Oxford Machine Learning Summer School

OxML 2021

9-20 August, 2021

 

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

17 February 2021

Applications Open

30 April 2021

Applications Close

31 May 2021

Acceptance Notification

9-20 August 2021

OxML summer school

SPEAKERS

More speakers will be announced soon...

PROGRAM 

Below is the provisional program of OxML 2021 school; some minor changes are likely.

 

ORGANISERS

Mona Alinejad
General Chair

Founder and CEO,

AI for Global Goals

Thomas Nichols
Area Chair - Statistical ML

Professor of Neuroimaging Statistics at Oxford University

Reza Khorshidi
Program Chair

Investigator in ML & Medicine at Oxford University,

Chief Scientist at AIG 

Kazem Rahimi
Program Chair

Professor of Cardiovascular Medicine at Oxford University

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 

Research Scientist at Huawei UK

 

PARTNERS

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CONTACT US

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