University news

Connecting you to the latest AI research and innovation

Stay up-to-date with the latest news from MBZUAI and the wider industry, featuring insights into ongoing University research, innovations, and developments shaping the future of artificial intelligence. The Node gathers expert commentary, press announcements, and initiatives from the University and its partners, keeping you informed on the most relevant trends and breakthroughs.

Multimodal single-cell atlas for ancestry-based diversity of immune system

Thursday, 25 January, 2024

Multimodal single-cell atlas for ancestry-based diversity of immune system

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People are geneticaly diverse, and this diversity influences many aspects of human bodies as well as disease mechanisms, diagnoses, and treatments. Present-day technologies allows profiling molecular mechanisms of a single […]

Data diagnostics: AI and statistics in computational biology and smart health

Wednesday, 24 January, 2024

Data diagnostics: AI and statistics in computational biology and smart health

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Global experts recently met at MBZUAI to discuss how statistics and AI are set to bring about major innovations in the fields of computational biology and smart health.

Foundations of Multisensory Artificial Intelligence

Wednesday, 24 January, 2024

Foundations of Multisensory Artificial Intelligence

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Building multisensory AI systems that can learn from many sensory inputs such as text, speech, audio, video, real-world sensors, wearable devices, and medical data holds great promise for impact in […]

Building Planetary-Scale Collaborative Intelligence

Wednesday, 24 January, 2024

Building Planetary-Scale Collaborative Intelligence

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Pretrained models and cheap compute have made machine learning (ML) easier to deploy than ever, with the key bottleneck now being high-quality, relevant data. The data that is most valuable […]

Microsoft scientist brings tech acumen and a focus on more inclusive LLMs to MBZUAI

Tuesday, 23 January, 2024

Microsoft scientist brings tech acumen and a focus on more inclusive LLMs to MBZUAI

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Newly appointed MBZUAI professor Monojit Choudhury is bringing experience he gained developing natural language processing applications at one of the world’s great tech giants to the halls of the university.

Continuously Streaming Artificial Intelligence

Wednesday, 17 January, 2024

Continuously Streaming Artificial Intelligence

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The talk will address one of the most challenging adversities faced by artificial intelligence systems, represented by forgetting previously learnt information when the AI system is trained on a new […]

Actionable and responsible AI in Medicine: a geometric deep learning approach

Tuesday, 16 January, 2024

Actionable and responsible AI in Medicine: a geometric deep learning approach

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In this talk I will introduce geometric deep learning techniques. I will focus on how to integrate Computational Biology and Deep Learning to build a digital patient twin using graph […]

AI-Enabled Technologies for People with Disabilities: Some Key Research and Privacy/Security Challenges

Thursday, 11 January, 2024

AI-Enabled Technologies for People with Disabilities: Some Key Research and Privacy/Security Challenges

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The World Health Organization (WHO) defines disability as the interaction between individuals with a health condition (physical or mental) and personal and environmental factors. Often, People with Disabilities (PWD) have […]

SGD from the Lens of Markov process: An Algorithmic Stability Perspective

Wednesday, 10 January, 2024

SGD from the Lens of Markov process: An Algorithmic Stability Perspective

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In this talk, we delve into the intricate relationships between heavy-tailed distributions, generalization error, and algorithmic stability in the realm of noisy stochastic gradient descent. Recent research has illustrated the […]

Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point Processes

Wednesday, 10 January, 2024

Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point Processes

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Marked Temporal Point Processes (TPPs) are a valuable tool for modeling continuous-time event sequences and predicting the arrival time and type of future events. Neural TPPs leverage the expressive power […]

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