January 5 – 6, 2026 — 2nd MBZUAI Speech & NLP Symposium 
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
Algorithmic Foundations of Online Decision-Making: From Operational Constraints to Generative AI
Hosted by: Prof. Chih-Jen Lin
January 13, 2026
10:00am
Virtual
Jianyu Xu
Postdoctoral research associate in the Machine Learning Department of Carnegie Mellon University
Hosted by: Prof. Chih-Jen Lin
Machine Learning
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Abstract
Algorithmic Foundations of Online Decision-Making: From Operational Constraints to Generative AI
"Online decision-making is the core engine behind intelligent systems that must learn from incomplete feedback and act in real-time, with ubiquitous applications ranging over adaptive recommendation system, e-commerce platform, autonomous vehicle navigation, and personalized healthcare assistance. To operate effectively, these agents must balance exploration against exploitation while navigating uncertainty and satisfying complex constraints. In this talk, I will present a research program for reliable and adaptive sequential decision-making, that bridges theoretical foundations with crucial real-world deployments. I will begin by briefly outlining decision-making in dynamic pricing under censored feedback, before extending this to various operational constraints like fairness, supply, and multi-stage bottlenecks. Then I will introduce ""Generative Online Learning"" as a combination of traditional decision-making framework with the emerging power of Generative AI, where agents strategically decide to either generate novel actions or select from the existing action list. I will demonstrate the impact of this framework through the architecture and deployment of a safe, adaptive maternal health chatbot. Finally, I will conclude with future directions in multi-party online learning, and adaptive in-context decision planning"
"Online decision-making is the core engine behind intelligent systems that must learn from incomplete feedback and act in real-time, with ubiquitous applications ranging over adaptive recommendation system, e-commerce platform, autonomous vehicle navigation, and personalized healthcare assistance. To operate effectively, these agents must balance exploration against exploitation while navigating uncertainty and satisfying complex constraints. In this talk, I will present a research program for reliable and adaptive sequential decision-making, that bridges theoretical foundations with crucial real-world deployments. I will begin by briefly outlining decision-making in dynamic pricing under censored feedback, before extending this to various operational constraints like fairness, supply, and multi-stage bottlenecks. Then I will introduce ""Generative Online Learning"" as a combination of traditional decision-making framework with the emerging power of Generative AI, where agents strategically decide to either generate novel actions or select from the existing action list. I will demonstrate the impact of this framework through the architecture and deployment of a safe, adaptive maternal health chatbot. Finally, I will conclude with future directions in multi-party online learning, and adaptive in-context decision planning"
AI, Machine Learning, and Medical Statistics
Hosted by: Prof. Eran Segal
January 15, 2026
12:00pm
MBZUAI - Classroom 6
Lei Clifton
Programme Director of the MSc in Applied Digital Health at the Nuffield Department of Primary Care Health Sciences & Governing Body Fellow of Reuben College, Oxford University.
Hosted by: Prof. Eran Segal
Computational Biology
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Abstract
AI, Machine Learning, and Medical Statistics
"There is considerable interest in AI for health data science, driven by the rapid growth of available data and declining computational costs. The debate over when to use AI versus classical statistical methods in medical research is long-standing, but merits fresh consideration in light of major methodological advances and increased policy attention. AI-based approaches offer substantial opportunities, while recognising that we may be near the peak of the Gartner hype cycle for AI. Lei argues that AI and classical statistics are best suited to different scenarios and are often complementary. In some domains, AI is widely regarded as essential because of the complexity and multimodality of the data, which are frequently free-form. A key example is unstructured clinical text, where clinical reasoning and summarisation tasks are increasingly addressed by contemporary large language models, a class of generative AI. In domains where either AI or classical statistics could plausibly be used, combining the strengths of both approaches is often the most effective strategy. In this talk, Lei will illustrate how she has integrated AI, machine learning, and medical statistics in her research through worked examples (and her own paintings). The session has two parts: Part I: Large language models (LLMs) for risk prediction and clinical tasks Part II: Combining machine learning and medical statistics This talk is suitable for a mixed audience interested in data modelling and its application in real-world clinical settings."
"There is considerable interest in AI for health data science, driven by the rapid growth of available data and declining computational costs. The debate over when to use AI versus classical statistical methods in medical research is long-standing, but merits fresh consideration in light of major methodological advances and increased policy attention. AI-based approaches offer substantial opportunities, while recognising that we may be near the peak of the Gartner hype cycle for AI. Lei argues that AI and classical statistics are best suited to different scenarios and are often complementary. In some domains, AI is widely regarded as essential because of the complexity and multimodality of the data, which are frequently free-form. A key example is unstructured clinical text, where clinical reasoning and summarisation tasks are increasingly addressed by contemporary large language models, a class of generative AI. In domains where either AI or classical statistics could plausibly be used, combining the strengths of both approaches is often the most effective strategy. In this talk, Lei will illustrate how she has integrated AI, machine learning, and medical statistics in her research through worked examples (and her own paintings). The session has two parts: Part I: Large language models (LLMs) for risk prediction and clinical tasks Part II: Combining machine learning and medical statistics This talk is suitable for a mixed audience interested in data modelling and its application in real-world clinical settings."
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
TBC
Miljana Radivojević
Associate Professor of Archaeological Science at the UCL Institute of Archaeology
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.
