October 15 – 17, 2025 — 2nd MBZUAI School of Digital Public Health Symposium 
October 19 – 23, 2025 — MBZUAI Reception at International Conference on Computer Vision (ICCV) 2025 
December 15 – 18, 2025 — MBZUAI Reception at International Conference on Statistics and Data Science (ICSDS) 2025 
Sharm El Sheikh, Egypt 
Rotterdam, Netherlands 
Vienna, Austria 
United Kingdom 
Vancouver, Canada 
Yokohama, Japan MBZUAI Nexus Speaker Series
Upcoming AI Talks
Past AI Talks
Exploring the Power of Speech: How Synthetic Voices Shape User Perception and Behavior
Hosted by: Prof. Elizabeth Churchill
September 29, 2025
11:00am
Classroom 1
Matuesz Dubiel
Research Associate, University of Luxembourg
Hosted by: Prof. Elizabeth Churchill
Human-Computer Interaction
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Abstract
Exploring the Power of Speech: How Synthetic Voices Shape User Perception and Behavior
Speech-enabled Conversational Agents (CAs), such as Amazon Alexa, Apple Siri, and Google Assistant, are becoming increasingly more popular interaction platforms for users to engage with their mobile devices and smart speakers. While CAs have the potential to support users in achieving behavioural change goals, such as increasing physical activity or improving productivity at work, they can also lead to complacent behaviour and a lack of reflection. In the first part of my presentation, I will discuss how different types of synthetic voices that vary in terms of prosodic qualities and method of synthesis can affect users' perception of CAs, and what impact they can have on users' behaviour in decision-making tasks. Specifically, we will analyse how differing voice characteristics can affect user trust and engagement. In the second part, we will explore several research avenues to enable the design and development of proactive conversational agents that can effectively support users while preserving their agency.
Speech-enabled Conversational Agents (CAs), such as Amazon Alexa, Apple Siri, and Google Assistant, are becoming increasingly more popular interaction platforms for users to engage with their mobile devices and smart speakers. While CAs have the potential to support users in achieving behavioural change goals, such as increasing physical activity or improving productivity at work, they can also lead to complacent behaviour and a lack of reflection. In the first part of my presentation, I will discuss how different types of synthetic voices that vary in terms of prosodic qualities and method of synthesis can affect users' perception of CAs, and what impact they can have on users' behaviour in decision-making tasks. Specifically, we will analyse how differing voice characteristics can affect user trust and engagement. In the second part, we will explore several research avenues to enable the design and development of proactive conversational agents that can effectively support users while preserving their agency.
Computational and AI-Driven Design of Random Heteropolymers as Protein Mimics
Hosted by: Prof. Mladen Kolar
September 29, 2025
9.30am
Classroom 6
Haiyan Huang
Professor of Statistics, UC Berkeley
Hosted by: Prof. Mladen Kolar
Statistics and Data Science
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Abstract
Computational and AI-Driven Design of Random Heteropolymers as Protein Mimics
Synthetic random heteropolymers (RHPs), composed of a predefined set of monomers, offer a promising strategy for creating protein mimicking materials with tailored biochemical functions. When designed appropriately, RHPs can replicate protein behavior, enabling applications in drug delivery, therapeutic protein stabilization, biosensing, tissue engineering, and medical diagnostics. However, designing RHPs that achieve specific biological functions in a time- and cost-effective manner remains a major challenge. In this talk, I will review this problem and discuss several successful efforts we have made to address it, using statistical, computational, and AI approaches. These include a generalized semi-hidden Markov model (GSHMM) and a hybrid variational autoencoder (VAE), which we call DeepRHP and implement within a semi-supervised framework. Both methods are designed to capture the structures of critical chemical features as well as individual RHP sequence patterns, but they offer different advantages in terms of interpretability and flexibility. These studies highlight the potential of computational approaches to accelerate the rational design of RHPs for a wide range of biological, medical, and healthcare applications.
Synthetic random heteropolymers (RHPs), composed of a predefined set of monomers, offer a promising strategy for creating protein mimicking materials with tailored biochemical functions. When designed appropriately, RHPs can replicate protein behavior, enabling applications in drug delivery, therapeutic protein stabilization, biosensing, tissue engineering, and medical diagnostics. However, designing RHPs that achieve specific biological functions in a time- and cost-effective manner remains a major challenge. In this talk, I will review this problem and discuss several successful efforts we have made to address it, using statistical, computational, and AI approaches. These include a generalized semi-hidden Markov model (GSHMM) and a hybrid variational autoencoder (VAE), which we call DeepRHP and implement within a semi-supervised framework. Both methods are designed to capture the structures of critical chemical features as well as individual RHP sequence patterns, but they offer different advantages in terms of interpretability and flexibility. These studies highlight the potential of computational approaches to accelerate the rational design of RHPs for a wide range of biological, medical, and healthcare applications.
3D Reconstruction in the era of Machine Learning and Gaussian Splatting
Hosted by: Prof. Ian Reid
September 30, 2025
11:00am
Classroom 3
Ravi Garg
Future Making Fellow, Australian Institute for Machine Learning, University of Adelaide
Hosted by: Prof. Ian Reid
Computer Vision
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Abstract
3D Reconstruction in the era of Machine Learning and Gaussian Splatting
"The problem of 3D reconstruction from multiple views has traditionally been posed as an inverse problem: estimating structure, appearance, and camera parameters from observed images. Classical approaches emphasised minimal parametrisation, simplified image formation models, and the use of hand-crafted priors to render the optimisation well-posed. This paradigm has recently been challenged by the emergence of overparameterised scene representations—such as Radiance Fields and Gaussian Splatting, and overparameterised camera models. These representations enable efficient inference, rapid novel-view synthesis, and offer greater flexibility in training neural networks for 3D reconstruction. This talk will examine the implications of such overparameterised formulations in recovering scene geometry. I will present recent works demonstrating that while the additional flexibility afforded by overparameterisation can be beneficial, it often necessitates careful geometric regularisation. I will discuss often overlooked considerations in employing these representations by both neural and non-neural 3D reconstruction techniques."
"The problem of 3D reconstruction from multiple views has traditionally been posed as an inverse problem: estimating structure, appearance, and camera parameters from observed images. Classical approaches emphasised minimal parametrisation, simplified image formation models, and the use of hand-crafted priors to render the optimisation well-posed. This paradigm has recently been challenged by the emergence of overparameterised scene representations—such as Radiance Fields and Gaussian Splatting, and overparameterised camera models. These representations enable efficient inference, rapid novel-view synthesis, and offer greater flexibility in training neural networks for 3D reconstruction. This talk will examine the implications of such overparameterised formulations in recovering scene geometry. I will present recent works demonstrating that while the additional flexibility afforded by overparameterisation can be beneficial, it often necessitates careful geometric regularisation. I will discuss often overlooked considerations in employing these representations by both neural and non-neural 3D reconstruction techniques."
Towards biological discovery with foundation models: applications in neuroscience
Hosted by: Prof. Eduardo Beltrame
September 30, 2025
3.00pm
Classroom 3
Ravi Solanki
Co-founder and CEO of Prima Mente
Hosted by: Prof. Eduardo Beltrame
Computational Biology
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Abstract
Towards biological discovery with foundation models: applications in neuroscience
Foundation models offer the potential to transform discovery for the biological science, promising novel biomarkers as well as new directions for therapeutic application. Design of such models however can be challenging, and their application can be equally difficult. Here, I will discuss our work generating the infrastructure to enable biological discovery robustly, efficiently, and at-scale with foundation modelling. Applied specifically to the neurosciences and the study of neurodegenerative conditions like Alzheimer’s and Parkinson’s, we have shown foundation models can learn complex representations of disease, and derive novel biomarkers and therapeutic directions. I will also share our thinking about future directions for frontier AI for treating these major causes of global mortality.
Foundation models offer the potential to transform discovery for the biological science, promising novel biomarkers as well as new directions for therapeutic application. Design of such models however can be challenging, and their application can be equally difficult. Here, I will discuss our work generating the infrastructure to enable biological discovery robustly, efficiently, and at-scale with foundation modelling. Applied specifically to the neurosciences and the study of neurodegenerative conditions like Alzheimer’s and Parkinson’s, we have shown foundation models can learn complex representations of disease, and derive novel biomarkers and therapeutic directions. I will also share our thinking about future directions for frontier AI for treating these major causes of global mortality.
The Age of AI: And Our Human Future
Hosted by: Prof. Timothy Baldwin
October 2, 2025
3.30pm
Executive Theatre, KC
Daniel Huttenlocher
Dean, MIT Schwarzman College of Computing
Hosted by: Prof. Timothy Baldwin
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Abstract
The Age of AI: And Our Human Future
In this talk we look at how AI is changing discovery, knowledge, human interaction, and how we understand the world around us. These changes are becoming more prominent with every passing moment, and this session endeavors to help build insights into the development and deployment of AI for broad benefit. The talk will also present a brief overview of the MIT Schwarzman College of Computing.
In this talk we look at how AI is changing discovery, knowledge, human interaction, and how we understand the world around us. These changes are becoming more prominent with every passing moment, and this session endeavors to help build insights into the development and deployment of AI for broad benefit. The talk will also present a brief overview of the MIT Schwarzman College of Computing.
A Formal but Pragmatic Foundation for General-Purpose Operating
Hosted by: Prof. Elizabeth Churchill
October 9, 2025
4:00pm
Classroom 1
Timothy Roscoe
Professor of Computer Science, ETH Zurich
Hosted by: Prof. Elizabeth Churchill
Human-Computer Interaction
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Abstract
A Formal but Pragmatic Foundation for General-Purpose Operating
The Operating System (OS) is fundamental to the correct working of any non-trivial computer system, and general-purpose OSes like Linux (and Android), Windows, iOS and MacOS are the central component of the infrastructure of modern computing and communications, from mobile phones to cloud providers. Modern AI would not be possible without OS software providing required scaling and communication between distributed tasks. Faults attributable to OS flaws have serious consequences ranging from security breaches to global-scale outages. Despite this, general-purpose OS design and implementation today remains surprisingly ad-hoc, based on a simplistic architecture proposed decades ago for machines designed in 1970s. Since then, system hardware has changed beyond recognition: computers are complex networks of cores, devices, management engines, and accelerators, all running code ignored by the nominal OS. This broad disconnect between hardware reality and OS structure underlies many security and reliability flaws, and will not go away without a radical change in approach. I'll talk about our attempts to put general-purpose OS development on a solid foundation for the first time, based on a formal framework for capturing the software-visible semantics of all the hardware in complete, real computers. Above this, we are working on tooling to assemble an OS for modern heterogeneous servers and systems-on-chip which can incorporate existing drivers, firmware, and application environments, but nevertheless offer strong, formal platform-wide guarantees of application isolation and security.
The Operating System (OS) is fundamental to the correct working of any non-trivial computer system, and general-purpose OSes like Linux (and Android), Windows, iOS and MacOS are the central component of the infrastructure of modern computing and communications, from mobile phones to cloud providers. Modern AI would not be possible without OS software providing required scaling and communication between distributed tasks. Faults attributable to OS flaws have serious consequences ranging from security breaches to global-scale outages. Despite this, general-purpose OS design and implementation today remains surprisingly ad-hoc, based on a simplistic architecture proposed decades ago for machines designed in 1970s. Since then, system hardware has changed beyond recognition: computers are complex networks of cores, devices, management engines, and accelerators, all running code ignored by the nominal OS. This broad disconnect between hardware reality and OS structure underlies many security and reliability flaws, and will not go away without a radical change in approach. I'll talk about our attempts to put general-purpose OS development on a solid foundation for the first time, based on a formal framework for capturing the software-visible semantics of all the hardware in complete, real computers. Above this, we are working on tooling to assemble an OS for modern heterogeneous servers and systems-on-chip which can incorporate existing drivers, firmware, and application environments, but nevertheless offer strong, formal platform-wide guarantees of application isolation and security.
Ubiquitous AI for Health
Hosted by: Prof. Elizabeth Churchill
October 9, 2025
11:00am
Afsaneh Doryab
Assistant Professor of Computer Science and Systems Engineering, University of Virginia
Hosted by: Prof. Elizabeth Churchill
Human-Computer Interaction
Join Stream
Abstract
Ubiquitous AI for Health
Harnessing data streams generated by widely used devices, such as smartphones, wearables, and embedded sensors, allows AI algorithms to continuously model, detect, and predict people's biobehavioural and social states. These algorithms can then use the resulting models to deliver personalized services, recommendations, and interventions. However, this capability also introduces new technical challenges related to data collection, processing, algorithm development, modelling, and interpretation. In this talk, I will discuss my research approaches to address some of these challenges in the context of health and wellness applications. I will demonstrate how we leverage multimodal mobile data streams to model aspects such as circadian rhythm variability. Additionally, I will describe how we integrate biobehavioural models to create innovative strategies, including music melodies designed for personalized health status communication.
Harnessing data streams generated by widely used devices, such as smartphones, wearables, and embedded sensors, allows AI algorithms to continuously model, detect, and predict people's biobehavioural and social states. These algorithms can then use the resulting models to deliver personalized services, recommendations, and interventions. However, this capability also introduces new technical challenges related to data collection, processing, algorithm development, modelling, and interpretation. In this talk, I will discuss my research approaches to address some of these challenges in the context of health and wellness applications. I will demonstrate how we leverage multimodal mobile data streams to model aspects such as circadian rhythm variability. Additionally, I will describe how we integrate biobehavioural models to create innovative strategies, including music melodies designed for personalized health status communication.
Navigating Privacy, Data Protection, AI, and IP Laws in AI Development: A Practical Approach
Hosted by: Prof. Elizabeth Churchill
October 15, 2025
2:00 pm
Classroom 6
Dr. Renato Leite Monteiro
VP of Legal Assurance & Data Protection, e&
Hosted by: Prof. Elizabeth Churchill
Human-Computer Interaction
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Abstract
Navigating Privacy, Data Protection, AI, and IP Laws in AI Development: A Practical Approach
VP - Privacy, Data Protection and AI @ e&. Former Global Head of Privacy @ X. PhD from the University of São Paulo (USP). Fellow at the Oxford Internet Institute (OII). Professor of Law. LL.M from New York University (NYU) and the National University of Singapore (NUS).
VP - Privacy, Data Protection and AI @ e&. Former Global Head of Privacy @ X. PhD from the University of São Paulo (USP). Fellow at the Oxford Internet Institute (OII). Professor of Law. LL.M from New York University (NYU) and the National University of Singapore (NUS).
