Trace and Isolate Strategies

Report 23: State-level tracking of COVID-19 in the United States