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OxML 2023
 

Location: Mathematical Institute, University of Oxford

Date: 8–16 July, 2023

 

OxML 2023 PROGRAM COMMITEE

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Dr. Mona Alinejad
General Chair
OxML School
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Founder & CEO

AI for Global Goals

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Prof. Kazem Rahimi
Area Chair
MLx Health
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 Professor of Cardiovascular Medicine 

University of Oxford

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Dr. Reza Khorshidi
Program Chair 
OxML 2023
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 Program Lead, ML & Medicine

University of Oxford 

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Dr. Moez Draief
Area Chair
MLx Cases 
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Managing Director at Mozilla.ai 

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Dr. Haitham Bou Ammar
Area Chair
MLx Fundamentals 
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H. Assistant Professor at UCL,

RL Team Leader at Huawei Research London

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Dr. Renyuan Xu
Area Chair
MLx Finance 
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Assistant Professor

Uni. of Southern California

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Dr. Karo Moilanen
Area Chair
MLx NLP 
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CTO, Founder, AI Scientist

Speakers

OxML 2023 SPEAKERS

ML x HEALTH 

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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

Louis-Philippe Morency

Louis-Philippe Morency

Prof. of Computer Science

Carnegie Mellon Uni.

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Cheng Zhang

Principal Researcher

Microsoft Research

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Jorge Cardoso

Reader in Artificial Medical Intelligence

King's College London

Munmun De Choudhury

Munmun De Choudhury

Associate Prof. of Interactive Computing

Georgia Tech 

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Pietro Liò

Professor of Computer Science

University of Cambridge

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Ravi Patel

Advanced AI Scientist

Benevolant AI

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Kazem Rahimi

Professor of Cardiovascular Medicine 

University of Oxfor

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Ali Eslami

Research Scientist

Google DeepMind 

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Christian Rupprecht 

Lecturer in Computer Vision

University of Oxford

MLx FINANCE & NLP

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Rama Cont

Professor of Mathematical Finance 

University of Oxford

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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

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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

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Rahul Savani 

Professor of Computer Science

University of Liverpool

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Edward Grefenstette 

Head of ML at Cohere,

 Honorary Professor at UCL

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Diyi Yang

Assistant Professor 

Stanford University

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Ryan Cotterell

Assistant Professor of Computer Science

ETH Zürich

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Pasquale Minervini

Lecturer in NLP

University of Edinburgh, UCL

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Stephen Clark

Head of AI

Quantinuum,

ML x FUNDAMENTALS

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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

Rasul Tutunov

Research Scientist

Huawei

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Eduardo C. Garrido-Merchán

Research Scientist

Universidad Pontificia Comillas

   ML x CASES

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Khémon BEH 

Founder & CEO

Quickscale.ai

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Vincent Moens

Research Engineer,

Meta

ML Fundamentals

MLx FUNDAMENTALS 

8-10 May

Online

MLx CASES

June 2023

Online

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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:

 

  • Linear Algebra and Mathematics of machine learning

  • Optimisation

  • Fundamentals of statistical / probabilistic ML

  • Fundamentals of representation / deep learning

  • and more

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:

  • Frame a problem as an ML problem

  • Leveraging appropriate toolboxes

  • Knowing which approach typically works best depending on the types of use cases

  • Defining what performance metrics to choose

  • Experimental setups for a performant model, while tracking and documenting experiments with MLFlow

  • Forming a naive baseline to more sophisticated experiments

  • Interpreting model results (e.g., under/overfitting and ways to remediate it).

  • Feedback loops and allowing the system to collect information from user inputs.

  • and more

MLx FINANCE &
NLP

Stock Market Down
MLx finance

Building on the topics covered in ML fundamentals module, the Finance module will continue and cover the following topics:

  • Statistical / probabilistic ML (e.g., Bayesian ML, Gaussian processes, approximate inference, modelling uncertainty, learning from large data, ...)

  • Advanced topics in representation learning (e.g., learning with no labels, representation learning in time series, text, and multi-modal data)

  • Natural language processing (e.g., large language models, multi-lingual NLP, sentiment/opinion mining, fact checking / false news, misinformation detection, ...)

  • Reinforcement learning

  • Knowledge graphs

  • Knowledge-aware ML

  • Symbolic reasoning

  • Neuro-symbolic AI

Applied talks on ML in/for:

  • Financial time series (e.g., standard models, Gaussian processes, representation learning, ...)

  • Building market simulators

  • Trading and hedging

  • Insurance, asset management, emerging risks

  • Financial inclusion and economic prosperity

  • ESG

  • ...

  • Taking ML to the real-world settings (e.g., interpretability, ethics, ML Ops, ML products, ...)

  • And more

MLx HEALTH

Brain Scans
ML x Health

Building on the topics covered in ML fundamentals module, the Health module will continue and cover the following topics:

  • Statistical / probabilistic ML (e.g., Bayesian ML, causal inference, approximate inference, modelling uncertainty, ...)

  • Advanced topics in representation learning (e.g., learning with little or nor supervision, self-supervised learning, multi-modal representation learning, ...)

  • Graph neural networks, and geometrical deep learning

  • Computer vision

  • Knowledge graphs

  • Knowledge-aware ML

  • Symbolic reasoning,

  • Neuro-symbolic AI

Applied talks on ML in/for:

  • EHR, imaging (e.g., brain, heart), genomics, multi-omics, ...

  • Chronic noncommunicable diseases, infectious diseases, oncology, ...

  • Drug discovery, and biopharma industry

  • Taking ML to the real-world settings (e.g., interpretability, ethics, ML Ops, ML products, ...)

  • And more

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