OxML 2022
University of Oxford's St Catherine's College
7-14 August, 2022

 

ML Fundamentals

27-29 June, 2022

Virtual

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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:

  • Fundamentals of statistical / probabilistic ML

  • Fundamentals of representation / deep learning

  • Optimisation

  • Mathematics of machine learning

  • And more

ML Fundamentals Speakers 

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Hao Ni

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Alan Turing Institute's Fellow 

Associate Professor at UCL

ML Maths (from linear regression to DL)
 

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Haitham Ammar

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Assistant Professor at UCL 

RL Team Leader at Huawei

Fundamentals of Stat./Bayesian/Prob. ML

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Yali Du

  • G
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Assistant Professor 

King's College London

Optimisation methods in ML
 

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Dingwen Tao

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Assistant Professor 

Washington State University

ML Systems, computational graph, Tensorflow, & PyTorch
 

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Yitao Liang

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Assistant Professor 

UCLA & Peking University

Neuro-symbolic AI, & tractable prob. models

ML x HEALTH

7-10 August, 2022

Oxford St Catherine's College & Online

Brain Scans
 

Building on the topics covered in ML fundamentals module, the Health module will continue and cover the following topics:

  • Statistical / probabilistic ML (e.g., Bayesian ML, causal inference, approximate inference, modelling uncertainty, ...)

  • Advanced topics in representation learning (e.g., learning with little or nor supervision, self-supervised learning, multi-modal representation learning, ...)

  • Graph neural networks, and geometrical deep learning

  • Computer vision

  • Knowledge graphs

  • Knowledge-aware ML

  • Symbolic reasoning,

  • Neuro-symbolic AI

Applied talks on ML in/for:

  • EHR, imaging (e.g., brain, heart), genomics, multi-omics, ...

  • Chronic noncommunicable diseases, infectious diseases, oncology, ...

  • Drug discovery, and biopharma industry

  • ...

  • Taking ML to the real-world settings (e.g., interpretability, ethics, ML Ops, ML products, ...)

  • And more

ML x Health Speakers 

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Michael Bronstein

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DeepMind Professor of AI

University of Oxford & Twitter

Geometric deep learning

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Mireia Crispin

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Lecturer in Integrated Cancer Medicine

University of Cambridge

ML, multi-omics, and Oncology

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Kazem Rahimi

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Professor of Cardiovascular Medicine 

University of Oxford

ML for population health, and chronic diseases

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Ali Eslami

  • G
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Research Scientist

Google DeepMind 

Advanced topics in representation learning

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Javier González

  • G
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Principal Researcher

Microsoft Research

Statistical / probabilistic ML, causal inference

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Reza Khorshidi

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Program lead, ML & Medicine

University of Oxford

ML for EHR, and ML products

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Sonali Parbhoo

  • G
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Research Fellow

Harvard/Imperial College

Reasoning in uncertainty, and ML Interpretability

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Jorge Cardoso

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Senior Lecturer in AI

King's College London

ML & medical imaging

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Ishan Misra

  • Google Scholar
  • Webpage

Research Scientist 

Facebook AI Research (FAIR)

ML, computer vision, & learning with reduced supervision

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Vincent Moens

  • Google Scholar
  • Webpage

ML Research Scientist 

Meta

ML Ops, Pytorch, 

DL architectures

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Hoifung Poon

  • G
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Senior Director of Biomedical NLP

Microsoft

Biomedical NLP, and knowledge-aware ML

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Vaishak Belle

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Chancellor’s Fellow & Faculty

University of Edinburgh

Symbolic reasoning, Neuro-symbolic AI

ML x FINANCE

11-14 August, 2022

Oxford St Catherine's College & Online

Stock Market Down
 

Building on the topics covered in ML fundamentals module, the Finance module will continue and cover the following topics:

  • Statistical / probabilistic ML (e.g., Bayesian ML, Gaussian processes, approximate inference, modelling uncertainty, learning from large data, ...)

  • Advanced topics in representation learning (e.g., learning with no labels, representation learning in time series, text, and multi-modal data)

  • Natural language processing (e.g., large language models, multi-lingual NLP, sentiment/opinion mining, fact checking / false news, misinformation detection, ...)

  • Reinforcement learning

  • Knowledge graphs

  • Knowledge-aware ML

  • Symbolic reasoning

  • Neuro-symbolic AI

Applied talks on ML in/for:

  • Financial time series (e.g., standard models, Gaussian processes, representation learning, ...)

  • Building market simulators

  • Trading and hedging

  • Insurance, asset management, emerging risks

  • Financial inclusion and economic prosperity

  • ESG

  • ...

  • Taking ML to the real-world settings (e.g., interpretability, ethics, ML Ops, ML products, ...)

  • And more

ML x Finance Speakers 

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Rama Cont

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Professor of Mathematical Finance 

University of Oxford

Quantitative finance, ML, and market simulation

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Stefan Zohren

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Deputy Director

Oxford-Man Institute

Representation learning  & (financial) time series

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Yulan He

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Professor of Computer Science

University of Warwick

NLP, Sentiment/opinion mining

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James Hensman

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  • Webpage

Principal Scientist

Amazon

Probabilistic ML, GPs, (financial) time series 

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Mihai Cucuringu

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Associate Professor of Statistics

University of Oxford

Networks, Statistical ML, and quantitative finance

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Kalesha Bullard

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Research Scientist 

DeepMind

Cooperative AI

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Sebastian Ruder

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Senior Research Scientist

Google

Multilingual NLP

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Ben Wood

  • LinkedIn
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Global Co-Head, Equity Derivatives Quant Research

JPMorgan Chase

ML and derivatives trading, deep hedging

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Renyuan Xu

  • Google Scholar
  • Webpage

Assistant Professor

USC

ML for financial markets, RL

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Thomas Spooner

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  • Webpage

Venture Capitalist

Sutter Hill Ventures

Reinforcement learning in finance 

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Svetlana Bryzgalova

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Assistant Professor of Finance

London Business School

ML, capital markets and  factor investing

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Arno Solin

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Assistant Professor of ML

Aalto University 

Probabilistic ML, GPs, (financial) time series 

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Alessandro Oltramari

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Senior Research Scientist

Bosch

Neuro-Symbolic Systems

Supporting Team & TAs

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Esther Beierl

Postdoctoral Research Associate, Trial Statistician, Data Scientist

University of Oxford

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Meyad Golmakani

Computer Scientist

KCL & AI for Global Goals

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Antoine Grosnit

ML Research Scientist

Huawei Technologies UK

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Alexandre Maraval

Research Engineer 

Huawei Technologies UK

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Matthieu Zimmer

Senior Research Scientist

Huawei Technologies UK

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Shahin Zibaee

Data/Structural Bioinformatics Scientist

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Cristina Geva

King's College London

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Runji Lin

CAS

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Sian Jin

Washington State Uni.

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Jiajia Tao

University College London

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Xue Yan

CAS

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Ziyan Wang

King's College London

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Baixi Sun

Washington State Uni.

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Anji Liu

UCLA

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Chaorui Wang

University College London

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Chengming Zhang

Washington State Uni.

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Hang Lou

University College London

Zihao Wang

Peking University