OxML 2023
8–16 July, 2023
Oxford Mathematical Institute & Online
OxML 2023 PROGRAM COMMITEE
OxML 2023 SPEAKERS
ML x HEALTH
Gitta Kutyniok
Professor of Applied Maths
University of Munich
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Kyunghyun Cho
Associate Prof. of computer science & data science, NYU
Senior Director of Frontier Research, Genentech
CIFAR Fellow
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Mireia Crispin
Lecturer in Integrated Cancer Medicine
University of Cambridge
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Louis-Philippe Morency
Prof. of Computer Science
Carnegie Mellon Uni.
Cheng Zhang
Principal Researcher
Microsoft Research
Jorge Cardoso
Reader in Artificial Medical Intelligence
King's College London
Munmun De Choudhury
Associate Prof. of Interactive Computing
Georgia Tech
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Pietro Liò
Professor of Computer Science
University of Cambridge​
Ravi Patel
Advanced AI Scientist
Benevolant AI​
Kazem Rahimi
Professor of Cardiovascular Medicine
University of Oxfor
Ali Eslami
Research Scientist
Google DeepMind
Christian Rupprecht
Lecturer in Computer Vision
University of Oxford​
MLx FINANCE & NLP
Rama Cont
Professor of Mathematical Finance
University of Oxford​
Stefan Zohren
Director of Oxford-Man Institute
University of Oxford
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Blanka Horvath
Professor in Oxford Math Finance Group
University of Oxford​
Svetlana Bryzgalova
Assistant Professor of Finance
London Business School
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Mihai Cucuringu
Associate Professor of Statistics
University of Oxford
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He He
Assistant Professor of computer science
NYU​
Rahul Savani
Professor of Computer Science
University of Liverpool
Edward Grefenstette
Head of ML at Cohere,
Honorary Professor at UCL
Diyi Yang
Assistant Professor
Stanford University
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Ryan Cotterell
Assistant Professor of Computer Science
ETH Zürich
Pasquale Minervini
Lecturer in NLP
University of Edinburgh, UCL
Stephen Clark
Head of AI
Quantinuum,
ML x FUNDAMENTALS
Yali Du
Lecturer in AI
King's College London
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Haitham Bou Ammar
RL Team Leader
Huawei Research
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Matthieu Zimmer
Senior Research Scientist
Huawei
Rasul Tutunov
Research Scientist
Huawei​
Eduardo C. Garrido-Merchán
Research Scientist
Universidad Pontificia Comillas
ML x CASES
Khémon BEH
Founder & CEO
Quickscale.ai
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Vincent Moens
Research Engineer,
Meta​
MLx FUNDAMENTALS
8-10 May | Online
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MLx CASES
June 2023​ | Online
MLx Fundamentals:
Based on the success of previous years' 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 -- during this module, we aim to provide everyone with training in the following topics:
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Linear Algebra and Mathematics of machine learning
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Optimisation
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Fundamentals of statistical / probabilistic ML
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Fundamentals of representation / deep learning
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and more
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MLx Cases:
The aim of ML x Cases track is to provide you with a training on real-world issues and processes related to ML development/implementation process. This will range from efficient and repeatable approaches to data collection, enrichment and cleaning, and labelling, to transfer learning use cases of pre-trained SOTA models and their fine-tuning to achieve good performance on a domain-specific task. We will run ~5 different cases, led by experienced ML / data scientists, supported by TAs to help make the sessions interactive.
At the end of the ML x Cases, participants will learn useful concepts on:
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Frame a problem as an ML problem
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Leveraging appropriate toolboxes
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Knowing which approach typically works best depending on the types of use cases
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Defining what performance metrics to choose
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Experimental setups for a performant model, while tracking and documenting experiments with MLFlow
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Forming a naive baseline to more sophisticated experiments
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Interpreting model results (e.g., under/overfitting and ways to remediate it).
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Feedback loops and allowing the system to collect information from user inputs.
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and more
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MLx FINANCE &
NLP
8-11 July, 2023
Oxford Mathematical Institute & Online
Building on the topics covered in ML fundamentals module, the Finance module will continue and cover the following topics:
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Statistical / probabilistic ML (e.g., Bayesian ML, Gaussian processes, approximate inference, modelling uncertainty, learning from large data, ...)
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Advanced topics in representation learning (e.g., learning with no labels, representation learning in time series, text, and multi-modal data)
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Natural language processing (e.g., large language models, multi-lingual NLP, sentiment/opinion mining, fact checking / false news, misinformation detection, ...)
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Reinforcement learning
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Knowledge graphs
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Knowledge-aware ML
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Symbolic reasoning
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Neuro-symbolic AI
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Applied talks on ML in/for:
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Financial time series (e.g., standard models, Gaussian processes, representation learning, ...)
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Building market simulators
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Trading and hedging
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Insurance, asset management, emerging risks
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Financial inclusion and economic prosperity
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ESG
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...
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Taking ML to the real-world settings (e.g., interpretability, ethics, ML Ops, ML products, ...)
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And more
MLx HEALTH
13-16 July, 2023
Building on the topics covered in ML fundamentals module, the Health module will continue and cover the following topics:
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Statistical / probabilistic ML (e.g., Bayesian ML, causal inference, approximate inference, modelling uncertainty, ...)
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Advanced topics in representation learning (e.g., learning with little or nor supervision, self-supervised learning, multi-modal representation learning, ...)
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Graph neural networks, and geometrical deep learning
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Computer vision
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Knowledge graphs
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Knowledge-aware ML
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Symbolic reasoning,
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Neuro-symbolic AI
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Applied talks on ML in/for:
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EHR, imaging (e.g., brain, heart), genomics, multi-omics, ...
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Chronic noncommunicable diseases, infectious diseases, oncology, ...
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Drug discovery, and biopharma industry
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Taking ML to the real-world settings (e.g., interpretability, ethics, ML Ops, ML products, ...)
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And more