MACHINE LEARNING & SDGs

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The field of Machine Learning (ML), including Deep Learning (DL), has seen tremendous growth, in the past decade. While such developments have simultaneously led to both optimistic and pessimistic views on AI and the future of society/humanity, one thing everyone agrees on is that certain industries and domains have benefited from AI more than others.

 

On the other hand, the Global Goals (i.e., the 17 goals officially known as the UN Sustainable Development Goals, or SDGs) can be among the biggest beneficiaries of AI. According to a report by PwC, by 2030, the use of AI for environmental applications could contribute up to $5.2T to the global economy; it could also create 38.2 million net new jobs globally. Adding the impact to overlapping industries such as healthcare, finance, education and more, the size of this impact can be even bigger.

 

OxML aims to bring some of the best talents in machine learning together with the primary goal of providing them with world-class training in ML/DL, with the main objective of raising awareness about AI+SDG. Our goal is to train and inspire more scientists and engineers in ML to pursue research towards the pledge to leave no one behind, that lies at the core of  SDG’s. This year's program consists of two main modules: ML x Health (Aug 7-10), and ML x Finance (Aug 11-14); participants of both modules will have access to an ML Fundamentals module (June 27-29).

 

ML Fundamentals

27-29 June, 2022

Virtual

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Based on the success of last year's 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

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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

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Research Scientist

Google DeepMind 

Advanced topics in representation learning

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Javier González

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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

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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 Statistic

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

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