Founder & CEO – AI for Global Goals General Chair – OxML School
London, England
Joined March 2012
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Mona Alinejad retweeted
Foundation Models | OxML 2027
How are foundation models built, trained and evaluated? And how do their underlying principles extend beyond language?
OxML 2027 explores representation learning, transformers, autoregressive and masked modelling, diffusion models, tokenisation, scaling laws and pre-training.
These foundations are then connected to applications across different domains.
📅 June 2027 | Online
Registration is now open: oxfordml.school/2027
#OxML2027 #FoundationModels
Mona Alinejad retweeted
OxML 2027 is now open for registration.
Foundation Models — June 2027, online only
Biomedical AI — 10–12 August 2027, Oxford + online
Frontier AI — 13–15 August 2027, Oxford + online
Register: oxfordml.school/2027/registe…
Every year, we evolve the OxML programme to reflect the latest advances in AI and machine learning.
For OxML 2027, we’re making some of the biggest changes since the school began.
Our commitment remains the same: advanced AI training taught by leading scientists and researchers.
The new structure reflects how we see some of the major shifts in the field:
• Foundation models are expanding far beyond language and vision.
• Frontier AI increasingly involves systems that reason, use tools, act and improve.
These shifts have shaped three complementary programmes:
1. Foundation Models — Fundamentals & Applications
2. Biomedical AI — Health, Biology & Discovery
3. Frontier AI — Learning, Reasoning, Agency & Self-Improvement
Each programme stands on its own. Together, they offer a connected journey through modern AI, combining theory with applications and hands-on cases.
More on the curriculum and speakers soon:
oxfordml.school/2027
#OxML2027
Mona Alinejad retweeted
💙 That's a wrap on MLx Representation Learning & Generative AI at OxML Oxford Machine Learning Summer School 2026! 💙
What an incredible four days of learning, collaboration and new friendships. Thank you to everyone who joined us and made this track so special - your curiosity, enthusiasm and passion for research are what make the OxML community so inspiring. A special congratulations to everyone who took part in the Poster Presentation competition. Thank you for sharing your exciting research and contributing to so many great conversations throughout the week!
To everyone who helped make this programme possible, and to every attendee who brought such fantastic energy - thank you! We hope to see many familiar faces again next year!
#OxML2026 #MachineLearning #RepresentationLearning #GenerativeAI #ArtificialIntelligence #OxfordMachineLearning #AIforGlobalGoals
📢⌛ Last chance to register for OxML 2026.
This July, at the Oxford Mathematical Institute and online, we will host two advanced tracks:
MLx Health & Bio
MLx Representation Learning & Generative AI
Outstanding speakers -- Advanced technical depth --Strong international community.
Final chance to join us this July:
oxfordml.school/2026
#OxML2026 #OxML #MachineLearning #AI #GenerativeAI
@rezakhorshidi @yalidux @GlobalGoalsAI @GRESEARCHjobs
ALT Across these tracks, we are bringing together an outstanding group of speakers working across some of the most important directions in machine learning today. - In Health & Bio, topics span EHR learning, digital health and novel data modalities, sensor and foundation models, geometric deep learning, cellular-scale biology, and broader questions at the intersection of machine learning, healthcare, and biomedical impact. - In Representation Learning & Generative AI, the programme includes formalised mathematics, intelligent data gathering, multi-robot and multi-agent learning, embodied multimodal intelligence, causal discovery, multimodal AI, AI safety and alignment, and related directions in modern generative and representation learning. What continues to make OxML distinctive is not only the quality of the speakers, but the kind of community it brings together: researchers, engineers, scientists, founders, clinicians, and practitioners from around the world who want serious technic
Mona Alinejad retweeted
⚠️ Limited seats remaining for MLx Representation Learning & Generative AI at Oxford Maths Institute + Online (15–18 July).
Join leading researchers and practitioners exploring frontier models, scaling laws, modern architectures, generative AI systems, representation learning, and AI products.
Some of the Featured Lectures:
Why formalize mathematics— Kevin Buzzard (Imperial College London)
Intelligent Data Gathering— Tom Rainforth (University of Oxford)
Multi-Robot and Multi-Agent Learning— Amanda Prorok (University of Cambridge)
Embodied Multimodal Intelligence with Foundation Models— Oier Mees (Microsoft)
Multimodal AI— Paul Liang (MIT)
On Causal Discovery and the Extrapolation of Causal Effects— Ricardo Silva (UCL)
A theoretical view with Arena's data—Peter Gostev (Arena AI)
Petar Veličković (Google DeepMind)
Fazl Barez (University of Oxford)
Tim Rocktäschel (UCL)
Alexander Tong (Aithyra)
Tony Feng UC Berkeley
Register now before seats fill up.
oxfordml.school
@FazlBarez @AlexanderTong7 @_rockt @petergostev @pliang279 @oier_mees @aprorok @tom_rainforth
#MachineLearning #GenerativeAI #RepresentationLearning #AI #OxML
We are delighted to welcome Prof. Tim Rocktäschel (UCL and Co-Founder of
Recursive Superintelligence) to OxML 2026 🙌
Tim’s work spans world models, representation learning, open-endedness, self-improving AI systems, grounded language understanding, and automated scientific discovery — all central directions in the future of AI.
This will be a particularly exciting session at OxML 2026, taking place this July in Oxford.
⌛ Registration deadline: 22 May
👉 oxfordml.school/2026
@GlobalGoalsAI
#OxML2026 #AI #MachineLearning #WorldModels #RecursiveSuperintelligence
🤖 Made with AI
Today we begin MLx Cases at OxML 2026.
A hands-on, engineering-driven module focused on building real end-to-end AI systems — from CNNs and diffusion models to MiniGPT, open-source agents, and AI systems that reason, act, and evolve.
At a time when the hardest problems in AI increasingly sit around the model, this is exactly the kind of practical training that matters.
Looking forward to a great module with an outstanding group of instructors and participants.
#OxML2026 #OxML #MachineLearning #AI #ML
Mona Alinejad retweeted
⏳ CALL FOR SUBMISSIONS
The deadline for the OxML 2026 Poster Challenge is fast approaching — don’t miss your chance to present your research in Oxford this July.
Whether it’s early-stage work, ongoing research, or a paper in progress, we welcome submissions across both tracks:
MLx Health & Bio (10–13 July)
MLx Representation Learning & GenAI (15–18 July)
$1,000 prize for the top poster in each track
Open to all in-person OxML participants
Submit by 15 May 2026 via EasyChair
This is your opportunity to share your work, get feedback from leading researchers, and connect with a global ML community.
👉 Apply now: oxfordml.school/2026/poster-…
#OxML2026 #MachineLearning #AIResearch #PosterChallenge #GenAI #HealthAI #AcademicResearch
⏳ Last call for MLx Cases — registration closes tomorrow, 24 April.
One of the clearest shifts in AI right now is that, in many domains, the hardest problems sit around the model: evaluation, multi-step system behaviour, tool use, constraints, and surfacing failure modes early.
These are the skills that increasingly differentiate strong ML practitioners.
That is exactly why MLx Cases matters. It is the most practical module of OxML 2026: hands-on, engineering-driven, and focused on building real end-to-end AI systems.
If you want to move beyond models and notebooks to building systems that actually work in practice, this is a very valuable module.
📅 Register by tomorrow, 24 April:
👉 oxfordml.school
#OxML2026 #OxML #MachineLearning #AI #DeepLearning #GenerativeAI #MLx #DataScience
Mona Alinejad retweeted
🚨 Limited slots left for OxML 2026 – MLX Cases
MLX Cases is a hands-on, engineering-driven track designed to help you go beyond theory and actually build end-to-end AI systems from scratch.
Over two intensive weekends, you’ll work through practical case studies covering:
• Training CNNs for image classification
• Building diffusion models for images and sequences
• Implementing a MiniGPT
• Designing and running your own open-source AI agent
• Creating real-world GenAI applications (travel planning, scheduling, and more)
This is not just about learning concepts — it’s about understanding how modern AI systems are engineered, deployed, and made useful in practice.
You’ll learn directly from experts across industry and academia, and leave with working systems and a deeper intuition for how everything fits together.
📅 7–9 & 15–16 May | Online
👉 Register now: oxfordml.school
David de la Iglesia Castro, Davide Eynard, Noor S., Xin Eric Wang, Peter Gostev, Jialin Yu, Sizhe Yuen
Yali Du, Mona Alinejad, D.Phil. (Oxon), Reza Khorshidi, D.Phil. (Oxon)
Oxford Internet Institute, University of Oxford, Oxford Women in Engineering Network
#MLxCases #OxML #AI2026 #AIProduct #ML #minigpt #CNN #AIAgent
@petergostev
@xwang_lk
@aittalam
@Harvard
@UniofOxford
@turinginst
@nsajidt
Mona Alinejad retweeted
📣 From models to real-world impact — build, deploy, and scale.
MLx Cases at the Oxford Machine Learning School (OxML 2026) is designed for those who want to go beyond theory and actually build—from CNNs and diffusion models to AI agents and production-ready apps.
Hands-on. Practical. Cutting-edge.
If you're ready to turn ideas into working AI systems, this is where it happens.
7–9 & 15–16 May 2026 | Online
Register by 7 April
Speakers:
Noor Sajid (Harvard University) – Building a CNN for image classification
Jialin Yu (University of Oxford) – Building a diffusion model for images/sequences
Sizhe Yuen (The Alan Turing Institute) – Building a MiniGPT
Davide Eynard & David de la Iglesia Castro (Mozilla AI) – Own your AI agent: open-source agents on your terms
Peter Gostev (Arena AI) – Building useful apps with today’s tools
Xin Eric Wang (niversity of California, Santa Barbara & Simular AI) – GenAI for calendar booking, travel planning & beyond
Build real systems. Learn by doing. Join a global community pushing AI forward. www. oxfordml.school
#OxML2026 #MachineLearning
#GenerativeAI #AIEngineering #DeepLearning #AIApplications
@petergostev @xwang_lk @aittalam @Harvard @UniofOxford @turinginst @nsajidt
Yali Du, Mona Alinejad, D.Phil. (Oxon), Reza Khorshidi, D.Phil. (Oxon)
A major frontier in AI is its growing role in scientific discovery.
At #OxML 2026, Prof. Christopher Bishop will discuss how AI can accelerate discovery, from fast emulators trained on synthetic data to new approaches across biology, healthcare, chemistry, & physics
@MSFTResearch
ALT www.oxfordml.school/2026
Why formalise mathematics?
As humans & LLMs get better at teaching maths to computers, the implications for verification & discovery are growing fast.
At OxML 2026, Prof. Kevin Buzzard will explore why this matters.
👉oxfordml.school/2026
#OxML #AIforMaths, @imperialcollege
🤖 Made with AI
Mona Alinejad retweeted
Speaker Announcement:
We’re excited to welcome Amanda Prorok (University of Cambridge) to the MLx Representation Learning & Generative AI track this July in Oxford.
Her lecture on Multi-Robot and Multi-Agent Learning will explore:
• Introduction to multi-robot and multi-agent problems
• Learning to synthesize cooperative behaviors and interaction strategies
• Tackling computationally hard multi-agent challenges through learning
Join us at MLx Representation Learning & Generative AI — July 2026, Oxford.
📌 Plus, don’t miss the opportunity to present your research in our Poster Challenge.
Register now: oxfordml.school
#OxML #MachineLearning #GenerativeAI #MultiAgentSystems #Robotics #AIResearch #Oxford #AICommunity @aprorok @Cambridge_Uni
A major frontier in AI is not just making single systems more capable, but understanding how agents learn, coordinate, and act together.
At OxML 2026, we'll explore multi-robot and multi-agent learning, from cooperative behaviours to hard coordination problems in the real world.
ALT www.oxfordml.school
Mona Alinejad retweeted
Explore how AI systems can perceive, reason, and act in the real world.
Join Oier Mees (Microsoft, ETH Zürich) for a deep dive into Embodied Multimodal Intelligence with Foundation Models, where vision, language, and action come together to shape the next generation of intelligent agents.
MLx Representation Learning & Generative AI
University of Oxford Mathematics
Discover how foundation models are moving beyond text and images into real-world interaction.
Secure your place: oxfordml.school/2026
In-person seats are limited. Register by 22 May 2026.
#OxML #MultimodalAI #RepresentationLearning #FoundationModels #Robotics
@oier_mees @Microsoft @ETH_en @OxUniMaths
One of the most important frontiers in AI is turning foundation models into systems that can perceive, reason & act in the real world.
At #OxML 2026, we'll explore embodied multimodal intelligence, generalist robot policies & reasoning-enhanced robotics.
oxfordml.school
Causal inference becomes much harder when the structure is uncertain.
Estimating effects from off-policy data is not enough on its own.
At #OxML 2026, Prof. Ricardo Silva (@ucl) will explore causal discovery and the extrapolation of causal effects.
👉oxfordml.school
Mona Alinejad retweeted
📣 Announcing the OxML 2026 Poster Challenge
We are excited to launch the OxML 2026 Poster Challenge, taking place this July at the Mathematical Institute, University of Oxford.
The challenge highlights outstanding research and applied work across machine learning and AI, and offers a platform to present your work, receive feedback, and engage with the OxML community.
Submissions are invited in two tracks:
🟢 MLx Health & Bio
🟢 MLx Representation Learning & Generative AI
🏆 Top posters in each track will receive $1,000 awards.
📅 Submission deadline: 15 May 2026
We particularly encourage submissions from researchers preparing or submitting work to venues such as NeurIPS, ICML, ICLR, and ACL, as well as early-stage and applied contributions.
Submit via EasyChair:
easychair.org/conferences/?c…
Full call for posters:
easychair.org/cfp/oxml26
#OxML2026 #OxML #MachineLearning #AI #GenerativeAI #RepresentationLearning #HealthAI #BioAI, @GRESEARCHjobs
