OxML 2022
University of Oxford's St Catherine's College & Online
7-14 August, 2022
ML Fundamentals
27-29 June, 2022
Virtual

Based on the success of previous years' program, and in order to provide all participants with the necessary background -- particularly for those who are new to the theory and fundamentals of modern ML -- during this module, we aim to provide everyone with training in the following topics:
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Fundamentals of statistical / probabilistic ML
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Fundamentals of representation / deep learning
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Optimisation
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Mathematics of machine learning
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And more
ML Fundamentals Speakers
ML x HEALTH
7-10 August, 2022
Oxford St Catherine's College & Online

Building on the topics covered in ML fundamentals module, the Health module will continue and cover the following topics:
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Statistical / probabilistic ML (e.g., Bayesian ML, causal inference, approximate inference, modelling uncertainty, ...)
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Advanced topics in representation learning (e.g., learning with little or nor supervision, self-supervised learning, multi-modal representation learning, ...)
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Graph neural networks, and geometrical deep learning
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Computer vision
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Knowledge graphs
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Knowledge-aware ML
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Symbolic reasoning,
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Neuro-symbolic AI
Applied talks on ML in/for:
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EHR, imaging (e.g., brain, heart), genomics, multi-omics, ...
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Chronic noncommunicable diseases, infectious diseases, oncology, ...
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Drug discovery, and biopharma industry
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...
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Taking ML to the real-world settings (e.g., interpretability, ethics, ML Ops, ML products, ...)
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And more
ML x Health Speakers
ML x FINANCE
11-14 August, 2022
Oxford St Catherine's College & Online

Building on the topics covered in ML fundamentals module, the Finance module will continue and cover the following topics:
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Statistical / probabilistic ML (e.g., Bayesian ML, Gaussian processes, approximate inference, modelling uncertainty, learning from large data, ...)
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Advanced topics in representation learning (e.g., learning with no labels, representation learning in time series, text, and multi-modal data)
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Natural language processing (e.g., large language models, multi-lingual NLP, sentiment/opinion mining, fact checking / false news, misinformation detection, ...)
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Reinforcement learning
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Knowledge graphs
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Knowledge-aware ML
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Symbolic reasoning
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Neuro-symbolic AI
Applied talks on ML in/for:
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Financial time series (e.g., standard models, Gaussian processes, representation learning, ...)
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Building market simulators
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Trading and hedging
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Insurance, asset management, emerging risks
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Financial inclusion and economic prosperity
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ESG
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...
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Taking ML to the real-world settings (e.g., interpretability, ethics, ML Ops, ML products, ...)
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And more
ML x Finance Speakers
Supporting Team & TAs

Esther Beierl
Postdoctoral Research Associate, Trial Statistician, Data Scientist
University of Oxford

Meyad Golmakani
Computer Scientist
KCL & AI for Global Goals

Antoine Grosnit
ML Research Scientist
Huawei Technologies UK

Alexandre Maraval
Research Engineer
Huawei Technologies UK

Matthieu Zimmer
Senior Research Scientist
Huawei Technologies UK

Shahin Zibaee
Data/Structural Bioinformatics Scientist

Cristina Geva
King's College London

Runji Lin
CAS

Sian Jin
Washington State Uni.

Jiajia Tao
University College London

Xue Yan
CAS

Ziyan Wang
King's College London

Baixi Sun
Washington State Uni.

Anji Liu
UCLA

Chaorui Wang
University College London

Chengming Zhang
Washington State Uni.

Hang Lou
University College London
Zihao Wang
Peking University