Speakers announcement (and 2nd CFA) -- Oxford ML Summer School (OxML 2023)

[Reza Khorshidi]

Dates: July 8-16, 2023 (Oxford Mathematical Institute + Virtual)

Application deadline: 27 March 2023 

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

Link to the application form: https://forms.gle/Qu6u3i5Q9YWdDMn2A

Target audience 

• Everyone is welcome to apply to OxML 2023 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 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.

Important note

Given the overwhelming number of applications we receive, the application portal may close earlier than the deadline (March 27th) if the number of applications exceeds our capacity to review. Furthermore, we already began the review process in January, which will lead to notifications of acceptance being sent gradually, as we go applications. Please make sure you apply ASAP, to avoid disappointment.

The Speakers

Below is the list of our confirmed speakers to date — we will announce additional speakers in the coming weeks (follow the updates via the school’s website, or Twitter and LinkedIn accounts). Note that, participants of both ML x Health and ML x Finance modules will have access to / can attend both ML x Fundamentals and ML x Cases module (as optional modules).

ML x Healthcare

• Gitta Kutyniok (Ludwig Maximilian University of Munich) — Mathematics & theory of ML/DL
• Kyunghyun Cho (NYU, CIFAR, Genentech) —Advanced topics in RL & ML for comp. bio.
• Mireia Crispin (University  of Cambridge) — ML, multi-omics, and oncology
• Louis-Philippe Morency (Carnegie Mellon University) — Multi-modal rep. learning
• Munmun De Choudhury (Georgia Tech) — ML, digital health, mental health & wellbeing
• Ishan Misra (Facebook AI Research) — ML, computer vision, and learning with reduced supervision
• Cheng Zhang (Microsoft Research) — Probabilistic ML & causal ML
• Marwin Segler (Microsoft Research) — ML for drug discovery

ML x Finance

• Rama Cont (University of Oxford) — Quantitative finance, ML for building market simulators
• Stefan Zohren (University of Oxford) — Representation learning  & (financial) time series
• Sebastian Ruder (Google) — Multi-lingual NLP 
• Mihai Cucuringu (University of Oxford) — Networks, statistical ML, and quant. finance
• Blanka Horvath (University of Oxford​) — Market Simulators, Deep Hedging
• Svetlana Bryzgalova (London Business School) — ML, capital markets and  factor investing
• Ryan Cotterell (ETH Zürich, University of Cambridge) — Computational linguistics, NLP & ML
• Rahul Savani (University of Liverpool , The Allen Turing Institute) — RL in Finance & Automated Trading
• Pasquale Minervini (University of Edinburgh, UCL) — Advanced topics in ML + NLP

ML x Fundamentals

• Haitham Ammar (UCL, and Huawei) — Fundamentals of Stat./Bayesian/Probabilistic ML
• Yali Du (King's College London) — Optimisation fundamentals
• Matthieu Zimmer (Huawei) — fundamentals of RL
• Rasul Tutunov (Huawei) — Maths fundamentals

ML x Cases

• Vincent Moens (Meta) — ML Ops, PyTorch, DL software architectures
• Khémon BEH (Quickscale.ai) — ML applications, ML Ops

About OxML 2023

• OxML is organised by AI for Global Goals, in partnership with CIFAR and The University of Oxford’s Deep Medicine Program.
• OxML schools have a special focus on ML and SDGs. That is, in addition to theoretical ML lectures, it will have lectures on the application of ML in various SDGs areas.
• OxML 2023 will have two separate 4-day schools: (1) ML x Health, and (2) ML x Finance. 
• To provide all participants with the necessary background in fundamental theory and implementation and coding of ML/DL models, our 2023 program will also have two additional modules: MLx Fundamentals, and MLx Cases (more info on the website). They will take place online, during May and June 2023, and are open to all accepted participants.
• The schools will take place in the Mathematical Institute, University of Oxford and online.
• We aim to host ~250 participants in person (plus 300 virtually) in each school. 
• You can find out more about the previous schools — including the previous speakers — here.

The schools' theoretical tutorials on modern ML (including DL) will cover topics such as:

• Neural networks, deep learning / representation learning (with, with little, or without supervision), ...
• Statistical/probabilistic ML (e.g., Bayesian ML, causal ML, variational inference, Bayesian neural networks)
• NLP, computer vision, and multi-modal representation learning
• ...

On the applied side, the school will cover topics such as:

• MLx Health: The applications of ML in imaging, genomics, electronic health records (EHR), drug discovery, ...
• MLx Finance: The applications of ML in investment and asset mgmt., banking, insurance and emerging risks , hedging and options trading, ...

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