Difference: InterOpNov2025DCPKD (1 vs. 17)

Revision 172025-11-15 - YihanTao

 
META TOPICPARENT name="InterOpNov2025"

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DCP/KD Session: November 2025

* back to main programme page *

Schedule (IG: IvoaDCP and KD)

Saturday November 15 @14:00 UTC+1: Room Wichernhaus "zenith"
Speaker Title Time Abstract Material
Gilles Landais Trustworthy AI (and the Eu AI Act) 10+5 Quick report on the European Articial Interlligence act. pdf
Thomas Aynaud (remote) Software Heritage, project presentation and applications to AI 20+5 This talk will provide an overview of what Software Heritage is. We will highlight the data we collect and how we do that. It includes the source code and all the events that can be observed during the development process. We will then explore how to utilize and enrich this information to develop services relevant to AI and the actions we take to create a more open, responsible, and transparent AI. pdf
Changed:
<
<
Thomas Boch First feedback on implementing MCP server to access CDS services 15+5 MCP (Model Context Protocol) is a standard aiming at connecting AI tools to external data sources. We will present the MCP server we developed at CDS, and demonstrate how it allows AI applications to access and consume data from Sesame, MocServer, HiPS2FITS, etc. pdf
>
>
Thomas Boch First feedback on implementing MCP server to access CDS services 15+5 MCP (Model Context Protocol) is a standard aiming at connecting AI tools to external data sources. We will present the MCP server we developed at CDS, and demonstrate how it allows AI applications to access and consume data from Sesame, MocServer, HiPS2FITS, etc. pdf
 
François Lanusse (remote) Make astronomical data AI-ready across surveys: Lessons-learned from the Multimodal Universe Project 15+5 Deep Learning has seen a recent shift in paradigm, from training specialized models on dedicated datasets, to so-called Foundation Models, trained in a self-supervised manner on vast amounts of data and then adapted to solve specific tasks with state-of-the-art performance. This new paradigm has been exceptionally successful not only for large language models (LLMs) but in other domains such as vision models. However applications of this new approach in astrophysics are still very scarce, for reasons ranging from new architectures to the (surprising) lack of availability of suitable large scale datasets.

In this talk, I will discuss our work on building the Multimodal Universe Dataset, the first attempt at building a framework for homogenizing data across many astronomical surveys specifically for AI training purposes. Besides discussing the technical solution we adopted, I will highlight some of the outstanding challenges to enable cross-matching across surveys and streaming data in a way that is compatible with large AI model training. Project page: https://github.com/MultimodalUniverse/MultimodalUniverse

pdf
All Discussions 10    
DCP session Notes : notes.md

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Revision 162025-11-15 - GillesLandais

 
META TOPICPARENT name="InterOpNov2025"

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DCP/KD Session: November 2025

* back to main programme page *

Schedule (IG: IvoaDCP and KD)

Saturday November 15 @14:00 UTC+1: Room Wichernhaus "zenith"
Speaker Title Time Abstract Material
Gilles Landais Trustworthy AI (and the Eu AI Act) 10+5 Quick report on the European Articial Interlligence act. pdf
Thomas Aynaud (remote) Software Heritage, project presentation and applications to AI 20+5 This talk will provide an overview of what Software Heritage is. We will highlight the data we collect and how we do that. It includes the source code and all the events that can be observed during the development process. We will then explore how to utilize and enrich this information to develop services relevant to AI and the actions we take to create a more open, responsible, and transparent AI. pdf
Thomas Boch First feedback on implementing MCP server to access CDS services 15+5 MCP (Model Context Protocol) is a standard aiming at connecting AI tools to external data sources. We will present the MCP server we developed at CDS, and demonstrate how it allows AI applications to access and consume data from Sesame, MocServer, HiPS2FITS, etc. pdf
François Lanusse (remote) Make astronomical data AI-ready across surveys: Lessons-learned from the Multimodal Universe Project 15+5 Deep Learning has seen a recent shift in paradigm, from training specialized models on dedicated datasets, to so-called Foundation Models, trained in a self-supervised manner on vast amounts of data and then adapted to solve specific tasks with state-of-the-art performance. This new paradigm has been exceptionally successful not only for large language models (LLMs) but in other domains such as vision models. However applications of this new approach in astrophysics are still very scarce, for reasons ranging from new architectures to the (surprising) lack of availability of suitable large scale datasets.

In this talk, I will discuss our work on building the Multimodal Universe Dataset, the first attempt at building a framework for homogenizing data across many astronomical surveys specifically for AI training purposes. Besides discussing the technical solution we adopted, I will highlight some of the outstanding challenges to enable cross-matching across surveys and streaming data in a way that is compatible with large AI model training. Project page: https://github.com/MultimodalUniverse/MultimodalUniverse

pdf
All Discussions 10    
Changed:
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DCP session Notes : todo
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DCP session Notes : notes.md
 
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META FILEATTACHMENT attachment="notes.md" attr="" comment="" date="1763224013" name="notes.md" path="notes.md" size="7292" user="GillesLandais" version="1"
 

Revision 152025-11-15 - GillesLandais

 
META TOPICPARENT name="InterOpNov2025"

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DCP/KD Session: November 2025

* back to main programme page *

Schedule (IG: IvoaDCP and KD)

Saturday November 15 @14:00 UTC+1: Room Wichernhaus "zenith"
Speaker Title Time Abstract Material
Gilles Landais Trustworthy AI (and the Eu AI Act) 10+5 Quick report on the European Articial Interlligence act. pdf
Thomas Aynaud (remote) Software Heritage, project presentation and applications to AI 20+5 This talk will provide an overview of what Software Heritage is. We will highlight the data we collect and how we do that. It includes the source code and all the events that can be observed during the development process. We will then explore how to utilize and enrich this information to develop services relevant to AI and the actions we take to create a more open, responsible, and transparent AI. pdf
Thomas Boch First feedback on implementing MCP server to access CDS services 15+5 MCP (Model Context Protocol) is a standard aiming at connecting AI tools to external data sources. We will present the MCP server we developed at CDS, and demonstrate how it allows AI applications to access and consume data from Sesame, MocServer, HiPS2FITS, etc. pdf
François Lanusse (remote) Make astronomical data AI-ready across surveys: Lessons-learned from the Multimodal Universe Project 15+5 Deep Learning has seen a recent shift in paradigm, from training specialized models on dedicated datasets, to so-called Foundation Models, trained in a self-supervised manner on vast amounts of data and then adapted to solve specific tasks with state-of-the-art performance. This new paradigm has been exceptionally successful not only for large language models (LLMs) but in other domains such as vision models. However applications of this new approach in astrophysics are still very scarce, for reasons ranging from new architectures to the (surprising) lack of availability of suitable large scale datasets.

In this talk, I will discuss our work on building the Multimodal Universe Dataset, the first attempt at building a framework for homogenizing data across many astronomical surveys specifically for AI training purposes. Besides discussing the technical solution we adopted, I will highlight some of the outstanding challenges to enable cross-matching across surveys and streaming data in a way that is compatible with large AI model training. Project page: https://github.com/MultimodalUniverse/MultimodalUniverse

pdf
All Discussions 10    
DCP session Notes : todo

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Changed:
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Revision 142025-11-15 - ThomasBoch

 
META TOPICPARENT name="InterOpNov2025"

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DCP/KD Session: November 2025

* back to main programme page *

Schedule (IG: IvoaDCP and KD)

Saturday November 15 @14:00 UTC+1: Room Wichernhaus "zenith"
Speaker Title Time Abstract Material
Gilles Landais Trustworthy AI (and the Eu AI Act) 10+5 Quick report on the European Articial Interlligence act. pdf
Thomas Aynaud (remote) Software Heritage, project presentation and applications to AI 20+5 This talk will provide an overview of what Software Heritage is. We will highlight the data we collect and how we do that. It includes the source code and all the events that can be observed during the development process. We will then explore how to utilize and enrich this information to develop services relevant to AI and the actions we take to create a more open, responsible, and transparent AI. pdf
Changed:
<
<
Thomas Boch First feedback on implementing MCP server to access CDS services 15+5 MCP (Model Context Protocol) is a standard aiming at connecting AI tools to external data sources. We will present the MCP server we developed at CDS, and demonstrate how it allows AI applications to access and consume data from Sesame, MocServer, HiPS2FITS, etc. pdf
>
>
Thomas Boch First feedback on implementing MCP server to access CDS services 15+5 MCP (Model Context Protocol) is a standard aiming at connecting AI tools to external data sources. We will present the MCP server we developed at CDS, and demonstrate how it allows AI applications to access and consume data from Sesame, MocServer, HiPS2FITS, etc. pdf
 
François Lanusse (remote) Make astronomical data AI-ready across surveys: Lessons-learned from the Multimodal Universe Project 15+5 Deep Learning has seen a recent shift in paradigm, from training specialized models on dedicated datasets, to so-called Foundation Models, trained in a self-supervised manner on vast amounts of data and then adapted to solve specific tasks with state-of-the-art performance. This new paradigm has been exceptionally successful not only for large language models (LLMs) but in other domains such as vision models. However applications of this new approach in astrophysics are still very scarce, for reasons ranging from new architectures to the (surprising) lack of availability of suitable large scale datasets.

In this talk, I will discuss our work on building the Multimodal Universe Dataset, the first attempt at building a framework for homogenizing data across many astronomical surveys specifically for AI training purposes. Besides discussing the technical solution we adopted, I will highlight some of the outstanding challenges to enable cross-matching across surveys and streaming data in a way that is compatible with large AI model training. Project page: https://github.com/MultimodalUniverse/MultimodalUniverse

pdf
All Discussions 10    
DCP session Notes : todo

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Revision 132025-11-15 - YihanTao

 
META TOPICPARENT name="InterOpNov2025"

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DCP/KD Session: November 2025

* back to main programme page *

Schedule (IG: IvoaDCP and KD)

Saturday November 15 @14:00 UTC+1: Room Wichernhaus "zenith"
Speaker Title Time Abstract Material
Gilles Landais Trustworthy AI (and the Eu AI Act) 10+5 Quick report on the European Articial Interlligence act. pdf
Thomas Aynaud (remote) Software Heritage, project presentation and applications to AI 20+5 This talk will provide an overview of what Software Heritage is. We will highlight the data we collect and how we do that. It includes the source code and all the events that can be observed during the development process. We will then explore how to utilize and enrich this information to develop services relevant to AI and the actions we take to create a more open, responsible, and transparent AI. pdf
Changed:
<
<
Thomas Boch First feedback on implementing MCP server to access CDS services 15+5 MCP (Model Context Protocol) is a standard aiming at connecting AI tools to external data sources. We will present the MCP server we developed at CDS, and demonstrate how it allows AI applications to access and consume data from Sesame, MocServer, HiPS2FITS, etc.  
François Lanusse (remote) Make astronomical data AI-ready across surveys: Lessons-learned from the Multimodal Universe Project 15+5 Deep Learning has seen a recent shift in paradigm, from training specialized models on dedicated datasets, to so-called Foundation Models, trained in a self-supervised manner on vast amounts of data and then adapted to solve specific tasks with state-of-the-art performance. This new paradigm has been exceptionally successful not only for large language models (LLMs) but in other domains such as vision models. However applications of this new approach in astrophysics are still very scarce, for reasons ranging from new architectures to the (surprising) lack of availability of suitable large scale datasets.

In this talk, I will discuss our work on building the Multimodal Universe Dataset, the first attempt at building a framework for homogenizing data across many astronomical surveys specifically for AI training purposes. Besides discussing the technical solution we adopted, I will highlight some of the outstanding challenges to enable cross-matching across surveys and streaming data in a way that is compatible with large AI model training. Project page: https://github.com/MultimodalUniverse/MultimodalUniverse

 
>
>
Thomas Boch First feedback on implementing MCP server to access CDS services 15+5 MCP (Model Context Protocol) is a standard aiming at connecting AI tools to external data sources. We will present the MCP server we developed at CDS, and demonstrate how it allows AI applications to access and consume data from Sesame, MocServer, HiPS2FITS, etc. pdf
François Lanusse (remote) Make astronomical data AI-ready across surveys: Lessons-learned from the Multimodal Universe Project 15+5 Deep Learning has seen a recent shift in paradigm, from training specialized models on dedicated datasets, to so-called Foundation Models, trained in a self-supervised manner on vast amounts of data and then adapted to solve specific tasks with state-of-the-art performance. This new paradigm has been exceptionally successful not only for large language models (LLMs) but in other domains such as vision models. However applications of this new approach in astrophysics are still very scarce, for reasons ranging from new architectures to the (surprising) lack of availability of suitable large scale datasets.

In this talk, I will discuss our work on building the Multimodal Universe Dataset, the first attempt at building a framework for homogenizing data across many astronomical surveys specifically for AI training purposes. Besides discussing the technical solution we adopted, I will highlight some of the outstanding challenges to enable cross-matching across surveys and streaming data in a way that is compatible with large AI model training. Project page: https://github.com/MultimodalUniverse/MultimodalUniverse

pdf
 
All Discussions 10    
DCP session Notes : todo

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Revision 122025-11-15 - ThomasBoch

 
META TOPICPARENT name="InterOpNov2025"

<--  
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DCP/KD Session: November 2025

* back to main programme page *

Schedule (IG: IvoaDCP and KD)

Saturday November 15 @14:00 UTC+1: Room Wichernhaus "zenith"
Speaker Title Time Abstract Material
Gilles Landais Trustworthy AI (and the Eu AI Act) 10+5 Quick report on the European Articial Interlligence act. pdf
Thomas Aynaud (remote) Software Heritage, project presentation and applications to AI 20+5 This talk will provide an overview of what Software Heritage is. We will highlight the data we collect and how we do that. It includes the source code and all the events that can be observed during the development process. We will then explore how to utilize and enrich this information to develop services relevant to AI and the actions we take to create a more open, responsible, and transparent AI. pdf
Thomas Boch First feedback on implementing MCP server to access CDS services 15+5 MCP (Model Context Protocol) is a standard aiming at connecting AI tools to external data sources. We will present the MCP server we developed at CDS, and demonstrate how it allows AI applications to access and consume data from Sesame, MocServer, HiPS2FITS, etc.  
François Lanusse (remote) Make astronomical data AI-ready across surveys: Lessons-learned from the Multimodal Universe Project 15+5 Deep Learning has seen a recent shift in paradigm, from training specialized models on dedicated datasets, to so-called Foundation Models, trained in a self-supervised manner on vast amounts of data and then adapted to solve specific tasks with state-of-the-art performance. This new paradigm has been exceptionally successful not only for large language models (LLMs) but in other domains such as vision models. However applications of this new approach in astrophysics are still very scarce, for reasons ranging from new architectures to the (surprising) lack of availability of suitable large scale datasets.

In this talk, I will discuss our work on building the Multimodal Universe Dataset, the first attempt at building a framework for homogenizing data across many astronomical surveys specifically for AI training purposes. Besides discussing the technical solution we adopted, I will highlight some of the outstanding challenges to enable cross-matching across surveys and streaming data in a way that is compatible with large AI model training. Project page: https://github.com/MultimodalUniverse/MultimodalUniverse

 
All Discussions 10    
DCP session Notes : todo

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Revision 112025-11-15 - GillesLandais

 
META TOPICPARENT name="InterOpNov2025"

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DCP/KD Session: November 2025

* back to main programme page *

Schedule (IG: IvoaDCP and KD)

Saturday November 15 @14:00 UTC+1: Room Wichernhaus "zenith"
Speaker Title Time Abstract Material
Gilles Landais Trustworthy AI (and the Eu AI Act) 10+5 Quick report on the European Articial Interlligence act. pdf
Changed:
<
<
Thomas Aynaud (remote) Software Heritage, project presentation and applications to AI 20+5 This talk will provide an overview of what Software Heritage is. We will highlight the data we collect and how we do that. It includes the source code and all the events that can be observed during the development process. We will then explore how to utilize and enrich this information to develop services relevant to AI and the actions we take to create a more open, responsible, and transparent AI.  
>
>
Thomas Aynaud (remote) Software Heritage, project presentation and applications to AI 20+5 This talk will provide an overview of what Software Heritage is. We will highlight the data we collect and how we do that. It includes the source code and all the events that can be observed during the development process. We will then explore how to utilize and enrich this information to develop services relevant to AI and the actions we take to create a more open, responsible, and transparent AI. pdf
 
Thomas Boch First feedback on implementing MCP server to access CDS services 15+5 MCP (Model Context Protocol) is a standard aiming at connecting AI tools to external data sources. We will present the MCP server we developed at CDS, and demonstrate how it allows AI applications to access and consume data from Sesame, MocServer, HiPS2FITS, etc.  
François Lanusse (remote) Make astronomical data AI-ready across surveys: Lessons-learned from the Multimodal Universe Project 15+5 Deep Learning has seen a recent shift in paradigm, from training specialized models on dedicated datasets, to so-called Foundation Models, trained in a self-supervised manner on vast amounts of data and then adapted to solve specific tasks with state-of-the-art performance. This new paradigm has been exceptionally successful not only for large language models (LLMs) but in other domains such as vision models. However applications of this new approach in astrophysics are still very scarce, for reasons ranging from new architectures to the (surprising) lack of availability of suitable large scale datasets.

In this talk, I will discuss our work on building the Multimodal Universe Dataset, the first attempt at building a framework for homogenizing data across many astronomical surveys specifically for AI training purposes. Besides discussing the technical solution we adopted, I will highlight some of the outstanding challenges to enable cross-matching across surveys and streaming data in a way that is compatible with large AI model training. Project page: https://github.com/MultimodalUniverse/MultimodalUniverse

 
All Discussions 10    
DCP session Notes : todo

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Added:
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META FILEATTACHMENT attachment="Software-Heritage-presentation_ivoa.pdf" attr="" comment="" date="1763197509" name="Software-Heritage-presentation_ivoa.pdf" path="Software-Heritage-presentation ivoa.pdf" size="2066933" user="GillesLandais" version="1"
 

Revision 102025-11-15 - YihanTao

 
META TOPICPARENT name="InterOpNov2025"

<--  
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DCP/KD Session: November 2025

* back to main programme page *

Schedule (IG: IvoaDCP and KD)

Saturday November 15 @14:00 UTC+1: Room Wichernhaus "zenith"
Speaker Title Time Abstract Material
Gilles Landais Trustworthy AI (and the Eu AI Act) 10+5 Quick report on the European Articial Interlligence act. pdf
Thomas Aynaud (remote) Software Heritage, project presentation and applications to AI 20+5 This talk will provide an overview of what Software Heritage is. We will highlight the data we collect and how we do that. It includes the source code and all the events that can be observed during the development process. We will then explore how to utilize and enrich this information to develop services relevant to AI and the actions we take to create a more open, responsible, and transparent AI.  
Thomas Boch First feedback on implementing MCP server to access CDS services 15+5 MCP (Model Context Protocol) is a standard aiming at connecting AI tools to external data sources. We will present the MCP server we developed at CDS, and demonstrate how it allows AI applications to access and consume data from Sesame, MocServer, HiPS2FITS, etc.  
François Lanusse (remote) Make astronomical data AI-ready across surveys: Lessons-learned from the Multimodal Universe Project 15+5 Deep Learning has seen a recent shift in paradigm, from training specialized models on dedicated datasets, to so-called Foundation Models, trained in a self-supervised manner on vast amounts of data and then adapted to solve specific tasks with state-of-the-art performance. This new paradigm has been exceptionally successful not only for large language models (LLMs) but in other domains such as vision models. However applications of this new approach in astrophysics are still very scarce, for reasons ranging from new architectures to the (surprising) lack of availability of suitable large scale datasets.

In this talk, I will discuss our work on building the Multimodal Universe Dataset, the first attempt at building a framework for homogenizing data across many astronomical surveys specifically for AI training purposes. Besides discussing the technical solution we adopted, I will highlight some of the outstanding challenges to enable cross-matching across surveys and streaming data in a way that is compatible with large AI model training. Project page: https://github.com/MultimodalUniverse/MultimodalUniverse

 
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DCP session Notes : todo

META FILEATTACHMENT attachment="trustworthAI.pdf" attr="" comment="" date="1763152473" name="trustworthAI.pdf" path="trustworthAI.pdf" size="489774" user="GillesLandais" version="3"
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META FILEATTACHMENT attachment="introduction-session-DCP-KD.pdf" attr="" comment="" date="1763196325" name="introduction-session-DCP-KD.pdf" path="introduction-session-DCP-KD.pdf" size="252868" user="YihanTao" version="1"
 

Revision 92025-11-14 - GillesLandais

 
META TOPICPARENT name="InterOpNov2025"

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DCP/KD Session: November 2025

* back to main programme page *

Schedule (IG: IvoaDCP and KD)

Saturday November 15 @14:00 UTC+1: Room Wichernhaus "zenith"
Speaker Title Time Abstract Material
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Gilles Landais Trustworthy AI (and the Eu AI Act) 10+5 Quick report on the European Articial Interlligence act.  
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Gilles Landais Trustworthy AI (and the Eu AI Act) 10+5 Quick report on the European Articial Interlligence act. pdf
 
Thomas Aynaud (remote) Software Heritage, project presentation and applications to AI 20+5 This talk will provide an overview of what Software Heritage is. We will highlight the data we collect and how we do that. It includes the source code and all the events that can be observed during the development process. We will then explore how to utilize and enrich this information to develop services relevant to AI and the actions we take to create a more open, responsible, and transparent AI.  
Thomas Boch First feedback on implementing MCP server to access CDS services 15+5 MCP (Model Context Protocol) is a standard aiming at connecting AI tools to external data sources. We will present the MCP server we developed at CDS, and demonstrate how it allows AI applications to access and consume data from Sesame, MocServer, HiPS2FITS, etc.  
François Lanusse (remote) Make astronomical data AI-ready across surveys: Lessons-learned from the Multimodal Universe Project 15+5 Deep Learning has seen a recent shift in paradigm, from training specialized models on dedicated datasets, to so-called Foundation Models, trained in a self-supervised manner on vast amounts of data and then adapted to solve specific tasks with state-of-the-art performance. This new paradigm has been exceptionally successful not only for large language models (LLMs) but in other domains such as vision models. However applications of this new approach in astrophysics are still very scarce, for reasons ranging from new architectures to the (surprising) lack of availability of suitable large scale datasets.

In this talk, I will discuss our work on building the Multimodal Universe Dataset, the first attempt at building a framework for homogenizing data across many astronomical surveys specifically for AI training purposes. Besides discussing the technical solution we adopted, I will highlight some of the outstanding challenges to enable cross-matching across surveys and streaming data in a way that is compatible with large AI model training. Project page: https://github.com/MultimodalUniverse/MultimodalUniverse

 
All Discussions 10    
DCP session Notes : todo
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META FILEATTACHMENT attachment="trustworthAI.pdf" attr="" comment="" date="1763152473" name="trustworthAI.pdf" path="trustworthAI.pdf" size="489774" user="GillesLandais" version="3"

Revision 82025-11-13 - GillesLandais

 
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DCP/KD Session: November 2025

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Schedule (IG: IvoaDCP and KD)

Changed:
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Wednesday November 15 @14:00 UTC+1: Room Wichernhaus "zenith"
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Saturday November 15 @14:00 UTC+1: Room Wichernhaus "zenith"
 
Speaker Title Time Abstract Material
Gilles Landais Trustworthy AI (and the Eu AI Act) 10+5 Quick report on the European Articial Interlligence act.  
Thomas Aynaud (remote) Software Heritage, project presentation and applications to AI 20+5 This talk will provide an overview of what Software Heritage is. We will highlight the data we collect and how we do that. It includes the source code and all the events that can be observed during the development process. We will then explore how to utilize and enrich this information to develop services relevant to AI and the actions we take to create a more open, responsible, and transparent AI.  
Thomas Boch First feedback on implementing MCP server to access CDS services 15+5 MCP (Model Context Protocol) is a standard aiming at connecting AI tools to external data sources. We will present the MCP server we developed at CDS, and demonstrate how it allows AI applications to access and consume data from Sesame, MocServer, HiPS2FITS, etc.  
Changed:
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François Lanusse (remote) Make astronomical data AI-ready across surveys: Lessons-learned from the Multimodal Universe Project 15+5 Deep Learning has seen a recent shift in paradigm, from training specialized models on dedicated datasets, to so-called Foundation Models, trained in a self-supervised manner on vast amounts of data and then adapted to solve specific tasks with state-of-the-art performance. This new paradigm has been exceptionally successful not only for large language models (LLMs) but in other domains such as vision models. However applications of this new approach in astrophysics are still very scarce, for reasons ranging from new architectures to the (surprising) lack of availability of suitable large scale datasets.

In this talk, I will discuss our work on building the Multimodal Universe Dataset, the first attempt at building a framework for homogenizing data across many astronomical surveys specifically for AI training purposes. Besides discussing the technical solution we adopted, I will highlight some of the outstanding challenges to enable cross-matching across surveys and streaming data in a way that is compatible with large AI model training. Project page: https://github.com/MultimodalUniverse/MultimodalUniverse

 
>
>
François Lanusse (remote) Make astronomical data AI-ready across surveys: Lessons-learned from the Multimodal Universe Project 15+5 Deep Learning has seen a recent shift in paradigm, from training specialized models on dedicated datasets, to so-called Foundation Models, trained in a self-supervised manner on vast amounts of data and then adapted to solve specific tasks with state-of-the-art performance. This new paradigm has been exceptionally successful not only for large language models (LLMs) but in other domains such as vision models. However applications of this new approach in astrophysics are still very scarce, for reasons ranging from new architectures to the (surprising) lack of availability of suitable large scale datasets.

In this talk, I will discuss our work on building the Multimodal Universe Dataset, the first attempt at building a framework for homogenizing data across many astronomical surveys specifically for AI training purposes. Besides discussing the technical solution we adopted, I will highlight some of the outstanding challenges to enable cross-matching across surveys and streaming data in a way that is compatible with large AI model training. Project page: https://github.com/MultimodalUniverse/MultimodalUniverse

 
 
All Discussions 10    
DCP session Notes : todo

Revision 72025-11-12 - AndreSchaaff

 
META TOPICPARENT name="InterOpNov2025"

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DCP/KD Session: November 2025

* back to main programme page *

Schedule (IG: IvoaDCP and KD)

Wednesday November 15 @14:00 UTC+1: Room Wichernhaus "zenith"
Speaker Title Time Abstract Material
Gilles Landais Trustworthy AI (and the Eu AI Act) 10+5 Quick report on the European Articial Interlligence act.  
Thomas Aynaud (remote) Software Heritage, project presentation and applications to AI 20+5 This talk will provide an overview of what Software Heritage is. We will highlight the data we collect and how we do that. It includes the source code and all the events that can be observed during the development process. We will then explore how to utilize and enrich this information to develop services relevant to AI and the actions we take to create a more open, responsible, and transparent AI.  
Thomas Boch First feedback on implementing MCP server to access CDS services 15+5 MCP (Model Context Protocol) is a standard aiming at connecting AI tools to external data sources. We will present the MCP server we developed at CDS, and demonstrate how it allows AI applications to access and consume data from Sesame, MocServer, HiPS2FITS, etc.  
François Lanusse (remote) Make astronomical data AI-ready across surveys: Lessons-learned from the Multimodal Universe Project 15+5 Deep Learning has seen a recent shift in paradigm, from training specialized models on dedicated datasets, to so-called Foundation Models, trained in a self-supervised manner on vast amounts of data and then adapted to solve specific tasks with state-of-the-art performance. This new paradigm has been exceptionally successful not only for large language models (LLMs) but in other domains such as vision models. However applications of this new approach in astrophysics are still very scarce, for reasons ranging from new architectures to the (surprising) lack of availability of suitable large scale datasets.

In this talk, I will discuss our work on building the Multimodal Universe Dataset, the first attempt at building a framework for homogenizing data across many astronomical surveys specifically for AI training purposes. Besides discussing the technical solution we adopted, I will highlight some of the outstanding challenges to enable cross-matching across surveys and streaming data in a way that is compatible with large AI model training. Project page: https://github.com/MultimodalUniverse/MultimodalUniverse

 
All Discussions 10    
DCP session Notes : todo
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DCP/KD Session: November 2025

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* back to main programme page *
Deleted:
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Schedule (IG: IvoaDCP and KD)

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Wednesday November 15 @14:00 UTC+1: Room 1309
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Wednesday November 15 @14:00 UTC+1: Room Wichernhaus "zenith"
 
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All Discussions 10    
Thomas Boch First feedback on implementing MCP server to access CDS services 15+5 MCP (Model Context Protocol) is a standard aiming at connecting AI tools to external data sources. We will present the MCP server we developed at CDS, and demonstrate how it allows AI applications to access and consume data from Sesame, MocServer, HiPS2FITS, etc.  
François Lanusse (remote) Make astronomical data AI-ready across surveys: Lessons-learned from the Multimodal Universe Project 15+5 Deep Learning has seen a recent shift in paradigm, from training specialized models on dedicated datasets, to so-called Foundation Models, trained in a self-supervised manner on vast amounts of data and then adapted to solve specific tasks with state-of-the-art performance. This new paradigm has been exceptionally successful not only for large language models (LLMs) but in other domains such as vision models. However applications of this new approach in astrophysics are still very scarce, for reasons ranging from new architectures to the (surprising) lack of availability of suitable large scale datasets.

In this talk, I will discuss our work on building the Multimodal Universe Dataset, the first attempt at building a framework for homogenizing data across many astronomical surveys specifically for AI training purposes. Besides discussing the technical solution we adopted, I will highlight some of the outstanding challenges to enable cross-matching across surveys and streaming data in a way that is compatible with large AI model training. Project page: https://github.com/MultimodalUniverse/MultimodalUniverse

 
Thomas Aynaud (remote) Software Heritage, project presentation and applications to AI 20+5 This talk will provide an overview of what Software Heritage is. We will highlight the data we collect and how we do that. It includes the source code and all the events that can be observed during the development process. We will then explore how to utilize and enrich this information to develop services relevant to AI and the actions we take to create a more open, responsible, and transparent AI.  
Gilles Landais Trustworthy AI (and the Eu AI Act) 10+5 Quick report on the European Articial Interlligence act.  
DCP session Notes : todo

Revision 52025-11-12 - AndreSchaaff

 
META TOPICPARENT name="InterOpNov2025"

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DCP/KD Session: November 2025

[ back to main programme page]

Schedule (IG: IvoaDCP and KD)

Changed:
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<
Wednesday June 4 @09:00 EDT: Room 1309
>
>
Wednesday November 15 @14:00 UTC+1: Room 1309
 
Speaker Title Time Abstract Material
Gilles Landais Trustworthy AI (and the Eu AI Act) 10+5 Quick report on the European Articial Interlligence act.  
Thomas Aynaud (remote) Software Heritage, project presentation and applications to AI 20+5 This talk will provide an overview of what Software Heritage is. We will highlight the data we collect and how we do that. It includes the source code and all the events that can be observed during the development process. We will then explore how to utilize and enrich this information to develop services relevant to AI and the actions we take to create a more open, responsible, and transparent AI.  
Thomas Boch First feedback on implementing MCP server to access CDS services 15+5 MCP (Model Context Protocol) is a standard aiming at connecting AI tools to external data sources. We will present the MCP server we developed at CDS, and demonstrate how it allows AI applications to access and consume data from Sesame, MocServer, HiPS2FITS, etc.  
François Lanusse (remote) Make astronomical data AI-ready across surveys: Lessons-learned from the Multimodal Universe Project 15+5 Deep Learning has seen a recent shift in paradigm, from training specialized models on dedicated datasets, to so-called Foundation Models, trained in a self-supervised manner on vast amounts of data and then adapted to solve specific tasks with state-of-the-art performance. This new paradigm has been exceptionally successful not only for large language models (LLMs) but in other domains such as vision models. However applications of this new approach in astrophysics are still very scarce, for reasons ranging from new architectures to the (surprising) lack of availability of suitable large scale datasets.

In this talk, I will discuss our work on building the Multimodal Universe Dataset, the first attempt at building a framework for homogenizing data across many astronomical surveys specifically for AI training purposes. Besides discussing the technical solution we adopted, I will highlight some of the outstanding challenges to enable cross-matching across surveys and streaming data in a way that is compatible with large AI model training. Project page: https://github.com/MultimodalUniverse/MultimodalUniverse

 
All Discussions 10    
DCP session Notes : todo
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Revision 42025-11-12 - AndreSchaaff

 
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DCP Session: Novemeber 2025

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DCP/KD Session: November 2025

  [ back to main programme page]

Schedule (IG: IvoaDCP and KD)

Wednesday June 4 @09:00 EDT: Room 1309
Speaker Title Time Abstract Material
Gilles Landais Trustworthy AI (and the Eu AI Act) 10+5 Quick report on the European Articial Interlligence act.  
Thomas Aynaud (remote) Software Heritage, project presentation and applications to AI 20+5 This talk will provide an overview of what Software Heritage is. We will highlight the data we collect and how we do that. It includes the source code and all the events that can be observed during the development process. We will then explore how to utilize and enrich this information to develop services relevant to AI and the actions we take to create a more open, responsible, and transparent AI.  
Thomas Boch First feedback on implementing MCP server to access CDS services 15+5 MCP (Model Context Protocol) is a standard aiming at connecting AI tools to external data sources. We will present the MCP server we developed at CDS, and demonstrate how it allows AI applications to access and consume data from Sesame, MocServer, HiPS2FITS, etc.  
François Lanusse (remote) Make astronomical data AI-ready across surveys: Lessons-learned from the Multimodal Universe Project 15+5 Deep Learning has seen a recent shift in paradigm, from training specialized models on dedicated datasets, to so-called Foundation Models, trained in a self-supervised manner on vast amounts of data and then adapted to solve specific tasks with state-of-the-art performance. This new paradigm has been exceptionally successful not only for large language models (LLMs) but in other domains such as vision models. However applications of this new approach in astrophysics are still very scarce, for reasons ranging from new architectures to the (surprising) lack of availability of suitable large scale datasets.

In this talk, I will discuss our work on building the Multimodal Universe Dataset, the first attempt at building a framework for homogenizing data across many astronomical surveys specifically for AI training purposes. Besides discussing the technical solution we adopted, I will highlight some of the outstanding challenges to enable cross-matching across surveys and streaming data in a way that is compatible with large AI model training. Project page: https://github.com/MultimodalUniverse/MultimodalUniverse

 
All Discussions 10    
DCP session Notes : todo

Revision 32025-11-09 - YihanTao

 
META TOPICPARENT name="InterOpNov2025"

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DCP Session: Novemeber 2025

[ back to main programme page]

Schedule (IG: IvoaDCP and KD)

Wednesday June 4 @09:00 EDT: Room 1309
Speaker Title Time Abstract Material
Gilles Landais Trustworthy AI (and the Eu AI Act) 10+5 Quick report on the European Articial Interlligence act.  
Thomas Aynaud (remote) Software Heritage, project presentation and applications to AI 20+5 This talk will provide an overview of what Software Heritage is. We will highlight the data we collect and how we do that. It includes the source code and all the events that can be observed during the development process. We will then explore how to utilize and enrich this information to develop services relevant to AI and the actions we take to create a more open, responsible, and transparent AI.  
Thomas Boch First feedback on implementing MCP server to access CDS services 15+5 MCP (Model Context Protocol) is a standard aiming at connecting AI tools to external data sources. We will present the MCP server we developed at CDS, and demonstrate how it allows AI applications to access and consume data from Sesame, MocServer, HiPS2FITS, etc.  
Changed:
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<
François Lanusse (remote) Multimodal Universe 15+5    
All Discussions 10  
>
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François Lanusse (remote) Make astronomical data AI-ready across surveys: Lessons-learned from the Multimodal Universe Project 15+5 Deep Learning has seen a recent shift in paradigm, from training specialized models on dedicated datasets, to so-called Foundation Models, trained in a self-supervised manner on vast amounts of data and then adapted to solve specific tasks with state-of-the-art performance. This new paradigm has been exceptionally successful not only for large language models (LLMs) but in other domains such as vision models. However applications of this new approach in astrophysics are still very scarce, for reasons ranging from new architectures to the (surprising) lack of availability of suitable large scale datasets.

In this talk, I will discuss our work on building the Multimodal Universe Dataset, the first attempt at building a framework for homogenizing data across many astronomical surveys specifically for AI training purposes. Besides discussing the technical solution we adopted, I will highlight some of the outstanding challenges to enable cross-matching across surveys and streaming data in a way that is compatible with large AI model training. Project page: https://github.com/MultimodalUniverse/MultimodalUniverse

 
All Discussions 10    
 DCP session Notes : todo
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Revision 22025-11-06 - GillesLandais

 
META TOPICPARENT name="InterOpNov2025"

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DCP Session: Novemeber 2025

[ back to main programme page]

Schedule (IG: IvoaDCP and KD)

Wednesday June 4 @09:00 EDT: Room 1309
Speaker Title Time Abstract Material
Changed:
<
<
Gilles Landais European AI Act 10+5 Quick report on the European Articial Interlligence act.  
Thomas Aynaud (remote) Software Heritage 15+5 Thomas Aynaud (CTO of Software Heritage) is working in INRIA, France.  
>
>
Gilles Landais Trustworthy AI (and the Eu AI Act) 10+5 Quick report on the European Articial Interlligence act.  
Thomas Aynaud (remote) Software Heritage, project presentation and applications to AI 20+5 This talk will provide an overview of what Software Heritage is. We will highlight the data we collect and how we do that. It includes the source code and all the events that can be observed during the development process. We will then explore how to utilize and enrich this information to develop services relevant to AI and the actions we take to create a more open, responsible, and transparent AI.  
 
Thomas Boch First feedback on implementing MCP server to access CDS services 15+5 MCP (Model Context Protocol) is a standard aiming at connecting AI tools to external data sources. We will present the MCP server we developed at CDS, and demonstrate how it allows AI applications to access and consume data from Sesame, MocServer, HiPS2FITS, etc.  
François Lanusse (remote) Multimodal Universe 15+5    
Changed:
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All Discussions 15   pdf
>
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All Discussions 10  
 DCP session Notes : todo

Revision 12025-10-23 - GillesLandais

 
META TOPICPARENT name="InterOpNov2025"

<--  
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DCP Session: Novemeber 2025

[ back to main programme page]

Schedule (IG: IvoaDCP and KD)

Wednesday June 4 @09:00 EDT: Room 1309
Speaker Title Time Abstract Material
Gilles Landais European AI Act 10+5 Quick report on the European Articial Interlligence act.  
Thomas Aynaud (remote) Software Heritage 15+5 Thomas Aynaud (CTO of Software Heritage) is working in INRIA, France.  
Thomas Boch First feedback on implementing MCP server to access CDS services 15+5 MCP (Model Context Protocol) is a standard aiming at connecting AI tools to external data sources. We will present the MCP server we developed at CDS, and demonstrate how it allows AI applications to access and consume data from Sesame, MocServer, HiPS2FITS, etc.  
François Lanusse (remote) Multimodal Universe 15+5    
All Discussions 15   pdf
DCP session Notes : todo
 
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