OxML 2021
9-18 Aug, 2021
AI for Global Goals, in partnership with CIFAR and the University of Oxford's Deep Medicine program successfully organised a number of schools in 2021, including
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ML Fundamentals 2021
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ML in Health & Medicine
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ML for Social Good 2021
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The schools covered multiple topics such as:
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Representation learning, vision, NLP, reinforcement learning, …
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Statistical ML, bayesian ML, Gaussian processes, causal ML, …
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Medical imaging, EHR, genomics, …
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And more topics in finance, sustainability, …
These schools were very competitive, and we only could accept the top ~5% of the applicants we received from 118 countries, which led to
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500 candidates from 60 countries
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42% female; 53% from underrepresented countries in AI
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77% from academia (students and staff/postdocs/faculty); 98% with a postgrad degree.
9-18 August, 2021
PROGRAM COMMITEE 2021
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
Oxford Machine Learning summer school (OxML 2021) gathered some of the world-renowned professors and scientists (incl. Prof Bengio, a Turing award winner) to deliver the best-in-class ML courses for OxML participants. The school consisted of three main tracks, each covering an extensive range of topics:
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Representation learning and statistical ML:
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​Causal representation learning
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Geometrical deep learning
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RL and computer vision
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Advanced topics in representation learning
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Probabilistic causal ML
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Bayesian ML
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Gaussian processes
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epresentation learning for causal inference
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NLP:
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​Multi-lingual NLP​
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Bias & ethics in NLP
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Fact-checking & misinformation detection
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Large-scale language models
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Common-sense reasoning
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Sentiment/opinion mining
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Scientific reviewing
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ML in healthcare:
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​​ML for survival and hazard models
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ML for medical imaging
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ML for Electronic Health Records (EHR)
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Computational Pathology
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REPRESENTATION LEARNING & STATISTICAL ML SPEAKERS
Yoshua Bengio
Université de Montreal, Mila, IVADO, CIFAR
Michael Bronstein
Imperial College London,
Andrea Vedaldi
Oxford University, Facebook AI
Melanie Mitchell
Santa Fe Institute
James Hensman
Amazon
Cheng Zhang
Microsoft
Ali Eslami
DeepMind
Robin Evans
Oxford University
Silvia Chiappa
DeepMind
NATURAL LANGUAGE PROCESSING (NLP) SPEAKERS
Rada Mihalcea
University of Michigan
Luke Zettlemoyer
University of Washington
Sebastian Ruder
DeepMind
Yulan He
University of Warwick
Andreas Vlachos
University of Cambridge
Yue Zhang
Westlake University
Pengfei Liu
Carnegie Mellon University
ML IN HEALTHCARE SPEAKERS
Kazem Rahimi
Oxford University
Jorge Cardoso
King's College London
Reza Khorshidi
Oxford University, AIG
Jens Rittscher
Oxford University
Russ Greiner
University of Alberta, CIFAR
Narges Razavian
New York University
Lea Goetz
GSK.ai
AI FOR SOCIAL GOOD 2021
AI for social good 2021, offered a series of lectures by some of the top scientists and industry leaders on the application of machine learning for SDGs such as:
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ML in financial services
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ML for climate action
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ML for energy efficiency
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Computational sustainability
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ML for water resources
AI FOR SOCIAL GOOD SPEAKERS
Thomas Dietterich
Adam Wierman
Naren Ramakrishnan
David Rolnick
Renyuan Xu
Oregon State University
Caltech
McGill University, Mila
Virginia Tech.
Oxford University
Deniz Gunduz
Imperial College London
Dimitris Vlitas
Accenture
University of Toronto
Daniele Magazzeni
J.P. Morgan,
Kings College London
Jacob Abernethy
Georgia Institute of Technology
ML FUNDAMENTALS 2021
20-21 JULY, 2021
ML fundamentals 2021 aimed to provide those participants who didn't have the necessary Computer Science/Stats backgrounds or just needed to refresh their memory, with an in depth introduction to the ML basics. Some of the topics covered during this track were:
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Deep Learning Basics: Deep Neural Net, Resnet, Transformer, …
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ML and Data Science Basics: Linear regression, Logistic regression,PCA, Boosting, …
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ML system basics: Automatic differentiation, Accelerators, Computational Graph, Graph Optimisation, Distributed Training, Model Serving, ...
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Probabilistic Modelling Basics: Bayesian stats, VAE, GP, Bayesian Optimisation, ...
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ML FUNDAMENTALS SPEAKERS
Oana Cocarascu
Haitham Ammar
Luo Mai
Yikuan Li
Kings College London
Huawei, UCL
University of Edinburgh
Oxford University
2021 SPONSORS
