Connectionists: Speakers announcement -- Oxford ML Summer School (OxML 2022)

[Reza Khorshidi]

Dates: August 7-14, 2022 (St Catherine's College <https://www.catzconferences.com/venues/bernard-sunley-building>, Oxford + Virtual)
For more info, please visit the school’s website: www.oxfordml.school <http://www.oxfordml.school/>

Target audience 
Everyone is welcome to apply to OxML 2022 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.

Application
You can find the link to the school's application form here <https://forms.gle/EqvC3qxKmoGKJgGs5>: https://forms.gle/EqvC3qxKmoGKJgGs5 <https://forms.gle/EqvC3qxKmoGKJgGs5>.
Application deadline is 15 April, 2022.
Given the overwhelming number of applications we receive, the application portal may close earlier than the deadline if the number of applications exceeds our capacity to review.

The Speakers
Below is the list of our confirmed speakers to date — we will announce additional (i.e., ~15) speakers, as well as the school's workshops, in the coming weeks (follow the updates via the school’s website <https://www.oxfordml.school/>, or Twitter <https://twitter.com/GlobalGoalsAI> and LinkedIn <https://www.linkedin.com/company/ai-for-global-goals/> accounts). Note that, participants of both xHealth and xFinance modules will have access to / can attend the ML Fundamentals module.

ML x Healthcare
Michael Bronstein <https://scholar.google.co.uk/citations?user=UU3N6-UAAAAJ&hl=en> (University  of Oxford) — Geometric deep learning
Mireia Crispin <https://scholar.google.com/citations?user=TRZzLJgAAAAJ&hl=en> (University  of Cambridge) — ML, multi-omics, and oncology
Kazem Rahimi <https://scholar.google.com/citations?user=5u7TxAMAAAAJ&hl=en> (University of Oxford) — ML for population health, and chronic diseases
Ali Eslami <http://arkitus.com/research/> (DeepMind) — Advanced topics in representation learning 
Ishan Misra <https://scholar.google.com/citations?user=WvufSLAAAAAJ&hl=en> (Facebook AI Research) — ML, computer vision, and learning with reduced supervision
Javier Gonzalez <https://scholar.google.com/citations?user=ynIWXnUAAAAJ&hl=en> (Microsoft research) — Statistical / probabilistic ML, causal inference
Reza Khorshidi  <https://scholar.google.co.uk/citations?user=2cWcY-MAAAAJ&hl=en>(University of Oxford) — ML for Electronic Health Records 
Sonali Parbhoo <https://scholar.google.ch/citations?user=FwEz5s4AAAAJ&hl=en> (Imperial College  London, Harvard) — Reasoning in uncertainty, and ML Interpretability
Jorge Cardoso  <https://scholar.google.com/citations?user=BuJuSqkAAAAJ&hl=en>(King’s College London) — ML for medical imaging
Vincent Moens <https://scholar.google.com/citations?user=8l-tvFoAAAAJ&hl=en> (Meta) — ML Ops, PyTorch, DL software architectures

ML x Finance
Rama Cont <https://scholar.google.com/citations?user=stlWUqcAAAAJ&hl=en> (University of Oxford) — Quantitative finance, ML for building market simulators
Stefan Zohren <https://scholar.google.co.uk/citations?user=mtNQD-8AAAAJ&hl=en> (University of Oxford) — Representation learning  & (financial) time series
Yulan He <https://scholar.google.co.uk/citations?user=SP9r32UAAAAJ&hl=en> (University of Warwick) — Sentiment/opinion mining NLP
Sebastian Ruder  <https://scholar.google.com/citations?user=8ONXPV8AAAAJ&hl=en>(DeepMind) — Multi-lingual NLP 
James Hensman <https://scholar.google.co.uk/citations?user=l8dX3ssAAAAJ&hl=en> (Amazon) — Probabilistic ML, Gaussian processes, (financial) time series
Kalesha Bullard <https://scholar.google.com/citations?user=QehMdGIAAAAJ&hl=en> (DeepMind) — Cooperative AI
Mihai Cucuringu <https://scholar.google.co.uk/citations?user=GFvVRzwAAAAJ&hl=en> (University of Oxford) — Networks, statistical ML, and quant. finance
Ben Wood <https://www.researchgate.net/profile/Ben-Wood-5> (JP Morgan chase) — ML and derivatives trading, deep hedging
Thomas Spooner <https://scholar.google.com/citations?user=PaIXL6AAAAAJ&hl=en> (Sutter Hill Ventures) — Reinforcement learning in finance

ML Fundamentals
Haitham Ammar <https://scholar.google.com/citations?user=AE5suDoAAAAJ&hl=en> (UCL, and Huawei) — Fundamentals of Stat./Bayesian/Probabilistic ML
Hao Ni  <https://scholar.google.co.uk/citations?user=VTTtSLcAAAAJ&hl=en>(Turing Institute, and UCL) — ML Maths (from linear regression to DL)
Yali Du  <https://scholar.google.com/citations?hl=en&user=WMlPkOoAAAAJ>(King's College London) — Optimisation methods in ML
Dingwen Tao <https://scholar.google.com/citations?user=Ppjzn_EAAAAJ&hl=en> (Washington State University) — ML Systems, computational graph, Tensorflow, & PyTorch
Yitao Liang <https://scholar.google.com.au/citations?user=k2RheI8AAAAJ&hl=en> (UCLA, and Peking University) — Neuro-symbolic AI, & tractable prob. models

About OxML 2022
OxML is organised by AI for Global Goals <https://www.globalgoals.ai/>, in partnership with CIFAR <https://cifar.ca/> and The University of Oxford’s Deep Medicine <http://deepmedicine.medsci.ox.ac.uk/> Program.
OxML schools have a special focus on ML and SDG <https://sustainabledevelopment.un.org/>s. That is, in addition to theoretical ML lectures, there will be lectures on the application of ML in various SDGs areas.
OxML 2022 will have two separate 4-day schools: (1) ML x Health, and (2) ML x Finance. 
Furthermore, 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 --  the program will also have an online ML Fundamentals module (June 27-29), which will be open to both schools' accepted participants.
The schools will take place in St Catherine's College <https://www.stcatz.ox.ac.uk/>, Oxford (UK). There will also be a virtual option for those who cannot (or prefer not to) travel to Oxford, UK.
During each school, in addition to applied and theoretical lectures (taking place in the main hall, with ~250 seats), there will be multiple workshops and sessions on Advanced ML topics, ML Ops, ML Products, and ML Career (taking place in the 4x smaller halls, that have ~50-100 seats).
We aim to host ~200 participants in person (plus 100-200 virtually) in each school. Note that, while our current plan is to have a hybrid format, in the worst case COVID scenarios, we have (and are ready to execute) a plan B to go fully virtual. 

For any queries, you can contact us using this email address: contact at oxfordml.school <mailto:contact at oxfordml.school>
Best,
—
Reza Khorshidi, D.Phil. (Oxon)
Deep Medicine Program, The University of Oxford
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