Program Overview

Introduction to the MBZUAI Machine Learning Winter School 2026

The MBZUAI Machine Learning Winter School: Representation Learning & GenAI is an intensive 5-day program that brings together world-leading researchers to explore the cutting-edge developments in modern machine learning and generative artificial intelligence. This comprehensive program combines keynote presentations, technical lectures, and hands-on practical sessions, offering participants direct access to the latest research and methodologies from pioneers who are shaping the future of AI.

About the Winter School

The winter school focuses on the revolutionary advances in deep learning that have enabled powerful generative capabilities across multiple domains. From large language models to diffusion models for images and videos, these breakthrough techniques are transforming how we approach AI research and applications.

Target Audience

This winter school is designed for:

• Graduate students (PhD and Master's) in machine learning, computer science, AI, or related fields
• Exceptional undergraduate students with strong mathematical and programming backgrounds
• Early-career researchers and industry practitioners seeking to advance their expertise in representation learning and generative AI
• Academic researchers looking to explore new directions or transition into generative AI research

Prerequisites

• Strong mathematical foundation: Linear algebra, calculus, probability theory, and statistics
• Proficiency in Python programming (required) with experience in ML libraries (NumPy, PyTorch/TensorFlow)
• Basic understanding of machine learning concepts: Neural networks, optimization, and deep learning fundamentals
• Recommended: Prior coursework or research experience in machine learning or computer vision/NLP

Confirmed Speakers and Lecturers

The school brings together world-leading researchers and practitioners from academia and industry to share cutting-edge insights in representation learning and generative AI.

• Michael Bronstein, DeepMind Professor of AI, Oxford / Scientific Director, AITHYRA
• Ahmed Elhag (TA), PhD student, University of Oxford
• Florence Forbes, Director of Research, INRIA Grenoble Rhone-Alpes
• Arthur Gretton, Professor, University College London
• Salman Khan, Associate Professor, MBZUAI
• Alexander Korotin, Assistant Professor, Skoltech
• Ivan Laptev, Professor, INRIA - MBZUAI
• Yingzhen Li, Associate Professor, Imperial College London
• Eric Moulines, Professor, École Polytechnique - MBZUAI
• Yuandong Tian, Research Scientist Director, Meta GenAI
• Eric Xing, Professor, Carnegie Mellon University - MBZUAI
• Kun Zhang, Professor, Carnegie Mellon University - MBZUAI

School Program

The winter school features a comprehensive 5-day program combining theoretical foundations with practical applications. Time | Speaker | Title
---|---|---
Monday, February 9
08:30 - 09:30 | - | Registration & Welcome / Intro
09:30 - 11:00 | Eric Xing | Opening Words
11:00 - 11:30 | Break
11:30 - 13:00 | Eric Moulines | Bayesian Inverse Problems with diffusion priors (1/2)
13:00 - 14:00 | Lunch Break
14:00 - 15:30 | Alex Korotin | Flow Matching and Rectified Flows
15:30 - 16:00 | Break
16:00 - 17:30 | Kun Zhang | Causal representation learning and causal generative AI
17:30 - 18:30+ | Social Event
Tuesday, February 10
09:00 - 10:30 | Alex Korotin | Diffusion Bridge Generative Models for Domain Translation Problems
10:30 - 11:00 | Break
11:00 - 12:30 | Florence Forbes | Scalable Bayesian Experimental Design with Diffusions
12:30 - 14:00 | Lunch Break
14:00 - 15:30 | Eric Moulines | Bayesian Inverse Problems with diffusion priors (2/2)
15:30 - 16:00 | Break
16:00 - 17:30 | Salman Khan | Advances in Video Understanding with Multimodal Large Language Models
Wednesday, February 11
09:00 - 10:30 | Yingzhen Li | Approximate Probabilistic Inference - Modern Advances in GenAI Era
10:30 - 11:00 | Break
11:00 - 12:30 | Yingzhen Li | Uncertainty Quantification in Deep Learning - A Bayesian Approach
12:30 - 14:00 | Lunch Break
14:00 - 15:30 | Ivan Laptev | Generative Models: From Vision and Language to Embodied AI
15:30 - 16:00 | Break
16:00 - 18:30+ | Social Event
Thursday, February 12
09:00 - 10:30 | Yuandong Tian | Neural and Symbolic Representation and its application (1/2)
10:30 - 11:00 | Break
11:00 - 12:30 | Yuandong Tian | Neural and Symbolic Representation and its application (2/2)
12:30 - 14:00 | Lunch Break
14:00 - 15:30 | Arthur Gretton | Causal Effect Estimation (1/2)
15:30 - 16:00 | Break
16:00 - 17:30 | Arthur Gretton | Causal Effect Estimation (2/2)
17:30 - 18:30+ | Social Event
Friday, February 13
09:00 - 11:00 | Michael Bronstein | Geometric DL and equivariance, graph neural networks, and learning in weight-spaces
11:00 - 11:30 | Break
11:30 - 13:00 | Ahmed Elhag | Tutorial on Graph Neural Networks and Geometric Graphs
13:00 - 18:30+ | Lunch and Checkout

Organizing Committee

• Salem Lahlou
• Maxim Panov
• Eric Moulines
• Kun Zhang
• Nicolas Mauricio Cuadrado Avila, Student Volunteer
• Klea Ziu, Student Volunteer