ΑΙhub.org
 

CLAIRE COVID-19 Initiative Video Series: Meet the Team Leaders – Davide Bacciu

Davide Bacciu

In the second interview in this series of Meet the Team Leaders from the CLAIRE COVID-19 Initiative, we hear from Davide Bacciu (Università di Pisa).

AIhub focus issue on good health and well-being

Davide Bacciu is the leader of the Bioinformatics (protein and molecular data analysis) topic group in the CLAIRE Covid-19 Initiative. In this interview you can find out what his team does, how they use AI in their work, and how he has found the experience of working as part of the taskforce.

To find out more about this series read our recent post, and watch the first video with Emanuela Girardi, here.

You will be able to watch the whole series on the CLAIRE YouTube channel. New videos will be posted over the coming days.

Find out more about the CLAIRE COVID-19 task force here.
You can contact the taskforce by email here.



tags: , ,


CLAIRE (Confederation of Laboratories for AI Research in Europe)

            AIhub is supported by:



Subscribe to AIhub newsletter on substack



Related posts :

‘Probably’ doesn’t mean the same thing to your AI as it does to you

  17 Apr 2026
Are you sure you and the AI chatbot you’re using are on the same page about probabilities?

Interview with Xinwei Song: strategic interactions in networked multi-agent systems

  16 Apr 2026
Xinwei Song tells us about her research using algorithmic game theory and multi-agent reinforcement learning.

2026 AI Index Report released

  15 Apr 2026
Find out what the ninth edition of the report, which was published on 13 April, says about trends in AI.

Formal verification for safety evaluation of autonomous vehicles: an interview with Abdelrahman Sayed Sayed

  14 Apr 2026
Find out more about work at the intersection of continuous AI models, formal methods, and autonomous systems.

Water flow in prairie watersheds is increasingly unpredictable — but AI could help

  13 Apr 2026
In recent years, the Prairies have seen bigger swings in climate conditions — very wet years followed by very dry ones.

Identifying interactions at scale for LLMs

  10 Apr 2026
Model behavior is rarely the result of isolated components; rather, it emerges from complex dependencies and patterns.

Interview with Sukanya Mandal: Synthesizing multi-modal knowledge graphs for smart city intelligence

  09 Apr 2026
A modular four-stage framework that draws on LLMs to automate synthetic multi-modal knowledge graphs.

Emergence of fragility in LLM-based social networks: an interview with Francesco Bertolotti

  08 Apr 2026
Francesco tells us how LLMs behave in the social network Moltbook, and what this reveals about network dynamics.



AIhub is supported by:







Subscribe to AIhub newsletter on substack




 















©2026.02 - Association for the Understanding of Artificial Intelligence