Organised by AI for Global Goals

Oxford Machine Learning Summer School


20–21 July &
9–20 August

 
Virtual


 

 

The second Oxford machine learning summer school (OxML 2021), aims to provide its participants with best-in-class training on a broad range of advanced topics and developments in machine learning (ML) and deep learning (DL). The school will cover some of the most important topics in ML/DL that the field is showing a growing interest in (e.g., Bayesian ML, representation learning, computer vision, natural language processing (NLP), reinforcement learning, causal ML, and transfer learning).

This year, in addition to SDG #3 (healthcare/medicine), OxML 2021 will have additional focus areas like AI for good (e.g., climate action, emerging risks, sustainable cities, and more). This will enable us to cover a broader range of global goals from a machine learning perspective.

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The field of Machine Learning (including Deep Learning) has seen tremendous growth, in the past decade. Accumulation of large datasets, availability of affordable computing power at scale, and new developments in intelligent algorithms are known to be the key drivers of this growth in the field. The number of ML papers on arXiv has grown faster than Moore's law, surpassing 100 papers per day in 2018, and keeps growing. 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.

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 $5.2tn to the global economy; it could also create 38.2million 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 a special focus on  ML/DL used for Healthcare and Social Good (e.g.insurance and climate action). Our main objective is to raise awareness about AI+SDG, and train and inspire more scientists and engineers in ML to pursue research towards the SDG’s pledge to leave no one behind.

MACHINE LEARNING & SDGs
HEALTHCARE & SOCIAL GOOD

 
 

IMPORTANT DATES

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17 February 2021

Applications Open

30 April 2021

Applications Close

7 June 2021

Acceptance Notification

20–21 July: ML Fundamentals

9–20 August: OxML 2021

SPEAKERS

REPRESENTATION LEARNING & STATISTICAL ML

Yoshua Bengio

Université de Montreal, Mila, IVADO

Michael Bronstein

Imperial College London,

Twitter

Andrea Vedaldi

Oxford University, Facebook AI 

Melanie Mitchell

Santa Fe Institute

Cheng Zhang

Microsoft

James Hensman

Amazon

Robin Evans

Oxford University

Silvia Chiappa

DeepMind

Ali Eslami

DeepMind

NATURAL LANGUAGE PROCESSING (NLP)

Rada Mihalcea

University of Michigan

Andreas Vlachos

University of Cambridge

Sebastian Ruder

DeepMind

Yulan He

University of Warwick

Yue Zhang 

Westlake University

Luke Zettlemoyer

University of Washington

ML IN HEALTHCARE

Reza Khorshidi

Oxford University, AIG 

Narges Razavian

New York University

Russ Greiner

University of Alberta

Kazem Rahimi

Oxford University

Jorge Cardoso

King's College London

AI FOR GOOD

Adam Wierman
Thomas Dietterich
David Rolnick
Sohee Park
Naren Ramakrishnan

Caltech

Oregon State University

McGill University, Mila

Virginia Tech.

Ping An

Deniz Gunduz

Imperial College London

Daniele Magazzeni

J.P. Morgan,

Kings College London

Renyuan Xu

Oxford University

Jacob Abernethy

Georgia Institute of Technology

ML FUNDAMENTALS

Oana Cocarascu
Haitham Ammar 
Luo Mai
Yikuan Li

Kings College London

Huawei, UCL

University of Edinburgh

Oxford University

SPEAKERS

REPRESENTATION LEARNING & STATISTICAL ML

Yoshua Bengio

Université de Montreal, Mila, IVADO, CIFAR

Michael Bronstein
Andrea Vedaldi

Imperial College London,

Twitter

Oxford University, Facebook AI 

Melanie Mitchell

Santa Fe Institute

Cheng Zhang
Silvia Chiappa

DeepMind

Microsoft

James Hensman

Amazon

Robin Evans
Ali Eslami

Oxford University

DeepMind

NATURAL LANGUAGE PROCESSING (NLP)

Rada Mihalcea

University of Michigan

Andreas Vlachos

University of Cambridge

Sebastian Ruder

DeepMind

Yulan He

University of Warwick

Yue Zhang 

Westlake University

Luke Zettlemoyer

University of Washington

Pengfei Liu

Carnegie Mellon University

ML IN HEALTHCARE

Reza Khorshidi

Oxford University, AIG 

Narges Razavian

New York University

Russ Greiner

University of Alberta, CIFAR

Kazem Rahimi

Oxford University

Jens Rittscher 

Oxford University

Jorge Cardoso

King's College London

Lea Goetz

GSK.ai

AI FOR GOOD

Thomas Dietterich
David Rolnick
Adam Wierman
Naren Ramakrishnan

Caltech

McGill University, Mila

Oregon State University

Virginia Tech.

Dimitris Vlitas

Accenture 

University of Toronto

Jacob Abernethy

Georgia Institute of Technology

Renyuan Xu

Oxford University

Daniele Magazzeni

J.P. Morgan,

Kings College London

Deniz Gunduz

Imperial College London

ML FUNDAMENTALS

Oana Cocarascu
Haitham Ammar 
Luo Mai
Yikuan Li

Kings College London

Huawei, UCL

University of Edinburgh

Oxford University

PROGRAM 

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Rep. Learning & Statistical ML

ML in Healthcare

Natural Language Processing

AI for Good

 

PROGRAM COMMITEE

Mona Alinejad
General Chair

Founder and CEO,

AI for Global Goals

Thomas Nichols
Area Chair - Statistical ML

Professor of Neuroimaging Statistics University of Oxford

Reza Khorshidi
Program Chair

Investigator in ML & Medicine at the University of Oxford,

Chief Scientist at AIG 

Kazem Rahimi
Program Chair

Professor of Cardiovascular Medicine University of Oxford

Yu Yu
Area Chair - AI for Good

Director of Data Science at

BNY Mellon

Moez Draief
Area Chair - AI for Good

Chief Scientist & VP of Data Science/Engineering at Capgemini

Karo Moilanen
Area Chair - NLP

Global Head of NLP & Science Director at AIG 

Yaodong Yang
Area Chair - ML Fundamentals 

Assistant Professor at King's College London

 

SPONSORS

DIAMOND SPONSORS

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GOLD SPONSORS

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PARTNERS

OxML schools are organised by AI for Global Goals and in partnership with CIFAR and the University of Oxford's Deep Medicine Program

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CONTACT US

If you are interested in attending our schools, discuss sponsorship/partnership opportunities, or simply stay updated about our progress, please send us an e-mail, or subscribe to our mailing list, below.

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