Final call for application -- Oxford ML Summer School (OxML 2021)

[Reza Khorshidi]

Dates: August 9-20, 2021 (virtual)

Application deadline: 30 April, 2021

For more info, please visit the school’s website: www.oxfordml.school

About OxML 2021

• OxML schools have a special focus on AI and SDG; in addition to theoretical ML lectures, there will be lectures on the applications of ML in SDGs.
• OxML 2021 is organised by AI for Global Goals, in partnership with CIFAR and The University of Oxford’s Deep Medicine Program.
• During OxML 2021, participants will learn about advanced topics in statistical/probabilistic ML, representation learning, causal ML, geometric DL, natural language processing, computer vision, and more, plus their applications in medicine [SDG3] and for social good.
• In order to provide the school's diverse participants with the necessary background for the advanced topics in ML/DL, the school will also include two days of lectures on ML fundamentals (during the onboarding week, i.e., Jul 19-21).

The Speakers

Below is the list of our confirmed speakers to date — more speakers will be added in the coming weeks:

• Yoshua Bengio (Mila, IVADO) — causal representation learning 
• Michael Bronstein (Imperial College, Twitter) — geometrical deep learning 
• Andrea Vedaldi (University of Oxford, Facebook AI) — representation learning and computer vision 
• Ali Eslami (DeepMind) — advanced topics in representation learning 
• Robin Evans (University of Oxford) — probabilistic causal ML
• Cheng Zhang (Microsoft Research) — Bayesian ML
• James Hensman (Amazon) — Gaussian processes
• Sebastian Ruder (DeepMind) — multi-lingual NLP
  
• Andreas Vlachos (University of Cambridge) — fact-checking, and misinformation detection 
• Luke Zettlemoyer (University of Washington) — large-scale language models
• Yue Zhang (Westlake University) — common-sense reasoning 
• Yulan He (University of Warwick) — sentiment/opinion mining 
• Kazem Rahimi (University of Oxford) — ML in medicine (electronic health records)
• Reza Khorshidi (University of Oxford, AIG) — ML in medicine (electronic health records)
• Narges Razavian (New York University) — ML in medicine (electronic health records) 
• Renyuan Xu (University of Oxford)  — ML in financial services 
• Adam Wierman (Caltech) — ML for energy efficiency
• Thomas Dietterich (Oregon State University) — computational sustainability 
• Deniz Gündüz (Imperial College) — ML for energy efficiency 
• David Rolnick (Mila, McGill) — ML for climate action 
• Jacob Abernethy (Georgia Institute of Technology) — ML for water resources 
• Haitham Ammar (Huawei, UCL) — ML fundamentals 
• Oana Cocarascu (Kings College) — ML fundamentals 
  
• Luo Mai (University of Edinburgh) — ML fundamentals 
• Sohee Park (Ping An) – ML for ESG investments 
• Yikuan Li (University of Oxford) — ML fundamentals 

Target audience 

• Everyone is welcome to apply to OxML 2021 regardless of their origin, nationality, and country of residence. 
• Our target audience are (1) PhD students with a good technical background whose research topics are related to ML, plus (2) researchers and engineers in both academia and industry with similar/advanced levels of technical knowledge.  
• All applicants are subject to a selection process; we aim to select strongly motivated participants, who are interested in broadening their knowledge of the advanced topics in the field of ML/DL and their applications.

Contacting Us 

For any queries, you can contact us using this email address: con...@oxfordml.school

Best,

—

Reza Khorshidi, D.Phil. (Oxon)

Deep Medicine Program, The University of Oxford

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