All Previous AI Speaker Series at MBZUAI
The Age of AI: And Our Human Future
Hosted by: Prof. Timothy Baldwin
October 2, 2025
Daniel Huttenlocher
The Age of AI: And Our Human Future
Hosted by: Prof. Timothy Baldwin
Featured
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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.
Staged Encounters: Dance as a Testbed for Human–Robot Interaction
Hosted by: Prof. Ivan Laptev
August 26, 2025
Merritt Moore
Staged Encounters: Dance as a Testbed for Human–Robot Interaction
Hosted by: Prof. Ivan Laptev
Featured
Computer Vision
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Abstract
Staged Encounters: Dance as a Testbed for Human–Robot Interaction
Science fiction has long been our window to the future, predicting technological advancements and their societal impacts. Fiction doesn’t just entertain—it prepares us to navigate the moral and emotional complexities yet to come. Extending this inquiry into practice, Dr. Merritt Moore shares how dancing with robots has become a living experiment in future human–robot interactions and relationships. Through staged and improvised duets, she tests how machines function not merely as tools but as partners in expression and creativity, raising questions about authorship, agency, and emotional impact. This talk explores how choreography and robotics can inform one another, shaping both creative practice and future possibilities.
Science fiction has long been our window to the future, predicting technological advancements and their societal impacts. Fiction doesn’t just entertain—it prepares us to navigate the moral and emotional complexities yet to come. Extending this inquiry into practice, Dr. Merritt Moore shares how dancing with robots has become a living experiment in future human–robot interactions and relationships. Through staged and improvised duets, she tests how machines function not merely as tools but as partners in expression and creativity, raising questions about authorship, agency, and emotional impact. This talk explores how choreography and robotics can inform one another, shaping both creative practice and future possibilities.
Please meet AI, our dear new colleague. In other words: can scientists and machines truly cooperate?
Hosted by: Prof. Preslav Nakov
August 18, 2025
Iryna Gurevych
Please meet AI, our dear new colleague. In other words: can scientists and machines truly cooperate?
Hosted by: Prof. Preslav Nakov
Featured
Natural Language Processing
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Abstract
Please meet AI, our dear new colleague. In other words: can scientists and machines truly cooperate?
How can AI and LLMs facilitate the work of scientists in different stages of the research process? Can technology even make scientists obsolete? The role of AI and Large Language Models (LLMs) in science as the target application domain has recently been rapidly growing. This includes assessing the impact of scientific work, facilitating writing and revising manuscripts as well as intelligent support for manuscript quality assessment, peer-review and scientific discussions. The talk will illustrate such methods and models using several tasks from the scientific domain. We argue that while AI and LLMs can effectively support and augment specific steps of the research process, expert-AI collaboration may be a more promising mode for complex research tasks.
How can AI and LLMs facilitate the work of scientists in different stages of the research process? Can technology even make scientists obsolete? The role of AI and Large Language Models (LLMs) in science as the target application domain has recently been rapidly growing. This includes assessing the impact of scientific work, facilitating writing and revising manuscripts as well as intelligent support for manuscript quality assessment, peer-review and scientific discussions. The talk will illustrate such methods and models using several tasks from the scientific domain. We argue that while AI and LLMs can effectively support and augment specific steps of the research process, expert-AI collaboration may be a more promising mode for complex research tasks.
Advancing Spatio-Temporal Statistics in Geo-Environmental Data Science through Deep Learning and High Performance Computing
Hosted by: Prof. Souhaib Ben Taieb
October 22, 2025
Ying Sun
Advancing Spatio-Temporal Statistics in Geo-Environmental Data Science through Deep Learning and High Performance Computing
Hosted by: Prof. Souhaib Ben Taieb
Statistics and Data Science
Abstract
Advancing Spatio-Temporal Statistics in Geo-Environmental Data Science through Deep Learning and High Performance Computing
In this talk, I will discuss the contributions and ongoing research of my Environmental Statistics Research Group in the area of spatio-temporal statistics, with a particular focus on leveraging deep learning and high performance computing for spatio-temporal analysis in Geo-Environmental Data Science. I will introduce the developed innovative software tools such as ExaGeoStat, ParallelVecchiaGP, and DeepKriging, which support the analysis of large-scale geostatistical datasets. During this presentation, I will also showcase environmental applications to air quality modeling and prediction.
In this talk, I will discuss the contributions and ongoing research of my Environmental Statistics Research Group in the area of spatio-temporal statistics, with a particular focus on leveraging deep learning and high performance computing for spatio-temporal analysis in Geo-Environmental Data Science. I will introduce the developed innovative software tools such as ExaGeoStat, ParallelVecchiaGP, and DeepKriging, which support the analysis of large-scale geostatistical datasets. During this presentation, I will also showcase environmental applications to air quality modeling and prediction.
High-Performance Statistical Computing: The Case of ExaGeoStat for Large-Scale Spatial Data Science
Hosted by: Prof. Souhaib Ben Taieb
October 20, 2025
Marc Genton
High-Performance Statistical Computing: The Case of ExaGeoStat for Large-Scale Spatial Data Science
Hosted by: Prof. Souhaib Ben Taieb
Statistics and Data Science
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Abstract
High-Performance Statistical Computing: The Case of ExaGeoStat for Large-Scale Spatial Data Science
The new field of High-Performance Statistical Computing (HPSC) reflects the emergence of a statistical computing community focused on working with large computing platforms and producing software for various applications. For example, spatial data science relies on some fundamental problems such as: 1) Spatial Gaussian likelihood inference; 2) Spatial kriging; 3) Gaussian random field simulations; 4) Multivariate Gaussian probabilities; and 5) Robust inference for spatial data. These problems develop into very challenging tasks when the number of spatial locations grows large. Moreover, they are the cornerstone of more sophisticated procedures involving non-Gaussian distributions, multivariate random fields, or space-time processes. Parallel computing becomes necessary for avoiding computational and memory restrictions associated with large-scale spatial data science applications. In this talk, I will demonstrate how high-performance computing (HPC) can provide solutions to the aforementioned problems using tile-based linear algebra, tile low-rank approximations, as well as multi- and mixed-precision computational statistics. I will introduce ExaGeoStat, and its R version ExaGeoStatR, a powerful HPSC software that can perform exascale (10^18 flops/s) geostatistics by exploiting the power of existing parallel computing hardware systems, such as shared-memory, possibly equipped with GPUs, and distributed-memory systems, i.e., supercomputers. I will then describe how ExaGeoStat can be used to design competitions on spatial statistics for large datasets and to benchmark new methods developed by statisticians and data scientists for large-scale spatial data science. Finally, I will briefly demonstrate how these techniques were used to build an exascale climate emulator that received the prestigious 2024 ACM Gordon Bell Prize in Climate Modeling.
The new field of High-Performance Statistical Computing (HPSC) reflects the emergence of a statistical computing community focused on working with large computing platforms and producing software for various applications. For example, spatial data science relies on some fundamental problems such as: 1) Spatial Gaussian likelihood inference; 2) Spatial kriging; 3) Gaussian random field simulations; 4) Multivariate Gaussian probabilities; and 5) Robust inference for spatial data. These problems develop into very challenging tasks when the number of spatial locations grows large. Moreover, they are the cornerstone of more sophisticated procedures involving non-Gaussian distributions, multivariate random fields, or space-time processes. Parallel computing becomes necessary for avoiding computational and memory restrictions associated with large-scale spatial data science applications. In this talk, I will demonstrate how high-performance computing (HPC) can provide solutions to the aforementioned problems using tile-based linear algebra, tile low-rank approximations, as well as multi- and mixed-precision computational statistics. I will introduce ExaGeoStat, and its R version ExaGeoStatR, a powerful HPSC software that can perform exascale (10^18 flops/s) geostatistics by exploiting the power of existing parallel computing hardware systems, such as shared-memory, possibly equipped with GPUs, and distributed-memory systems, i.e., supercomputers. I will then describe how ExaGeoStat can be used to design competitions on spatial statistics for large datasets and to benchmark new methods developed by statisticians and data scientists for large-scale spatial data science. Finally, I will briefly demonstrate how these techniques were used to build an exascale climate emulator that received the prestigious 2024 ACM Gordon Bell Prize in Climate Modeling.
AMA - Chip Design, Software Design, and Using AI
Hosted by: Prof. Abdulrahman Mahmoud
October 16, 2025
Jim Keller
AMA - Chip Design, Software Design, and Using AI
Hosted by: Prof. Abdulrahman Mahmoud
Undergraduate Division
Abstract
AMA - Chip Design, Software Design, and Using AI
This will be a conversational "ask me anything" session
This will be a conversational "ask me anything" session
Language Model × Robotics – From Embodied Navigation to AI-Driven Robot Hand Design
Hosted by: Prof. Yutong Xie
October 16, 2025
Yanyuan Qiao
Language Model × Robotics – From Embodied Navigation to AI-Driven Robot Hand Design
Hosted by: Prof. Yutong Xie
Computer Vision
Abstract
Language Model × Robotics – From Embodied Navigation to AI-Driven Robot Hand Design
"Recent advances in language models are transforming how robots can perceive, reason, and act. This talk presents a series of works that explore how language models, used both as pretrained representations and interactive reasoning engines, can be applied to develop intelligent embodied agents. The studies span tasks from embodied navigation in 3D environments to automatic design of robot morphologies for manipulation. The first part focuses on embodied navigation. I began by exploring how to improve an agent’s perception of temporal and historical context through multimodal pretraining. Building on this foundation, I then examined how large language models can assist decision-making—by interpreting ambiguous instructions and injecting external knowledge to support generalization. Taking this further, we investigated using language models directly as agents, enabling them to perform navigation in continuous environments without additional training. To systematically understand what these models can and cannot do, we introduced a benchmark that evaluates key embodied capabilities, such as instruction comprehension, spatial reasoning, and alignment between language and action. The second part turns to robot design. I present our recent work on AI-driven robot hand generation, where task descriptions are translated into diverse and functional morphologies. This system leverages language models to capture user intent and guides structural generation through reasoning and feedback. Together, these studies explore a central question: how far can language models take us in embodied robotics? From interpreting instructions to designing physical form, they reveal both the opportunities and current frontiers in this rapidly evolving intersection."
"Recent advances in language models are transforming how robots can perceive, reason, and act. This talk presents a series of works that explore how language models, used both as pretrained representations and interactive reasoning engines, can be applied to develop intelligent embodied agents. The studies span tasks from embodied navigation in 3D environments to automatic design of robot morphologies for manipulation. The first part focuses on embodied navigation. I began by exploring how to improve an agent’s perception of temporal and historical context through multimodal pretraining. Building on this foundation, I then examined how large language models can assist decision-making—by interpreting ambiguous instructions and injecting external knowledge to support generalization. Taking this further, we investigated using language models directly as agents, enabling them to perform navigation in continuous environments without additional training. To systematically understand what these models can and cannot do, we introduced a benchmark that evaluates key embodied capabilities, such as instruction comprehension, spatial reasoning, and alignment between language and action. The second part turns to robot design. I present our recent work on AI-driven robot hand generation, where task descriptions are translated into diverse and functional morphologies. This system leverages language models to capture user intent and guides structural generation through reasoning and feedback. Together, these studies explore a central question: how far can language models take us in embodied robotics? From interpreting instructions to designing physical form, they reveal both the opportunities and current frontiers in this rapidly evolving intersection."
Towards AI Superhuman Reasoning & the future of knowledge discovery
Hosted by: Prof. Monojit Choudhury
October 16, 2025
Thang Luong
Towards AI Superhuman Reasoning & the future of knowledge discovery
Hosted by: Prof. Monojit Choudhury
Natural Language Processing
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Abstract
Towards AI Superhuman Reasoning & the future of knowledge discovery
In this talk, I will discuss recent advances in AI for Mathematics, from AlphaGeometry and AlphaProof to the recent Gemini Deep Think, which achieved a historic gold-medal level performance at the International Mathematical Olympiad 2025. Through these technological breakthroughs, I will also share my thoughts towards the future of AI for knowledge discovery.
In this talk, I will discuss recent advances in AI for Mathematics, from AlphaGeometry and AlphaProof to the recent Gemini Deep Think, which achieved a historic gold-medal level performance at the International Mathematical Olympiad 2025. Through these technological breakthroughs, I will also share my thoughts towards the future of AI for knowledge discovery.
Navigating Privacy, Data Protection, AI, and IP Laws in AI Development: A Practical Approach
Hosted by: Prof. Elizabeth Churchill
October 15, 2025
Dr. Renato Leite Monteiro
Navigating Privacy, Data Protection, AI, and IP Laws in AI Development: A Practical Approach
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).
Human-Centric AI: Learning and Co-Creating Humans in 2D, 3D and 4D.
Hosted by: Prof. Elizabeth Churchill
October 13, 2025
Yi Zhou
Human-Centric AI: Learning and Co-Creating Humans in 2D, 3D and 4D.
Hosted by: Prof. Elizabeth Churchill
Human-Computer Interaction
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Abstract
Human-Centric AI: Learning and Co-Creating Humans in 2D, 3D and 4D.
This talk explores how AI can learn from humans and co-create with humans to capture the richness of human appearance, motion, interactions, and personality. I will present three lines of work: (1) building large-scale 4D datasets such as HUMOTO, which capture human–human and human–object interactions with industry-standard fidelity; (2) developing novel 3D representations and differentiable simulations, including DMesh and Digital Salon, for efficient modeling of complex geometry and dynamics; and (3) designing generative tools that enable intuitive, user-guided creation of digital humans and their interactions and behaviors in scenes. Together, these efforts advance a vision of human-centric generative AI: systems that learn about humans, collaborate with humans, and empower creativity across 2D, 3D, and 4D domains.
This talk explores how AI can learn from humans and co-create with humans to capture the richness of human appearance, motion, interactions, and personality. I will present three lines of work: (1) building large-scale 4D datasets such as HUMOTO, which capture human–human and human–object interactions with industry-standard fidelity; (2) developing novel 3D representations and differentiable simulations, including DMesh and Digital Salon, for efficient modeling of complex geometry and dynamics; and (3) designing generative tools that enable intuitive, user-guided creation of digital humans and their interactions and behaviors in scenes. Together, these efforts advance a vision of human-centric generative AI: systems that learn about humans, collaborate with humans, and empower creativity across 2D, 3D, and 4D domains.
A Formal but Pragmatic Foundation for General-Purpose Operating Systems
Hosted by: Prof. Elizabeth Churchill
October 9, 2025
Timothy Roscoe
A Formal but Pragmatic Foundation for General-Purpose Operating Systems
Hosted by: Prof. Elizabeth Churchill
Human-Computer Interaction
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Abstract
A Formal but Pragmatic Foundation for General-Purpose Operating Systems
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
Afsaneh Doryab
Ubiquitous AI for Health
Hosted by: Prof. Elizabeth Churchill
Human-Computer Interaction
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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.
3D Reconstruction in the era of Machine Learning and Gaussian Splatting
Hosted by: Prof. Ian Reid
September 30, 2025
Ravi Garg
3D Reconstruction in the era of Machine Learning and Gaussian Splatting
Hosted by: Prof. Ian Reid
Computer Vision
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
Ravi Solanki
Towards biological discovery with foundation models: applications in neuroscience
Hosted by: Prof. Eduardo Beltrame
Computational Biology
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.
Exploring the Power of Speech: How Synthetic Voices Shape User Perception and Behavior
Hosted by: Prof. Elizabeth Churchill
September 29, 2025
Matuesz Dubiel
Exploring the Power of Speech: How Synthetic Voices Shape User Perception and Behavior
Hosted by: Prof. Elizabeth Churchill
Human-Computer Interaction
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
Haiyan Huang
Computational and AI-Driven Design of Random Heteropolymers as Protein Mimics
Hosted by: Prof. Mladen Kolar
Statistics and Data Science
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.
On Generalisation and Learning
Hosted by: Prof. Mladen Kolar
September 24, 2025
Benjamin Guedj
On Generalisation and Learning
Hosted by: Prof. Mladen Kolar
Statistics and Data Science
Abstract
On Generalisation and Learning
"Generalisation is one of the essential problems in machine learning and foundational AI. The PAC-Bayes theory has emerged in the past two decades as a generic and flexible framework to study and enforce generalisation abilities of machine learning algorithms. It leverages the power of Bayesian inference and allows to derive new learning strategies. I will briefly present the key concepts of PAC-Bayes and pinpoint how generalisation-driven principled approaches can help further advance a better mathematical understanding of AI systems, and will highlight a few recent contributions from my group including connections to information theory, with a particular focus on our AISTATS 2024 paper https://proceedings.mlr.press/v238/hellstrom24a in which we present a unifying framework for deriving information-theoretic and PAC-Bayesian generalization bounds based on arbitrary convex comparator functions that quantify the gap between empirical and population loss. References: https://cas5-0-urlprotect.trendmicro.com:443/wis/clicktime/v1/query?url=https%3a%2f%2fbguedj.github.io%2fpublications%2f&umid=22d342e6-1e2d-415e-ac94-86c451c45ff8&rct=1756738884&auth=2558bcdb84e02b0c27cd7aa4822a24989cb4e596-640ea02a57d89009a8841304e29c786fa103dcca"
"Generalisation is one of the essential problems in machine learning and foundational AI. The PAC-Bayes theory has emerged in the past two decades as a generic and flexible framework to study and enforce generalisation abilities of machine learning algorithms. It leverages the power of Bayesian inference and allows to derive new learning strategies. I will briefly present the key concepts of PAC-Bayes and pinpoint how generalisation-driven principled approaches can help further advance a better mathematical understanding of AI systems, and will highlight a few recent contributions from my group including connections to information theory, with a particular focus on our AISTATS 2024 paper https://proceedings.mlr.press/v238/hellstrom24a in which we present a unifying framework for deriving information-theoretic and PAC-Bayesian generalization bounds based on arbitrary convex comparator functions that quantify the gap between empirical and population loss. References: https://cas5-0-urlprotect.trendmicro.com:443/wis/clicktime/v1/query?url=https%3a%2f%2fbguedj.github.io%2fpublications%2f&umid=22d342e6-1e2d-415e-ac94-86c451c45ff8&rct=1756738884&auth=2558bcdb84e02b0c27cd7aa4822a24989cb4e596-640ea02a57d89009a8841304e29c786fa103dcca"
Decoding Genome Instability: Regulatory Rewiring in Osteosarcoma and Beyond
Hosted by: Prof. Eran Segal
September 18, 2025
Yanding Zhao
Decoding Genome Instability: Regulatory Rewiring in Osteosarcoma and Beyond
Hosted by: Prof. Eran Segal
Computational Biology
Abstract
Decoding Genome Instability: Regulatory Rewiring in Osteosarcoma and Beyond
Genome instability in cancer spans from small-scale mutations, such as non-coding SNVs that alter transcription factor motifs, to large-scale structural variants (SVs) and extrachromosomal DNA (ecDNA) that reconfigure the 3D genome. Together, these alterations promote tumor growth and remodel the tumor microenvironment. Yet existing technologies remain siloed—each illuminates one layer of the genome, but none can connect structural change to regulatory consequence in a unified way. My work in the TCGA Pan-Cancer 3D Genome Project established integrative computational frameworks to bridge these gaps, linking variants of different scales to enhancer rewiring. Building on this methodological foundation, I applied and refined this framework in osteosarcoma, the most instability-driven pediatric cancer, providing a natural context to test this framework. Using longitudinal and multi-modal profiling, I identified MYC enhancer hijacking linked to chemoresistance and uncovered high-risk instability trajectories associated with poor prognosis. Spatial and single-cell analyses further revealed that these trajectories propagate into distinct stromal and immune states. Together, these studies show how integrative methods can decode regulatory rewiring across multiple levels, from genome architecture to the tumor microenvironment. Looking forward, I aim to extend this platform beyond osteosarcoma by integrating the Emirati Genome Programme with publicly available genomic resources to advance our understanding of instability-driven regulation and therapeutic opportunities.
Genome instability in cancer spans from small-scale mutations, such as non-coding SNVs that alter transcription factor motifs, to large-scale structural variants (SVs) and extrachromosomal DNA (ecDNA) that reconfigure the 3D genome. Together, these alterations promote tumor growth and remodel the tumor microenvironment. Yet existing technologies remain siloed—each illuminates one layer of the genome, but none can connect structural change to regulatory consequence in a unified way. My work in the TCGA Pan-Cancer 3D Genome Project established integrative computational frameworks to bridge these gaps, linking variants of different scales to enhancer rewiring. Building on this methodological foundation, I applied and refined this framework in osteosarcoma, the most instability-driven pediatric cancer, providing a natural context to test this framework. Using longitudinal and multi-modal profiling, I identified MYC enhancer hijacking linked to chemoresistance and uncovered high-risk instability trajectories associated with poor prognosis. Spatial and single-cell analyses further revealed that these trajectories propagate into distinct stromal and immune states. Together, these studies show how integrative methods can decode regulatory rewiring across multiple levels, from genome architecture to the tumor microenvironment. Looking forward, I aim to extend this platform beyond osteosarcoma by integrating the Emirati Genome Programme with publicly available genomic resources to advance our understanding of instability-driven regulation and therapeutic opportunities.
Bridging Digital and Physical Intelligence: from Generative to Embodied AI and Beyond
September 9, 2025
Yu Zeng
Bridging Digital and Physical Intelligence: from Generative to Embodied AI and Beyond
From State Estimation on Lie Groups to Robot Imagination
September 8, 2025
Gregory S. Chirikjian
From State Estimation on Lie Groups to Robot Imagination
The Human Quotient for Better AI Systems: Agents, Appropriate Reliance, and Alignment
September 8, 2025
Ujwal Gadiraju
The Human Quotient for Better AI Systems: Agents, Appropriate Reliance, and Alignment
Bayesian Monitoring of a Pandemic: A Case Study
September 4, 2025
Edward Boone
Bayesian Monitoring of a Pandemic: A Case Study
Statistical Inference on Fractional Partial Differential Equations
September 4, 2025
Ryad Ghanam
Statistical Inference on Fractional Partial Differential Equations
Testing composite null hypotheses with high-dimensional dependent data
September 2, 2025
Hongyuan Cao
Testing composite null hypotheses with high-dimensional dependent data
Building AI Systems for Sustainable Automotive Behaviors
September 2, 2025
David Ayman Shamma
Building AI Systems for Sustainable Automotive Behaviors
DB+AI: A Paradigm to Stimulate the Value of Data
August 27, 2025
Yong Zhang
DB+AI: A Paradigm to Stimulate the Value of Data
Memorization-to-Generalization in Foundation Model Pretraining: Through the Lens of Pathway Optimization
July 29, 2025
Tianyi Zhou
Memorization-to-Generalization in Foundation Model Pretraining: Through the Lens of Pathway Optimization
Rethinking AI Agents: Human-Centered Reinforcement Learning
July 10, 2025
Stephanie Milani
Rethinking AI Agents: Human-Centered Reinforcement Learning
Causal Mediation Analysis Integrating Exposure, Genomic, and Phenotype Data via Tail Likelihood Ratio Method in Epigenome-Wide Studies
July 9, 2025
Haoyu Yang
Causal Mediation Analysis Integrating Exposure, Genomic, and Phenotype Data via Tail Likelihood Ratio Method in Epigenome-Wide Studies
Multilinguality in LLMs with an Eye on Semitic Languages
June 12, 2025
Reut Tsarfaty
Multilinguality in LLMs with an Eye on Semitic Languages
Enhanced localized conformal prediction with imperfect auxiliary information
June 2, 2025
Liuhua Peng
Enhanced localized conformal prediction with imperfect auxiliary information
From Argument Generation to Explainable AI: My Research in Natural Language Processing
May 26, 2025
Milad Alshomary
From Argument Generation to Explainable AI: My Research in Natural Language Processing
Bidirectional Human-AI Alignment: A User-Centered Approach to Shaping AI Systems in Practice
May 20, 2025
Tiffany Knearem
Bidirectional Human-AI Alignment: A User-Centered Approach to Shaping AI Systems in Practice
“AI For Good” Isn’t Good Enough: A Call for Human-Centered AI
May 15, 2025
James Landay
“AI For Good” Isn’t Good Enough: A Call for Human-Centered AI
Multi-modal data analysis using Graph Deep Learning for applications in healthcare
May 14, 2025
Anees Kazi
Multi-modal data analysis using Graph Deep Learning for applications in healthcare
Neuro-symbolic AI: The Third Wave of AI
May 14, 2025
Houbing Herbert Song
Neuro-symbolic AI: The Third Wave of AI
Connecting dots between different science fields towards better treatments – Breast Cancer Research - from HTA to performance assessment using real world data and genomics
May 13, 2025
Augusto Guerra
Connecting dots between different science fields towards better treatments – Breast Cancer Research - from HTA to performance assessment using real world data and genomics
Explainable Speech and Sign Language Processing using Posterior Features
May 13, 2025
Mathew Magimai Doss
Explainable Speech and Sign Language Processing using Posterior Features
The Future of Human-AI Interaction: Teaching, Talking & Teaming Up
May 12, 2025
Diyi Yang
The Future of Human-AI Interaction: Teaching, Talking & Teaming Up
Deep Learning in the Brazilian Network for Genomic Surveillance of Multidrug-Resistant Bacteria
May 8, 2025
Fabricio A. B. da Silva
Deep Learning in the Brazilian Network for Genomic Surveillance of Multidrug-Resistant Bacteria
Towards Uncertainty-Aware, Multimodal Data-Centric AI Pipelines
May 5, 2025
Laure Berti
Towards Uncertainty-Aware, Multimodal Data-Centric AI Pipelines
Harmonizing, Understanding, and Deploying Responsible AI
May 5, 2025
Junyuan Hong
Harmonizing, Understanding, and Deploying Responsible AI
New advances in the epigenetics of common disease
May 1, 2025
Andrew P. Feinberg
New advances in the epigenetics of common disease
Words Meet World: Grounded Language in Embodied AI
April 30, 2025
Joyce Chai
Words Meet World: Grounded Language in Embodied AI
Object-centric Open-world Visual Understanding
April 30, 2025
Shilong Liu
Object-centric Open-world Visual Understanding
Reverse Bioengineering to recreate multicellular animals in vitro
April 29, 2025
Ken-ichiro Kamei
Reverse Bioengineering to recreate multicellular animals in vitro
Pattern Recognition with Optimum-Path Forests
April 28, 2025
João Paulo Papa
Pattern Recognition with Optimum-Path Forests
Cameras as rays: spatial representations for 2D and 3D understanding with foundation models
April 22, 2025
Deva Ramanan
Cameras as rays: spatial representations for 2D and 3D understanding with foundation models
Towards Robust Self-supervised Representation Learning
April 22, 2025
Prakash Chandra + Rajkumar Saini
Towards Robust Self-supervised Representation Learning
Scalable and Efficient Semantic Search in Videos
April 21, 2025
Mattia Soldan
Scalable and Efficient Semantic Search in Videos
Harnessing Causal Discovery for Robust and Adaptive Natural Language Processing
April 18, 2025
Lizhen Qu
Harnessing Causal Discovery for Robust and Adaptive Natural Language Processing
Building Trustworthy Text-to-Image Models: Risks, Defenses, and Forensics
April 16, 2025
Zhang Jie
Building Trustworthy Text-to-Image Models: Risks, Defenses, and Forensics
Operationalizing Fairness in an Interconnected World
April 16, 2025
Jian Kang
Operationalizing Fairness in an Interconnected World
Watch, Predict, Act: Robot Learning Meets Web Videos
April 16, 2025
Homanga Bharadwaj
Watch, Predict, Act: Robot Learning Meets Web Videos
From Intelligence to Artificial Intelligence: Exploring the Future of Humanity
April 15, 2025
Amin Beheshti
From Intelligence to Artificial Intelligence: Exploring the Future of Humanity
A3C3 – AI Algorithm & Accelerator Co-design, Co-search, and Co-generation
April 15, 2025
Deming Chen
A3C3 – AI Algorithm & Accelerator Co-design, Co-search, and Co-generation
Building Equitable Technology Futures: A Relational Access Approach
Hosted by: Prof. Elizabeth Churchill
April 14, 2025
Vaishnav Kameswaran
Building Equitable Technology Futures: A Relational Access Approach
Hosted by: Prof. Elizabeth Churchill
Human-Computer Interaction
Watch Now
Abstract
Building Equitable Technology Futures: A Relational Access Approach
A grand challenge in HCI is understanding how technology-mediated access can enable fuller participation of people with disabilities in society. However, access, framed solely as a feature of technology, can overlook how communities of people with disabilities actively create, share, and sustain access in their everyday lives. In this talk, I show how drawing from disability justice scholarship can broaden the concept of access and open up novel avenues for design. I will share examples from my work where I reconceptualize access as a relational, socio-technical construct-- one shaped by social and material conditions, as well as community values. I will show how this perspective also expands the design space for emerging technologies like AI, shifting their roles from simply mitigating impairments to augmenting human abilities. By reframing technology-mediated access as a socio-technical and relational concept, my work offers new pathways toward more equitable technological futures in HCI.
A grand challenge in HCI is understanding how technology-mediated access can enable fuller participation of people with disabilities in society. However, access, framed solely as a feature of technology, can overlook how communities of people with disabilities actively create, share, and sustain access in their everyday lives. In this talk, I show how drawing from disability justice scholarship can broaden the concept of access and open up novel avenues for design. I will share examples from my work where I reconceptualize access as a relational, socio-technical construct-- one shaped by social and material conditions, as well as community values. I will show how this perspective also expands the design space for emerging technologies like AI, shifting their roles from simply mitigating impairments to augmenting human abilities. By reframing technology-mediated access as a socio-technical and relational concept, my work offers new pathways toward more equitable technological futures in HCI.
Controlled Natural Language Generation for Morphologically Rich Languages: The Case of Arabic
April 14, 2025
Bashar Alhafni
Controlled Natural Language Generation for Morphologically Rich Languages: The Case of Arabic
Next-generation Photorealistic Rendering
April 14, 2025
Lingqi Yan
Next-generation Photorealistic Rendering
Digital Twin of a living Cell using Physics based Artificial Intelligence
April 11, 2025
Dilip K. Prasad
Digital Twin of a living Cell using Physics based Artificial Intelligence
Don't underestimate the power of small language models
April 10, 2025
Tanmoy Chakraborty
Don't underestimate the power of small language models
Unpacking Reasoning in LLMs: Input Formats, Generating CoTs, and Fair Evaluation
April 8, 2025
Haritz Puerto
Unpacking Reasoning in LLMs: Input Formats, Generating CoTs, and Fair Evaluation
Artificial Intelligence in Drug Discovery and Computational Biology: Current Status, Successes, and Pitfalls
April 8, 2025
Andreas Bender
Artificial Intelligence in Drug Discovery and Computational Biology: Current Status, Successes, and Pitfalls
Navigating Uncertainty in Commonsense Causal Reasoning
April 8, 2025
Shaobo Cui
Navigating Uncertainty in Commonsense Causal Reasoning
Stochastic First-Order Optimization with Gradient Clipping
April 7, 2025
Eduard Gorbunov
Stochastic First-Order Optimization with Gradient Clipping
The Role of Human-Computer Interaction Perspectives in Advancing AI-Driven Next-Generation Spatial User Interfaces
April 7, 2025
Johannes Schoning
The Role of Human-Computer Interaction Perspectives in Advancing AI-Driven Next-Generation Spatial User Interfaces
Failing Forward: Rethinking the Foundations of Medical Imaging AI
April 3, 2025
Lena Maier-Hein
Failing Forward: Rethinking the Foundations of Medical Imaging AI
The Politics of Using AI in Policy Implementation: Evidence from a Field Experiment
March 24, 2025
Yotam Margalit
The Politics of Using AI in Policy Implementation: Evidence from a Field Experiment
Automated Reasoning over Strings and Sequences
March 24, 2025
Anthony Lin
Automated Reasoning over Strings and Sequences
Uncertainty Quantification for Scientific Machine Learning
March 24, 2025
Dongxia Wu
Uncertainty Quantification for Scientific Machine Learning
Towards Enhanced Linguistic Reasoning in Language Models
March 20, 2025
Bhat Suma Pallathadka
Towards Enhanced Linguistic Reasoning in Language Models
Enhancing Computational Precision Medicine with Electronic Health Records
March 20, 2025
Jun Wen
Enhancing Computational Precision Medicine with Electronic Health Records
AI-Assisted Experimentation: Challenges, Advances, and Future Directions
March 20, 2025
Raul Astudillo
AI-Assisted Experimentation: Challenges, Advances, and Future Directions
Moving GPU Systems from “Real-Fast” to “Real-Time”
March 19, 2025
Joshua Bakita
Moving GPU Systems from “Real-Fast” to “Real-Time”
Evaluating Long-Context Language Models
March 17, 2025
Marzena Karpinska
Evaluating Long-Context Language Models
Mechanism Design for Decentralized Systems
March 17, 2025
Hao Chung
Mechanism Design for Decentralized Systems
Towards Strategic Alignment in AI: Foundations, Progress and Outlook
March 13, 2025
Jibang Wu
Towards Strategic Alignment in AI: Foundations, Progress and Outlook
Thermal Imaging For Amplifying Human Perception
March 12, 2025
Yomna Abdelrahman
Thermal Imaging For Amplifying Human Perception
Algorithms in the AI Age: Fair and Learning-Augmented
March 11, 2025
Ali Valikian
Algorithms in the AI Age: Fair and Learning-Augmented
AI Advance Pathway: From Targeted Evaluation to Holistic Intelligence
March 10, 2025
Haonan Li
AI Advance Pathway: From Targeted Evaluation to Holistic Intelligence
Fear of Small Data: AI’s Blind Spot in Ethics, Lifecycle Assessment, and Policy
March 10, 2025
Ishtiaque Ahmed
Fear of Small Data: AI’s Blind Spot in Ethics, Lifecycle Assessment, and Policy
Advancing Medical AI: Robust, Interpretable, and Collaborative Solutions
March 10, 2025
Gustavo Carneiro
Advancing Medical AI: Robust, Interpretable, and Collaborative Solutions
Next-Word Prediction in Language Models and Humans
March 4, 2025
Tatsuki Kuribayashi
Next-Word Prediction in Language Models and Humans
Speech Enhancement & Video Summarization - Technology Transfer of Academic Research
March 4, 2025
Shmuel Peleg
Speech Enhancement & Video Summarization - Technology Transfer of Academic Research
Causal Neuro-Symbolic AI: synergy between neuro-symbolic and causal AI
March 3, 2025
Utkarshani Jaimini
Causal Neuro-Symbolic AI: synergy between neuro-symbolic and causal AI
Automated Program Repair for Security
March 3, 2025
Yannic Noller
Automated Program Repair for Security
Formal Methods for Modern Payment Protocols
February 24, 2025
David Basin
Formal Methods for Modern Payment Protocols
LLMs (for code) sometimes make mistakes. When should I trust them?
February 21, 2025
Prem Devanbu
LLMs (for code) sometimes make mistakes. When should I trust them?
Applying Machine Learning and GenAI to the design and operation of climate-resilient residential infrastructure
February 21, 2025
James Ehrlich
Applying Machine Learning and GenAI to the design and operation of climate-resilient residential infrastructure
Sequential Quantile Estimation for Distributed and Streaming Data
February 20, 2025
Nan Lin
Sequential Quantile Estimation for Distributed and Streaming Data
Multimodal Information Extraction from Unstructured Documents
February 19, 2025
Gülşen Eryiğit
Multimodal Information Extraction from Unstructured Documents
Towards safe, factual, and empathetic human-AI interaction
February 19, 2025
Yuxia Wang
Towards safe, factual, and empathetic human-AI interaction
Balancing Explore-exploit, or Purely Exploring
February 18, 2025
Junpei Komiyama
Balancing Explore-exploit, or Purely Exploring
PEaRCE: A Platform for Ethical and Responsible Computing Education in CS Courses
February 17, 2025
Peter Haas
PEaRCE: A Platform for Ethical and Responsible Computing Education in CS Courses
Towards Usable and Useful Explainable AI
February 11, 2025
Lijie Hu
Towards Usable and Useful Explainable AI
Open Science: A New Paradigm for the Research Lifecycle and the Role of Computing
February 6, 2025
Yannis Ioannidis
Open Science: A New Paradigm for the Research Lifecycle and the Role of Computing
Towards Responsible Visual Analytics: Fostering Inclusivity, Accessibility and Trustworthiness in the AI Era
February 5, 2025
Ali Sarvghad
Towards Responsible Visual Analytics: Fostering Inclusivity, Accessibility and Trustworthiness in the AI Era
Polygenic Score Modeling to Investigate Genotype-Phenotype Associations
February 5, 2025
Carlo Maj
Polygenic Score Modeling to Investigate Genotype-Phenotype Associations
Community-Centered Computing for Collective Action and Societal Impact
February 4, 2025
Narges Mahyar
Community-Centered Computing for Collective Action and Societal Impact
Trustworthy Machine Learning: Transparency, Collaboration, and Evaluation
February 4, 2025
Umang Bhatt
Trustworthy Machine Learning: Transparency, Collaboration, and Evaluation
Deep generative modeling of sample-level heterogeneity in single-cell genomics
February 3, 2025
Justin Hong
Deep generative modeling of sample-level heterogeneity in single-cell genomics
AI-enhanced Personalized Medicine and Therapeutic Development
January 29, 2025
Fatemeh Vafaee
AI-enhanced Personalized Medicine and Therapeutic Development
The Econometrics of Unobservables: Identification, Estimation, and Empirical Applications
January 27, 2025
Yingyao Hu
The Econometrics of Unobservables: Identification, Estimation, and Empirical Applications
Cell Biology of Developmental Processes: Imaging Across Scales
January 23, 2025
Senthil Arumugam
Cell Biology of Developmental Processes: Imaging Across Scales
Optimizing 3D Flash-Based SSDs through Device-Aware Techniques
January 23, 2025
Jihong Kim
Optimizing 3D Flash-Based SSDs through Device-Aware Techniques
How to Boot Up a New Engineering Program
January 22, 2025
Seth Fraden
How to Boot Up a New Engineering Program
Human-Computer Conversational Vision-and-Language Navigation
January 21, 2025
Qi Wu
Human-Computer Conversational Vision-and-Language Navigation
From Individual to Society: Social Simulation Driven by LLM-based Agent
January 20, 2025
Zhongyu Wei
From Individual to Society: Social Simulation Driven by LLM-based Agent
AI-based Whole-cycle Health Care Management: Problems, Challenges, and Opportunities
January 17, 2025
Jingshan Li
AI-based Whole-cycle Health Care Management: Problems, Challenges, and Opportunities
Memory representation and retrieval in neuroscience and AI
January 15, 2025
Surya Narayanan Hari
Memory representation and retrieval in neuroscience and AI
Complex disease modeling and efficient drug discovery with large language models
January 14, 2025
Yu Li
Complex disease modeling and efficient drug discovery with large language models
Efficiently Approximating Equivariance in Unconstrained Models
January 13, 2025
Ahmed Elhag
Efficiently Approximating Equivariance in Unconstrained Models
Bring an order to the chaos: Order-Preserving IO stack for Modern Flash storage
January 13, 2025
Youjip Won
Bring an order to the chaos: Order-Preserving IO stack for Modern Flash storage
Communication in the Age of AI: AI for Communication and Communication for AI
December 9, 2024
Joonhyuk Kang
Communication in the Age of AI: AI for Communication and Communication for AI
Reliability Exploration of Neural Network Accelerator
December 5, 2024
Masanori Hashimomo
Reliability Exploration of Neural Network Accelerator
Chip Design and Manufacturing with AI
December 5, 2024
Youngsoo Shin
Chip Design and Manufacturing with AI
Golden Noise and Ziazag Sampling of Diffusion Models
December 4, 2024
Zeke Xie
Golden Noise and Ziazag Sampling of Diffusion Models
Many-cell sequencing: machine learning principles and methods for moving beyond single cells to population-scale analysis
November 26, 2024
David Brown
Many-cell sequencing: machine learning principles and methods for moving beyond single cells to population-scale analysis
Security-Enhanced Radio Access Networks for 5G OpenRAN
November 21, 2024
Zhiqiang Lin
Security-Enhanced Radio Access Networks for 5G OpenRAN
Energy-Efficient and Secure EdgeAI Systems: From Architectures to Applications
November 20, 2024
Muhammad Shafique
Energy-Efficient and Secure EdgeAI Systems: From Architectures to Applications
Generative Artificial Intelligence in RNA Biology
November 19, 2024
Alexandre Paschoal
Generative Artificial Intelligence in RNA Biology
Multimodality for story-level understanding and generation of visual data
November 13, 2024
Vicky Kalogeiton
Multimodality for story-level understanding and generation of visual data
Image- and AI-guided robotics for minimally invasive surgery
November 12, 2024
Momen Abayazid
Image- and AI-guided robotics for minimally invasive surgery
From cloud computing to cloudless computing
November 11, 2024
Ang Chen
From cloud computing to cloudless computing
Physics-Based Deep Learning for Medical Imaging
November 4, 2024
Pascal Fua
Physics-Based Deep Learning for Medical Imaging
To Make Just-Noticeable Difference (JND) Computable toward Visual Intelligence
October 31, 2024
Weisi Lin
To Make Just-Noticeable Difference (JND) Computable toward Visual Intelligence
The chameleon effect in education with social AI: can children learn by subconsciously mimicking a social robot?
October 31, 2024
Maha Elgarf
The chameleon effect in education with social AI: can children learn by subconsciously mimicking a social robot?
Integrating Micro-Emotion Recognition with Mental Health Estimation for Improved Well-being
October 25, 2024
Santosh Kumar Vipparthi
Integrating Micro-Emotion Recognition with Mental Health Estimation for Improved Well-being
Amplifying the Invisible: The Impact of Video Motion Magnification in Healthcare, Engineering, and Beyond
October 25, 2024
Subrahmanyam Murala
Amplifying the Invisible: The Impact of Video Motion Magnification in Healthcare, Engineering, and Beyond
Social Media Influencers, Misinformation, and the threat to elections
October 23, 2024
Joyojeet Pal
Social Media Influencers, Misinformation, and the threat to elections
Unlocking the Potential of Large Models for Vision Related Tasks
October 16, 2024
Yanwei Fu
Unlocking the Potential of Large Models for Vision Related Tasks
Spatial AI to help humans and enable robots
October 15, 2024
Marc Pollefeys
Spatial AI to help humans and enable robots
Embodied Robot Skills and Good Old Fashioned Engineering
September 30, 2024
Michael Yu Wang
Embodied Robot Skills and Good Old Fashioned Engineering
Confidence sets for Causal Discovery
September 25, 2024
Mladen Kolar
Confidence sets for Causal Discovery
AI, Robotics, and the Living: A Research Journey and Future Perspectives
September 17, 2024
Cesare Stefanini
AI, Robotics, and the Living: A Research Journey and Future Perspectives
Human-Centric Approaches for Multimodal Deepfakes Analysis
September 13, 2024
Abhinav Dhall
Human-Centric Approaches for Multimodal Deepfakes Analysis
Towards Controllable Swarms: Integrating Artificial Intelligence at Microscopic and Macroscopic Scales
September 11, 2024
Eliseo Ferrante
Towards Controllable Swarms: Integrating Artificial Intelligence at Microscopic and Macroscopic Scales
Humanizing Technology with Assistive Augmentations
September 3, 2024
Suranga Nanayakkara
Humanizing Technology with Assistive Augmentations
Bring Your Own Kernel! Constructing High-Performance Data Management Systems from Components
September 2, 2024
Holger Pirk
Bring Your Own Kernel! Constructing High-Performance Data Management Systems from Components
Unlocking Decentralized AI and Vision: Overcoming Incentive Barriers, Orchestration Challenges, and Data Silos
August 26, 2024
Ramesh Raskar
Unlocking Decentralized AI and Vision: Overcoming Incentive Barriers, Orchestration Challenges, and Data Silos
Integrating Virtual Reality and Robotics: Enhancing Human and Robot Experiences in Assistive Technologies
August 22, 2024
Tetsunari Inamura
Integrating Virtual Reality and Robotics: Enhancing Human and Robot Experiences in Assistive Technologies
Latent Space Exploration for Safe and Trustworthy AI Models
August 21, 2024
Hassan Sajjad
Latent Space Exploration for Safe and Trustworthy AI Models
Super-aligned Machine Intelligence via a Soft Touch
August 21, 2024
Chaoyang Song
Super-aligned Machine Intelligence via a Soft Touch
Automated Decision Making for Safety Critical Applications
July 22, 2024
Mykel Kochenderfer
Automated Decision Making for Safety Critical Applications
Structured World Models for Robots
June 7, 2024
Krishnan Murthy Jatavallabhula
Structured World Models for Robots
Past, Present and Future of Speech Technologies
May 28, 2024
Pedro Moreno
Past, Present and Future of Speech Technologies
Past, Present and Future of Speech Technologies
May 28, 2024
Pedro Moreno
Past, Present and Future of Speech Technologies
Enabling precision medicine with single cell omics and decentralized clinical studies
May 23, 2024
Eduardo da Veiga Beltrame
Enabling precision medicine with single cell omics and decentralized clinical studies
Martingale-based Verification of Probabilistic Programs
May 21, 2024
Amir Goharshady
Martingale-based Verification of Probabilistic Programs
Recent Advance of Two-sample Testing and Its Application in AI Security
May 16, 2024
Feng Liu
Recent Advance of Two-sample Testing and Its Application in AI Security
Understanding Machine Learning on Graphs: From Node Classification to Algorithmic Reasoning
May 14, 2024
Kimon Fountoulakis
Understanding Machine Learning on Graphs: From Node Classification to Algorithmic Reasoning
Hardware Security through the Lens of Dr ML
May 10, 2024
Debdeep Mukhopadhyay
Hardware Security through the Lens of Dr ML
Safety of Deploying NLP Models: Uncertainty Quantification of Generative LLMs
May 6, 2024
Artem Shelmanov
Safety of Deploying NLP Models: Uncertainty Quantification of Generative LLMs
Objective-Driven AI: Towards Machines that can Learn, Reason, and Plan
February 16, 2024
Yann LeCun
Objective-Driven AI: Towards Machines that can Learn, Reason, and Plan