February 2 – 4, 2026 — Abu Dhabi Edge AI Summit 
Seville, Spain 
San Diego, USA 
Hangzhou, China 
Sharm El Sheikh, Egypt 
Rotterdam, Netherlands 
Vienna, Austria 
United Kingdom 
Vancouver, Canada 
Yokohama, Japan MBZUAI Nexus Speaker Series
Upcoming AI Talks
Past AI Talks
Reproducible Query Optimization Research for Data Systems
Hosted by: Prof. Xiaosong Ma
February 5, 2026
2:00pm
MBZUAI - Classroom 2
Wolfgang Lehner
Professor in Database Research and Director of the Institute of Systems Architecture at Dresden University of Technology
Hosted by: Prof. Xiaosong Ma
Computer Science
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Abstract
Reproducible Query Optimization Research for Data Systems
"Identifying reasonably good plans to execute complex queries in large data systems is a crucial ingredient for a robust data management platform. The traditional cost-based query optimizer approach enumerates different execution plans for each individual query, assesses each plan based on its costs, and selects the plan that promises the lowest execution costs. However, as we all know, the optimal execution plan is not always selected, opportunities are missed, and complex analytical queries might not even work. Thus, query optimization for data systems is a highly active research area, with novel concepts being introduced continuously. The talk will discuss this research area by addressing three distinct themes. First, the talk shows the potential of optimizer improvements by sharing insights from a comprehensive and in-depth evaluation. Based upon this analysis, the talk introduces TONIC and FASTgres. TONIC is a novel cardinality estimation-free extension for generic SPJ query optimizers, revising operator decisions for arbitrary join paths based on learned query feedback. FASTgres is a context-aware classification strategy for steering existing optimizers using hint set prediction. Finally, the talk sheds light on PostBOUND, a novel optimizer development and benchmarking framework that enables rapid prototyping and common-ground comparisons, serving as a base for reproducible optimizer research."
"Identifying reasonably good plans to execute complex queries in large data systems is a crucial ingredient for a robust data management platform. The traditional cost-based query optimizer approach enumerates different execution plans for each individual query, assesses each plan based on its costs, and selects the plan that promises the lowest execution costs. However, as we all know, the optimal execution plan is not always selected, opportunities are missed, and complex analytical queries might not even work. Thus, query optimization for data systems is a highly active research area, with novel concepts being introduced continuously. The talk will discuss this research area by addressing three distinct themes. First, the talk shows the potential of optimizer improvements by sharing insights from a comprehensive and in-depth evaluation. Based upon this analysis, the talk introduces TONIC and FASTgres. TONIC is a novel cardinality estimation-free extension for generic SPJ query optimizers, revising operator decisions for arbitrary join paths based on learned query feedback. FASTgres is a context-aware classification strategy for steering existing optimizers using hint set prediction. Finally, the talk sheds light on PostBOUND, a novel optimizer development and benchmarking framework that enables rapid prototyping and common-ground comparisons, serving as a base for reproducible optimizer research."
Orchestrating Agents Under Constraints: Optimization, Evaluation, and Small-Model Proxies
Hosted by: Prof. Mladen Kolar
February 6, 2026
11:00am
MBZUAI - Classroom 7
Chaithanya Band
Associate Professor in Operations at NUS Business School
Hosted by: Prof. Mladen Kolar
Statistics and Data Science
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Abstract
Orchestrating Agents Under Constraints: Optimization, Evaluation, and Small-Model Proxies
Tool-using LLM agents can be best understood as resource-constrained decision systems. Each run implicitly solves an operations problem: how to allocate scarce budget (tokens, latency, tool-call limits, and verification/judging compute) across planning, execution, recovery, and checking—under uncertainty about tool reliability, user intent, and when to stop. In this talk, I’ll connect modern agent design to classic OR ideas—sequential decision-making, budgeted optimization, scheduling, and robust objectives—and show how this framing leads to systems that are measurably more reliable, not just larger. I’ll walk through a unified set of results across three themes: (1) tool orchestration in realistic multi-tool environments, with evaluation designed to be diagnostic and trajectory-agnostic; (2) open-ended research agents evaluated via structured rubrics that surface systematic failure modes and make iteration scientific; and (3) cost-aware evaluation protocols, where debate/deliberation and budgeted stopping explicitly trade off accuracy against compute to trace a cost–accuracy frontier. Finally, I’ll discuss why small-model proxies (“analogs”) are a practical accelerator for this agenda: they enable faster experimentation on orchestration policies and evaluation designs at a fraction of the cost, while preserving the failure modes that matter. I’ll close with how these ideas translate into ongoing research collaborations with startups, developing deployable agents with explicit budgets, measurable guarantees, and clear reliability trade-offs.
Tool-using LLM agents can be best understood as resource-constrained decision systems. Each run implicitly solves an operations problem: how to allocate scarce budget (tokens, latency, tool-call limits, and verification/judging compute) across planning, execution, recovery, and checking—under uncertainty about tool reliability, user intent, and when to stop. In this talk, I’ll connect modern agent design to classic OR ideas—sequential decision-making, budgeted optimization, scheduling, and robust objectives—and show how this framing leads to systems that are measurably more reliable, not just larger. I’ll walk through a unified set of results across three themes: (1) tool orchestration in realistic multi-tool environments, with evaluation designed to be diagnostic and trajectory-agnostic; (2) open-ended research agents evaluated via structured rubrics that surface systematic failure modes and make iteration scientific; and (3) cost-aware evaluation protocols, where debate/deliberation and budgeted stopping explicitly trade off accuracy against compute to trace a cost–accuracy frontier. Finally, I’ll discuss why small-model proxies (“analogs”) are a practical accelerator for this agenda: they enable faster experimentation on orchestration policies and evaluation designs at a fraction of the cost, while preserving the failure modes that matter. I’ll close with how these ideas translate into ongoing research collaborations with startups, developing deployable agents with explicit budgets, measurable guarantees, and clear reliability trade-offs.
From AI for Biological and Medical Science to Virtual Cells
Hosted by: Prof. Aziz Khan
February 6, 2026
10:00am
MBZUAI - Classroom 2
Xuegong Zhang
Professor in Bioinformatics and Pattern Recognition and Director of the Bioinformatics Division at Tsinghua University
Hosted by: Prof. Aziz Khan
Computational Biology
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Abstract
From AI for Biological and Medical Science to Virtual Cells
Many tasks in biological and medical science can be modeled as Pattern Recognition tasks, and AI is playing more and more important roles in those tasks. With the enrichment of single-cell level high-throughput omics data, it is now even possible to build digital virtual cells with advanced AI foundation models. Prof. Xuegong Zhang has been one of the leading researchers in using AI for cutting-edge pattern recognition tasks in biology and medicine, and in prompting the concept and practices of developing AI virtual cell models. In this seminar, he will provide an overview of both the fields based on their own work in the past two decades, and discuss the future trends in AI biology and medicine.
Many tasks in biological and medical science can be modeled as Pattern Recognition tasks, and AI is playing more and more important roles in those tasks. With the enrichment of single-cell level high-throughput omics data, it is now even possible to build digital virtual cells with advanced AI foundation models. Prof. Xuegong Zhang has been one of the leading researchers in using AI for cutting-edge pattern recognition tasks in biology and medicine, and in prompting the concept and practices of developing AI virtual cell models. In this seminar, he will provide an overview of both the fields based on their own work in the past two decades, and discuss the future trends in AI biology and medicine.
Time Will Tell: Transforming Digital Health Data into Meaningful Distributions
Hosted by: Prof. Marcos Matabuena
February 6, 2026
2:00pm
MBZUAI - Classroom 6
Sujit Kumar Ghosh
Professor, NC State University
Hosted by: Prof. Marcos Matabuena
Computational Biology
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Abstract
Time Will Tell: Transforming Digital Health Data into Meaningful Distributions
Modern digital devices continuously record physiological signals such as heart rate and physical activity, generating rich but complex data that evolve over time and across individuals. This talk introduces flexible statistical frameworks that move beyond modeling averages to capture full outcome distributions and dynamic time patterns. By representing responses through quantile functions and allowing data‐driven transformations of time, the proposed methods provide a unified way to study how entire distributions change with covariates and over the course of daily life. These approaches enable more nuanced questions: not only how a typical heart rate responds to activity, but how variability, extremes, and temporal dynamics differ across individuals and contexts. Applications to continuously monitored wearable data demonstrate how the methods reveal interpretable features of human behavior and physiology, offering powerful tools for digital health research and personalized monitoring.
Modern digital devices continuously record physiological signals such as heart rate and physical activity, generating rich but complex data that evolve over time and across individuals. This talk introduces flexible statistical frameworks that move beyond modeling averages to capture full outcome distributions and dynamic time patterns. By representing responses through quantile functions and allowing data‐driven transformations of time, the proposed methods provide a unified way to study how entire distributions change with covariates and over the course of daily life. These approaches enable more nuanced questions: not only how a typical heart rate responds to activity, but how variability, extremes, and temporal dynamics differ across individuals and contexts. Applications to continuously monitored wearable data demonstrate how the methods reveal interpretable features of human behavior and physiology, offering powerful tools for digital health research and personalized monitoring.
Physics of Learning and Structure
Hosted by: Prof. Chih-Jen Lin
February 9, 2026
11:00am
MBZUAI - Classroom 5
Siyuan Guo
PhD Candidate at the University of Cambridge and Max Planck Institute for Intelligent Systems
Hosted by: Prof. Chih-Jen Lin
Machine Learning
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Abstract
Physics of Learning and Structure
"In physics, phenomena such as light propagation and Newtonian mechanics obey the principle of least action: the true trajectory is a stationary point of the Lagrangian. In our recent work [1], we investigated whether learning, too, follows a least-action principle. We model learning as stationary-action dynamics on information fields. Concretely, we derive classical learning algorithms as stationary points of information-field Lagrangians, recovering Bellman optimality from a reward-based Hamiltonian and Fisher-information–aware updates for estimation. This potentially yields a unifying variational view across reinforcement learning and supervised learning, and suggests optimisers with testable properties. Conceptually, it treats the training of a learning system as the dynamical evolution of a physical system in an abstract information space. Structure is also central to learning, enabling interventional reasoning and scientific understanding. Causality provides a framework for discovering structure from data under the hypothesis that causal mechanisms are independent. In earlier work [2], we formalise independent mechanisms as independent latent variables controlling each mechanism, and show how this perspective extends across effect estimation, counterfactual reasoning, representation learning, and reinforcement learning. Methodologically, in collaboration with Prior Labs, we developed Do-PFN [3], a pre-trained foundation model that performs in-context causal inference. This serves as a promising out-of-the-box tool for practitioners working across diverse scientific domains. References [1] Siyuan Guo and Bernhard Schölkopf. Physics of Learning: A Lagrangian Perspective to Different Learning Paradigms. arXiv preprint arXiv:2509.21049, 2025. [2] Siyuan Guo*, Viktor Tóth*, Bernhard Schölkopf, and Ferenc Huszár. Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data. Advances in Neural Information Processing Systems (NeurIPS), 2023. [3] Jake Robertson*, Arik Reuter*, Siyuan Guo, Noah Hollmann, Frank Hutter, and Bernhard Schölkopf. Do-PFN: In-Context Learning for Causal Effect Estimation. Advances in Neural Information Processing Systems (NeurIPS), 2025. (Spotlight; acceptance rate 3.19%.)"
"In physics, phenomena such as light propagation and Newtonian mechanics obey the principle of least action: the true trajectory is a stationary point of the Lagrangian. In our recent work [1], we investigated whether learning, too, follows a least-action principle. We model learning as stationary-action dynamics on information fields. Concretely, we derive classical learning algorithms as stationary points of information-field Lagrangians, recovering Bellman optimality from a reward-based Hamiltonian and Fisher-information–aware updates for estimation. This potentially yields a unifying variational view across reinforcement learning and supervised learning, and suggests optimisers with testable properties. Conceptually, it treats the training of a learning system as the dynamical evolution of a physical system in an abstract information space. Structure is also central to learning, enabling interventional reasoning and scientific understanding. Causality provides a framework for discovering structure from data under the hypothesis that causal mechanisms are independent. In earlier work [2], we formalise independent mechanisms as independent latent variables controlling each mechanism, and show how this perspective extends across effect estimation, counterfactual reasoning, representation learning, and reinforcement learning. Methodologically, in collaboration with Prior Labs, we developed Do-PFN [3], a pre-trained foundation model that performs in-context causal inference. This serves as a promising out-of-the-box tool for practitioners working across diverse scientific domains. References [1] Siyuan Guo and Bernhard Schölkopf. Physics of Learning: A Lagrangian Perspective to Different Learning Paradigms. arXiv preprint arXiv:2509.21049, 2025. [2] Siyuan Guo*, Viktor Tóth*, Bernhard Schölkopf, and Ferenc Huszár. Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data. Advances in Neural Information Processing Systems (NeurIPS), 2023. [3] Jake Robertson*, Arik Reuter*, Siyuan Guo, Noah Hollmann, Frank Hutter, and Bernhard Schölkopf. Do-PFN: In-Context Learning for Causal Effect Estimation. Advances in Neural Information Processing Systems (NeurIPS), 2025. (Spotlight; acceptance rate 3.19%.)"
Stepsize anything: A unified learning rate schedule for budgeted-iteration training
Hosted by: Prof. Kun Zhang
February 10, 2026
11:00am
MBZUAI - Classroom 3
Zhouchen Lin
Professor in Science and Technology, Peking University
Hosted by: Prof. Kun Zhang
Machine Learning
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Abstract
Stepsize anything: A unified learning rate schedule for budgeted-iteration training
The expanding computational costs and limited resources underscore the critical need for budgeted-iteration training, which aims to achieve optimal learning within predetermined iteration budgets. While learning rate schedules fundamentally govern the performance of different networks and tasks, particularly in budgeted-iteration scenarios, their design remains largely heuristic, lacking theoretical foundations. In addition, the optimal learning rate schedule requires extensive trial-and-error selection, making the training process inefficient. In this work, we propose the Unified Budget-Aware (UBA) schedule, a theoretically grounded learning rate schedule that consistently outperforms commonly-used schedules among diverse architectures and tasks under different constrained training budgets. First, we bridge the gap by constructing a novel training budget-aware optimization framework, which explicitly accounts for the robustness to landscape curvature variations. From this framework, we derive the UBA schedule, controlled by a single hyper-parameter φ that provides a trade-off between flexibility and simplicity, eliminating the need for per-network numerical optimization. Moreover, we establish a theoretical connection between φ and the condition number, adding interpretation and justification to our approach. Besides, we prove the convergence for different values of φ. We offer practical guidelines for its selection via theoretical analysis and empirical results. Extensive experimental results show that UBA consistently surpasses the commonly-used schedules across diverse vision and language tasks, spanning network architectures (e.g., ResNet, OLMo) and scales, under different training-iteration budgets.
The expanding computational costs and limited resources underscore the critical need for budgeted-iteration training, which aims to achieve optimal learning within predetermined iteration budgets. While learning rate schedules fundamentally govern the performance of different networks and tasks, particularly in budgeted-iteration scenarios, their design remains largely heuristic, lacking theoretical foundations. In addition, the optimal learning rate schedule requires extensive trial-and-error selection, making the training process inefficient. In this work, we propose the Unified Budget-Aware (UBA) schedule, a theoretically grounded learning rate schedule that consistently outperforms commonly-used schedules among diverse architectures and tasks under different constrained training budgets. First, we bridge the gap by constructing a novel training budget-aware optimization framework, which explicitly accounts for the robustness to landscape curvature variations. From this framework, we derive the UBA schedule, controlled by a single hyper-parameter φ that provides a trade-off between flexibility and simplicity, eliminating the need for per-network numerical optimization. Moreover, we establish a theoretical connection between φ and the condition number, adding interpretation and justification to our approach. Besides, we prove the convergence for different values of φ. We offer practical guidelines for its selection via theoretical analysis and empirical results. Extensive experimental results show that UBA consistently surpasses the commonly-used schedules across diverse vision and language tasks, spanning network architectures (e.g., ResNet, OLMo) and scales, under different training-iteration budgets.
Big Data and the Global Past: AI, Complexity Science and the Co-Evolution of Human Cultures and Environments
Hosted by: Prof. Natasa Przulj
February 11, 2026
12:00pm
Classroom 2
Miljana Radivojević
Associate Professor in Archaeological Science at UCL
Hosted by: Prof. Natasa Przulj
Computational Biology
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Abstract
Big Data and the Global Past: AI, Complexity Science and the Co-Evolution of Human Cultures and Environments
Understanding the deep human past requires analytical frameworks capable of integrating diverse datasets and tracing long-term trajectories of cultural and environmental change. Archaeology—uniquely positioned at the intersection of material culture, ecology, and human behaviour—holds unparalleled potential to address these challenges. This talk presents a suite of pioneering studies in which artificial intelligence, network science, and complexity theory are applied to Eurasian archaeological datasets, offering the most robust quantitative framework to date for modelling cooperation, exchange, and cultural co-evolution. The first part of the talk focuses on the origins of metallurgy in the Balkans between the 6th and 3rd millennia BC, where copper production and circulation first took recognisable regional form. Using trace element and lead isotope analyses from 410 artefacts across c. 80 sites (6200–3200 BC), we apply seven community detection algorithms—including Louvain, Leiden, Spinglass, and Eigenvector methods—to reconstruct prehistoric copper-supply networks. These models reveal stable and meaningful supply communities that correlate strikingly with regional archaeological cultures such as Vinča, KGK VI and Bodrogkeresztúr. By critically evaluating algorithm performance on archaeological compositional data, this case study not only demonstrates the power of network science for reconstructing prehistoric exchange but also challenges the traditional, typology-based concept of “archaeological culture.” It exemplifies how AI and complexity science can rigorously decode patterns of cooperation, resource movement, and social boundaries in the deep past.
Understanding the deep human past requires analytical frameworks capable of integrating diverse datasets and tracing long-term trajectories of cultural and environmental change. Archaeology—uniquely positioned at the intersection of material culture, ecology, and human behaviour—holds unparalleled potential to address these challenges. This talk presents a suite of pioneering studies in which artificial intelligence, network science, and complexity theory are applied to Eurasian archaeological datasets, offering the most robust quantitative framework to date for modelling cooperation, exchange, and cultural co-evolution. The first part of the talk focuses on the origins of metallurgy in the Balkans between the 6th and 3rd millennia BC, where copper production and circulation first took recognisable regional form. Using trace element and lead isotope analyses from 410 artefacts across c. 80 sites (6200–3200 BC), we apply seven community detection algorithms—including Louvain, Leiden, Spinglass, and Eigenvector methods—to reconstruct prehistoric copper-supply networks. These models reveal stable and meaningful supply communities that correlate strikingly with regional archaeological cultures such as Vinča, KGK VI and Bodrogkeresztúr. By critically evaluating algorithm performance on archaeological compositional data, this case study not only demonstrates the power of network science for reconstructing prehistoric exchange but also challenges the traditional, typology-based concept of “archaeological culture.” It exemplifies how AI and complexity science can rigorously decode patterns of cooperation, resource movement, and social boundaries in the deep past.
Machine Learning for Combinatorial Optimization
Hosted by: Prof. Zhiqiang Xu
February 11, 2026
4:00pm
MBZUAI - Classroom 3
Junchi Yan
Professor in Artificial Intelligence at Shanghai Jiao Tong University
Hosted by: Prof. Zhiqiang Xu
Machine Learning
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Abstract
Machine Learning for Combinatorial Optimization
In this talk, I will discuss the development of machine learning for combinatorial optimization, covering general methodology and especially generative models for AI4Opt. I will show how the idea of diffusion models could be introduced to solve the notoriously hard combinatorial problems. I will also share some forward-looking ideas on future research directions.
In this talk, I will discuss the development of machine learning for combinatorial optimization, covering general methodology and especially generative models for AI4Opt. I will show how the idea of diffusion models could be introduced to solve the notoriously hard combinatorial problems. I will also share some forward-looking ideas on future research directions.
Continual Learning (… and Forgetting too)
Hosted by: Muhammad Haris Khan
February 11, 2026
10:30am
MBZUAI - Classroom 4
Subhasis Chaudhuri
Professor in Electrical Engineering at Indian Institute of Technology Bombay
Hosted by: Muhammad Haris Khan
Computer Vision
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Abstract
Continual Learning (… and Forgetting too)
We spend a lot of time in training a network to recognize different but a fixed number of types of objects in a scene. If we are to induct new object classes subsequently in the recognition engine, should we be retraining the network from scratch again? Can we tweak the network so that it can incrementally learn new classes of object? Unfortunately, any attempt to incrementally learn new concepts may also lead to forgetting, often catastrophic, of previously learnt concepts. Similarly, can we also selectively forget a few concepts that may be required for socio-technical reasons? In this talk, we shall discuss how some of these objectives can be achieved.
We spend a lot of time in training a network to recognize different but a fixed number of types of objects in a scene. If we are to induct new object classes subsequently in the recognition engine, should we be retraining the network from scratch again? Can we tweak the network so that it can incrementally learn new classes of object? Unfortunately, any attempt to incrementally learn new concepts may also lead to forgetting, often catastrophic, of previously learnt concepts. Similarly, can we also selectively forget a few concepts that may be required for socio-technical reasons? In this talk, we shall discuss how some of these objectives can be achieved.
Reinforcement Learning in Health-related Sequential Decision Problems: From Dynamic Treatment Regimes to Mobile Health
Hosted by: Prof. Marcos Matabuena
February 13, 2026
1:00pm
MBZUAI - Classroom 4
Bibhas Chakraborty
Associate Professor and Deputy Director in Biomedical Data Science at Duke-NUS Medical School
Hosted by: Prof. Marcos Matabuena
Computational Biology
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Abstract
Reinforcement Learning in Health-related Sequential Decision Problems: From Dynamic Treatment Regimes to Mobile Health
In recent years, Reinforcement Learning (RL) has gained a prominent position in addressing health-related sequential decision-making problems. In this talk, we will discuss two such sequential decision-making problems: (1) dynamic treatment regimes (DTRs), i.e., clinical decision rules for adapting the type, dosage and timing of treatment according to an individual patient’s characteristics and evolving health status; and (2) just-in-time adaptive interventions (JITAIs) in mobile app-based behavioral nudges in population health. Specifically, we will illustrate the similarities and differences between these two types of RL problems (e.g., offline vs. online RL), common algorithms used in these two settings (e.g., Q-learning vs. Thomson sampling), and real-life case studies.
In recent years, Reinforcement Learning (RL) has gained a prominent position in addressing health-related sequential decision-making problems. In this talk, we will discuss two such sequential decision-making problems: (1) dynamic treatment regimes (DTRs), i.e., clinical decision rules for adapting the type, dosage and timing of treatment according to an individual patient’s characteristics and evolving health status; and (2) just-in-time adaptive interventions (JITAIs) in mobile app-based behavioral nudges in population health. Specifically, we will illustrate the similarities and differences between these two types of RL problems (e.g., offline vs. online RL), common algorithms used in these two settings (e.g., Q-learning vs. Thomson sampling), and real-life case studies.
