<html><head><meta http-equiv="Content-Type" content="text/html; charset=utf-8"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class=""><div data-pm-slice="0 1 []" data-en-clipboard="true" class="">Dates: August 7-14, 2022 (<a href="https://www.catzconferences.com/venues/bernard-sunley-building" rev="en_rl_none" class="">St Catherine's College</a>, Oxford + Virtual)</div><div class="">For more info, please visit the school’s website: <a href="http://www.oxfordml.school" class="">www.oxfordml.school</a></div><div class=""><br class=""></div><div class=""><span data-markholder="true" class=""></span></div><div class=""><b class="">Target audience </b></div><ul class=""><li class="">Everyone is welcome to apply to OxML 2022 regardless of their origin, nationality, and country of residence. </li><li class="">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. </li><li class="">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.</li></ul><div class=""><span data-markholder="true" class=""></span></div><div class=""><b class=""><br class=""></b></div><div class=""><b class="">Application</b></div><div class="">You can find the link to the school's application form <a href="https://forms.gle/EqvC3qxKmoGKJgGs5" rev="en_rl_none" class="">here</a>: <a href="https://forms.gle/EqvC3qxKmoGKJgGs5" rev="en_rl_none" class="">https://forms.gle/EqvC3qxKmoGKJgGs5</a>.</div><div class="">Application deadline is 15 April, 2022. </div><div class="">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.</div><div class=""><span data-markholder="true" class=""></span></div><div class=""><b class=""><br class=""></b></div><div class=""><b class="">The Speakers</b></div><div class="">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 <a href="https://www.oxfordml.school" rev="en_rl_none" class="">school’s website</a>, or <a href="https://twitter.com/GlobalGoalsAI" rev="en_rl_none" class="">Twitter</a> and <a href="https://www.linkedin.com/company/ai-for-global-goals/" rev="en_rl_none" class="">LinkedIn</a> accounts). Note that, participants of both xHealth and xFinance modules will have access to / can attend the ML Fundamentals module.</div><div class=""><br class=""></div><div class=""><i class="">ML x Healthcare</i></div><ul class=""><li class=""><a href="https://scholar.google.co.uk/citations?user=UU3N6-UAAAAJ&hl=en" rev="en_rl_none" class="">Michael Bronstein</a> (University of Oxford) — Geometric deep learning</li><li class=""><a href="https://scholar.google.com/citations?user=TRZzLJgAAAAJ&hl=en" rev="en_rl_none" class="">Mireia Crispin</a> (University of Cambridge) — ML, multi-omics, and oncology</li><li class=""><a href="https://scholar.google.com/citations?user=5u7TxAMAAAAJ&hl=en" rev="en_rl_none" class="">Kazem Rahimi</a> (University of Oxford) — ML for population health, and chronic diseases</li><li class=""><a href="http://arkitus.com/research/" rev="en_rl_none" class="">Ali Eslami</a> (DeepMind) — Advanced topics in representation learning </li><li class=""><a href="https://scholar.google.com/citations?user=WvufSLAAAAAJ&hl=en" rev="en_rl_none" class="">Ishan Misra</a> (Facebook AI Research) — ML, computer vision, and learning with reduced supervision</li><li class=""><a href="https://scholar.google.com/citations?user=ynIWXnUAAAAJ&hl=en" rev="en_rl_none" class="">Javier Gonzalez</a> (Microsoft research) — Statistical / probabilistic ML, causal inference</li><li class=""><a href="https://scholar.google.co.uk/citations?user=2cWcY-MAAAAJ&hl=en" rev="en_rl_none" class="">Reza Khorshidi </a>(University of Oxford) — ML for Electronic Health Records </li><li class=""><a href="https://scholar.google.ch/citations?user=FwEz5s4AAAAJ&hl=en" rev="en_rl_none" class="">Sonali Parbhoo</a> (Imperial College London, Harvard) — Reasoning in uncertainty, and ML Interpretability</li><li class=""><a href="https://scholar.google.com/citations?user=BuJuSqkAAAAJ&hl=en" rev="en_rl_none" class="">Jorge Cardoso </a>(King’s College London) — ML for medical imaging</li><li class=""><a href="https://scholar.google.com/citations?user=8l-tvFoAAAAJ&hl=en" rev="en_rl_none" class="">Vincent Moens</a> (Meta) — ML Ops, PyTorch, DL software architectures</li></ul><div class=""><br class=""></div><div class=""><i class="">ML x Finance</i></div><ul class=""><li class=""><a href="https://scholar.google.com/citations?user=stlWUqcAAAAJ&hl=en" rev="en_rl_none" class="">Rama Cont</a> (University of Oxford) — Quantitative finance, ML for building market simulators</li><li class=""><a href="https://scholar.google.co.uk/citations?user=mtNQD-8AAAAJ&hl=en" rev="en_rl_none" class="">Stefan Zohren</a> (University of Oxford) — Representation learning & (financial) time series</li><li class=""><a href="https://scholar.google.co.uk/citations?user=SP9r32UAAAAJ&hl=en" rev="en_rl_none" class="">Yulan He</a> (University of Warwick) — Sentiment/opinion mining NLP</li><li class=""><a href="https://scholar.google.com/citations?user=8ONXPV8AAAAJ&hl=en" rev="en_rl_none" class="">Sebastian Ruder </a>(DeepMind) — Multi-lingual NLP </li><li class=""><a href="https://scholar.google.co.uk/citations?user=l8dX3ssAAAAJ&hl=en" rev="en_rl_none" class="">James Hensman</a> (Amazon) — <span style="color:rgb(51, 51, 51);" class="">Probabilistic ML, Gaussian processes, (financial) time series </span></li><li class=""><a href="https://scholar.google.com/citations?user=QehMdGIAAAAJ&hl=en" rev="en_rl_none" class="">Kalesha Bullard</a> (DeepMind) — Cooperative AI</li><li class=""><a href="https://scholar.google.co.uk/citations?user=GFvVRzwAAAAJ&hl=en" rev="en_rl_none" class="">Mihai Cucuringu</a> (University of Oxford) — Networks, statistical ML, and quant. finance</li><li class=""><a href="https://www.researchgate.net/profile/Ben-Wood-5" rev="en_rl_none" class="">Ben Wood</a> (JP Morgan chase) — ML and derivatives trading, deep hedging</li><li class=""><a href="https://scholar.google.com/citations?user=PaIXL6AAAAAJ&hl=en" rev="en_rl_none" class="">Thomas Spooner</a> (Sutter Hill Ventures) — Reinforcement learning in finance</li></ul><div class=""><br class=""></div><div class=""><i class="">ML Fundamentals</i></div><ul class=""><li class=""><a href="https://scholar.google.com/citations?user=AE5suDoAAAAJ&hl=en" rev="en_rl_none" class="">Haitham Ammar</a> (UCL, and Huawei) <span style="color:rgb(51, 51, 51);" class="">— Fundamentals of Stat./Bayesian/Probabilistic ML</span></li><li class=""><a href="https://scholar.google.co.uk/citations?user=VTTtSLcAAAAJ&hl=en" rev="en_rl_none" class="">Hao Ni </a><span style="color:rgb(51, 51, 51);" class="">(Turing Institute, and UCL)</span><i class=""><span style="color:rgb(51, 51, 51);" class=""> </span></i><span style="color:rgb(51, 51, 51);" class="">— ML Maths (from linear regression to DL) </span></li><li class=""><a href="https://scholar.google.com/citations?hl=en&user=WMlPkOoAAAAJ" rev="en_rl_none" class="">Yali Du </a><span style="color:rgb(51, 51, 51);" class="">(King's College London) — Optimisation methods in ML</span></li><li class=""><a href="https://scholar.google.com/citations?user=Ppjzn_EAAAAJ&hl=en" rev="en_rl_none" class="">Dingwen Tao</a><span style="color:rgb(51, 51, 51);" class=""> (Washington State University) — ML Systems, computational graph, Tensorflow, & PyTorch</span></li><li class=""><a href="https://scholar.google.com.au/citations?user=k2RheI8AAAAJ&hl=en" rev="en_rl_none" class="">Yitao Liang</a> (UCLA, and Peking University) <span style="color:rgb(51, 51, 51);" class="">— </span>Neuro-symbolic AI, & tractable prob. models</li></ul><div class=""><span style="color:#7030A0;" class=""><span data-markholder="true" class=""></span></span></div><div class=""><span style="color:#7030A0;" class=""><span data-markholder="true" class=""></span></span></div><div class=""><b class=""><br class=""></b></div><div class=""><b class="">About OxML 2022</b></div><ul class=""><li class="">OxML is organised by <a href="https://www.globalgoals.ai" rev="en_rl_none" class="">AI for Global Goals</a>, in partnership with <a href="https://cifar.ca/" rev="en_rl_none" class=""><span style="color:rgb(0, 0, 0);" class="">CIFAR</span></a> and The University of Oxford’s <a href="http://deepmedicine.medsci.ox.ac.uk/" rev="en_rl_none" class=""><span style="color:rgb(0, 0, 0);" class="">Deep Medicine</span></a> Program.</li><li class="">OxML schools have a special focus on ML and <a href="https://sustainabledevelopment.un.org/" rev="en_rl_none" class=""><span style="color:rgb(0, 0, 0);" class="">SDG</span></a>s. That is, in addition to theoretical ML lectures, there will be lectures on the application of ML in various SDGs areas.</li><li class="">OxML 2022 will have two separate 4-day schools: (1) ML x Health, and (2) ML x Finance. </li><li class="">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.</li><li class="">The schools will take place in <a href="https://www.stcatz.ox.ac.uk" rev="en_rl_none" class="">St Catherine's College</a>, Oxford (UK). There will also be a virtual option for those who cannot (or prefer not to) travel to Oxford, UK.</li><li class="">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).</li><li class="">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. </li></ul><div class=""><br class=""></div><div class="">For any queries, you can contact us using this email address: <a href="mailto:contact@oxfordml.school" rev="en_rl_none" class="">contact@oxfordml.school</a></div><div class=""><span data-markholder="true" class=""></span></div><div class="">Best,</div><div class="">—</div><div class="">Reza Khorshidi, D.Phil. (Oxon)</div><div class="">Deep Medicine Program, The University of Oxford</div></body></html>