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Alumni Insights: Shaping a Responsible Future with AI for Good

12/06/2025
18:30 - 20.45

How can we ensure that artificial intelligence doesn’t just disrupt the world, but improves it?

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May
23
Michael Huth (PhD)

AI: Ethics & Diversity Seminar Series with Professor Michael Huth

23/05/2024
14.00 - 15.00

Convergence science refers to transdisciplinary integrations of scientific disciplines in addressing the world’s pressing problems, in advancing science, and in rethinking how we live, work, and innovate. Diversity – of perspective, education, culture, aspiration, talent, and more – and advances in AI are key enablers of convergence science.

May
14
Dr Gege Wen

AI: Cutting-Edge Overviews & Tutorials with Dr Gege Wen

14/05/2024
14.00 - 15.00

CO2 geological storage plays an essential role in global decarbonization and the energy transition. Predicting the transport of CO2 in subsurface formations requires the numerical simulation of multiphase flow through porous media.

May
09
Dr Mary Meaney Haynes

AI: Ethics & Diversity Seminar Series with Dr Mary Meaney Haynes

09/05/2024
13.00 - 14.00

From running mobile clinics with semi-nomadic polygamous tribes to running McKinsey’s global Organization practice, Dr Mary Meaney Haynes will share some perspectives on a career in consulting, serving on Boards of Fortune500’s and Imperial College Council and her recent work supporting Ukrainian refugees.

May
07
Jiameng Liu

AI: Cutting-Edge Overviews & Tutorials with Jiameng Liu

07/05/2024
14.00 - 15.00

Graph Neural Networks (GNNs) are the most widely used techniques for handling unstructured data and have demonstrated success in various applications, such as drug discovery and recommendation systems.

Feb
22
Professor Hamed Haddadi

I-X Research Presentations: Hamed Haddadi

22/02/2024
15.30 - 16.30

In this talk, Hamed will introduce the NetSys group’s research. They work on a broad range of research topics including User-Centred Systems, IoT, Applied Machine Learning, Privacy, and Human-Data Interaction.

Feb
08
Francesco Di Giovanni

I-X Seminar Series: Understanding message passing: limitations of the paradigm and new possibilities with Francesco Di Giovanni

08/02/2024
13.00 - 14.00

Message passing (MP) stands as a cornerstone in Geometric Deep Learning, driving the success of Graph Neural Networks (GNNs) in analysing both graphs and point clouds, and leading to empirical achievements in many scientific domains. Despite its widespread application, the theoretical underpinnings of MP’s successes and limitations remain underexplored.