About CauseFinder<\/h1><\/h1><\/div><\/div><\/div><\/div>The next wave of Big Data and Artificial Intelligence (AI) will be centred on extracting deeper structure from observed systems. Built on the rapid development of existing Data and AI technologies, topics such as Multilevel Evolving Knowledge Graphs, Causal Reasoning, Explanation Based Modelling will result in much more efficient solutions than possible today.<\/p>\n<\/div><\/div><\/div>
The project aims at ground breaking results in theory, methodology and algorithms, especially in the fields of scalable multiresolution causality modelling, scalable probabilistic reasoning, evolving knowledge graph construction from diverse data sources and new insights into innovation ecosystem lifecycle management.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
<\/i>Project Duration<\/strong><\/h1><\/div>36 months<\/p>\n<\/div><\/div><\/div>
<\/i>Project Budget<\/strong><\/h1><\/div>approx. 1.4 mil. Euro<\/p>\n<\/div><\/div><\/div>
<\/i>Implementation<\/strong><\/h1><\/div>July 2023 – June 2026<\/p>\n<\/div><\/div><\/div>
<\/i>Contact<\/strong><\/h1><\/div>office<\/span>@causefinder.ase.ro<\/span><\/a><\/p>\n<\/div><\/div><\/div><\/div><\/div>Mission & Objectives<\/p><\/h1><\/div><\/div><\/div>
![](\"https:\/\/causefinder.ase.ro\/wp-content\/uploads\/2025\/09\/CauseFinder-2.svg\" "\\"CauseFinder")
<\/span><\/div><\/div><\/div>The long-term perspective of the project lies in interconnection, model development, and understanding of all relevant aspects of the global innovation ecosystem, from the emergence of ideas to their potential implementation in business, as well as decision-making in research policy. The aim is to employ modern artificial intelligence (AI) techniques to support the modeling and analysis of the complex system of the global innovation lifecycle, with specialized approaches at the national level.<\/span><\/p>\n<\/div><\/div><\/div><\/i>Innovation Knowledge Graph<\/h1><\/div>\nBuilding an evolving knowledge graph from various data sources connected to a global model.<\/p>\n<\/div>\n<\/div><\/div><\/div>
<\/i>Causality<\/h1><\/div>\nConstructing a probabilistic, multi-resolution causal structure of the observed world derived from data streams.<\/p>\n<\/div>\n<\/div><\/div><\/div>
<\/i>Probabilistic Reasoning<\/h1><\/div>\nDeveloping scalable probabilistic reasoning that leverages the integration of knowledge graphs with multiple operators.<\/p>\n<\/div>\n<\/div><\/div><\/div>
<\/i>Representation Learning for Unsupervised Phenomena and Anomaly Detection<\/h1><\/div>Using existing representation learning to enhance the grounding power of reasoning and analysis techniques for unsupervised phenomena and anomaly detection.<\/p>\n<\/div><\/div><\/div>
<\/i>Use Case Validation<\/h1><\/div>Validating the proposed techniques in the field of innovation\u00a0management.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
![](\"https:\/\/causefinder.ase.ro\/wp-content\/uploads\/2026\/05\/ChatGPT-Image-12-mai-2026-13_16_59.png\" "\\"ChatGPT")
<\/span><\/div><\/div><\/div><\/div>Our Platform<\/strong><\/h2>\nVisit our platform to find out more about it!<\/h6>\n
http:\/\/research.causefinder.ase.ro<\/a><\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div>Our Team<\/p><\/h1><\/div>
![\"Dumitru](\"https:\/\/causefinder.ase.ro\/wp-content\/uploads\/2025\/08\/6.png\")
<\/div><\/div>Dumitru Roman<\/span><\/span><\/div><\/a><\/div><\/div><\/div>Researcher<\/div><\/div><\/div><\/div><\/div>![\"Alina](\"https:\/\/causefinder.ase.ro\/wp-content\/uploads\/2025\/08\/15.png\")
<\/div><\/div>Alina Dima<\/span><\/span><\/div><\/a><\/div><\/div><\/div>Researcher<\/div><\/div><\/div><\/div><\/div>![\"Vasile](\"https:\/\/causefinder.ase.ro\/wp-content\/uploads\/2025\/08\/13.png\")
<\/div><\/div>Vasile Strat<\/span><\/span><\/div><\/a><\/div><\/div><\/div>Researcher<\/div><\/div><\/div><\/div><\/div>![\"Adriana](\"https:\/\/causefinder.ase.ro\/wp-content\/uploads\/2025\/08\/1.png\")
<\/div><\/div>Adriana Davidescu<\/span><\/span><\/div><\/a><\/div><\/div><\/div>Researcher<\/div><\/div><\/div><\/div><\/div><\/div>![\"Giani](\"https:\/\/causefinder.ase.ro\/wp-content\/uploads\/2025\/08\/8.png\")
<\/div><\/div>Giani Gradinaru<\/span><\/span><\/div><\/a><\/div><\/div><\/div>
The next wave of Big Data and Artificial Intelligence (AI) will be centred on extracting deeper structure from observed systems. Built on the rapid development of existing Data and AI technologies, topics such as Multilevel Evolving Knowledge Graphs, Causal Reasoning, Explanation Based Modelling will result in much more efficient solutions than possible today.<\/p>\n<\/div><\/div><\/div>
The project aims at ground breaking results in theory, methodology and algorithms, especially in the fields of scalable multiresolution causality modelling, scalable probabilistic reasoning, evolving knowledge graph construction from diverse data sources and new insights into innovation ecosystem lifecycle management.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
Project Duration<\/strong><\/h1><\/div>36 months<\/p>\n<\/div><\/div><\/div>
<\/i>Project Budget<\/strong><\/h1><\/div>approx. 1.4 mil. Euro<\/p>\n<\/div><\/div><\/div>
<\/i>Implementation<\/strong><\/h1><\/div>July 2023 – June 2026<\/p>\n<\/div><\/div><\/div>
<\/i>Contact<\/strong><\/h1><\/div>office<\/span>@causefinder.ase.ro<\/span><\/a><\/p>\n<\/div><\/div><\/div><\/div><\/div>Mission & Objectives<\/p><\/h1><\/div><\/div><\/div>
<\/span><\/div><\/div><\/div>The long-term perspective of the project lies in interconnection, model development, and understanding of all relevant aspects of the global innovation ecosystem, from the emergence of ideas to their potential implementation in business, as well as decision-making in research policy. The aim is to employ modern artificial intelligence (AI) techniques to support the modeling and analysis of the complex system of the global innovation lifecycle, with specialized approaches at the national level.<\/span><\/p>\n<\/div><\/div><\/div><\/i>Innovation Knowledge Graph<\/h1><\/div>\nBuilding an evolving knowledge graph from various data sources connected to a global model.<\/p>\n<\/div>\n<\/div><\/div><\/div>
<\/i>Causality<\/h1><\/div>\nConstructing a probabilistic, multi-resolution causal structure of the observed world derived from data streams.<\/p>\n<\/div>\n<\/div><\/div><\/div>
<\/i>Probabilistic Reasoning<\/h1><\/div>\nDeveloping scalable probabilistic reasoning that leverages the integration of knowledge graphs with multiple operators.<\/p>\n<\/div>\n<\/div><\/div><\/div>
<\/i>Representation Learning for Unsupervised Phenomena and Anomaly Detection<\/h1><\/div>Using existing representation learning to enhance the grounding power of reasoning and analysis techniques for unsupervised phenomena and anomaly detection.<\/p>\n<\/div><\/div><\/div>
<\/i>Use Case Validation<\/h1><\/div>Validating the proposed techniques in the field of innovation\u00a0management.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
<\/span><\/div><\/div><\/div><\/div>Our Platform<\/strong><\/h2>\nVisit our platform to find out more about it!<\/h6>\n
http:\/\/research.causefinder.ase.ro<\/a><\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div>Our Team<\/p><\/h1><\/div>
<\/div><\/div>Dumitru Roman<\/span><\/span><\/div><\/a><\/div><\/div><\/div>Researcher<\/div><\/div><\/div><\/div><\/div><\/div><\/div>Alina Dima<\/span><\/span><\/div><\/a><\/div><\/div><\/div>Researcher<\/div><\/div><\/div><\/div><\/div><\/div><\/div>Vasile Strat<\/span><\/span><\/div><\/a><\/div><\/div><\/div>Researcher<\/div><\/div><\/div><\/div><\/div><\/div><\/div>Adriana Davidescu<\/span><\/span><\/div><\/a><\/div><\/div><\/div>Researcher<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>Giani Gradinaru<\/span><\/span><\/div><\/a><\/div><\/div><\/div>
36 months<\/p>\n<\/div><\/div><\/div>
Project Budget<\/strong><\/h1><\/div>approx. 1.4 mil. Euro<\/p>\n<\/div><\/div><\/div>
<\/i>Implementation<\/strong><\/h1><\/div>July 2023 – June 2026<\/p>\n<\/div><\/div><\/div>
<\/i>Contact<\/strong><\/h1><\/div>office<\/span>@causefinder.ase.ro<\/span><\/a><\/p>\n<\/div><\/div><\/div><\/div><\/div>Mission & Objectives<\/p><\/h1><\/div><\/div><\/div>
<\/span><\/div><\/div><\/div>The long-term perspective of the project lies in interconnection, model development, and understanding of all relevant aspects of the global innovation ecosystem, from the emergence of ideas to their potential implementation in business, as well as decision-making in research policy. The aim is to employ modern artificial intelligence (AI) techniques to support the modeling and analysis of the complex system of the global innovation lifecycle, with specialized approaches at the national level.<\/span><\/p>\n<\/div><\/div><\/div><\/i>Innovation Knowledge Graph<\/h1><\/div>\nBuilding an evolving knowledge graph from various data sources connected to a global model.<\/p>\n<\/div>\n<\/div><\/div><\/div>
<\/i>Causality<\/h1><\/div>\nConstructing a probabilistic, multi-resolution causal structure of the observed world derived from data streams.<\/p>\n<\/div>\n<\/div><\/div><\/div>
<\/i>Probabilistic Reasoning<\/h1><\/div>\nDeveloping scalable probabilistic reasoning that leverages the integration of knowledge graphs with multiple operators.<\/p>\n<\/div>\n<\/div><\/div><\/div>
<\/i>Representation Learning for Unsupervised Phenomena and Anomaly Detection<\/h1><\/div>Using existing representation learning to enhance the grounding power of reasoning and analysis techniques for unsupervised phenomena and anomaly detection.<\/p>\n<\/div><\/div><\/div>
<\/i>Use Case Validation<\/h1><\/div>Validating the proposed techniques in the field of innovation\u00a0management.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
<\/span><\/div><\/div><\/div><\/div>Our Platform<\/strong><\/h2>\nVisit our platform to find out more about it!<\/h6>\n
http:\/\/research.causefinder.ase.ro<\/a><\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div>Our Team<\/p><\/h1><\/div>
<\/div><\/div>Dumitru Roman<\/span><\/span><\/div><\/a><\/div><\/div><\/div>Researcher<\/div><\/div><\/div><\/div><\/div><\/div><\/div>Alina Dima<\/span><\/span><\/div><\/a><\/div><\/div><\/div>Researcher<\/div><\/div><\/div><\/div><\/div><\/div><\/div>Vasile Strat<\/span><\/span><\/div><\/a><\/div><\/div><\/div>Researcher<\/div><\/div><\/div><\/div><\/div><\/div><\/div>Adriana Davidescu<\/span><\/span><\/div><\/a><\/div><\/div><\/div>Researcher<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>Giani Gradinaru<\/span><\/span><\/div><\/a><\/div><\/div><\/div>
approx. 1.4 mil. Euro<\/p>\n<\/div><\/div><\/div>
Implementation<\/strong><\/h1><\/div>July 2023 – June 2026<\/p>\n<\/div><\/div><\/div>
<\/i>Contact<\/strong><\/h1><\/div>office<\/span>@causefinder.ase.ro<\/span><\/a><\/p>\n<\/div><\/div><\/div><\/div><\/div>Mission & Objectives<\/p><\/h1><\/div><\/div><\/div>
<\/span><\/div><\/div><\/div>The long-term perspective of the project lies in interconnection, model development, and understanding of all relevant aspects of the global innovation ecosystem, from the emergence of ideas to their potential implementation in business, as well as decision-making in research policy. The aim is to employ modern artificial intelligence (AI) techniques to support the modeling and analysis of the complex system of the global innovation lifecycle, with specialized approaches at the national level.<\/span><\/p>\n<\/div><\/div><\/div><\/i>Innovation Knowledge Graph<\/h1><\/div>\nBuilding an evolving knowledge graph from various data sources connected to a global model.<\/p>\n<\/div>\n<\/div><\/div><\/div>
<\/i>Causality<\/h1><\/div>\nConstructing a probabilistic, multi-resolution causal structure of the observed world derived from data streams.<\/p>\n<\/div>\n<\/div><\/div><\/div>
<\/i>Probabilistic Reasoning<\/h1><\/div>\nDeveloping scalable probabilistic reasoning that leverages the integration of knowledge graphs with multiple operators.<\/p>\n<\/div>\n<\/div><\/div><\/div>
<\/i>Representation Learning for Unsupervised Phenomena and Anomaly Detection<\/h1><\/div>Using existing representation learning to enhance the grounding power of reasoning and analysis techniques for unsupervised phenomena and anomaly detection.<\/p>\n<\/div><\/div><\/div>
<\/i>Use Case Validation<\/h1><\/div>Validating the proposed techniques in the field of innovation\u00a0management.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
<\/span><\/div><\/div><\/div><\/div>Our Platform<\/strong><\/h2>\nVisit our platform to find out more about it!<\/h6>\n
http:\/\/research.causefinder.ase.ro<\/a><\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div>Our Team<\/p><\/h1><\/div>
<\/div><\/div>Dumitru Roman<\/span><\/span><\/div><\/a><\/div><\/div><\/div>Researcher<\/div><\/div><\/div><\/div><\/div><\/div><\/div>Alina Dima<\/span><\/span><\/div><\/a><\/div><\/div><\/div>Researcher<\/div><\/div><\/div><\/div><\/div><\/div><\/div>Vasile Strat<\/span><\/span><\/div><\/a><\/div><\/div><\/div>Researcher<\/div><\/div><\/div><\/div><\/div><\/div><\/div>Adriana Davidescu<\/span><\/span><\/div><\/a><\/div><\/div><\/div>Researcher<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>Giani Gradinaru<\/span><\/span><\/div><\/a><\/div><\/div><\/div>
July 2023 – June 2026<\/p>\n<\/div><\/div><\/div>
Contact<\/strong><\/h1><\/div>office<\/span>@causefinder.ase.ro<\/span><\/a><\/p>\n<\/div><\/div><\/div><\/div><\/div>Mission & Objectives<\/p><\/h1><\/div><\/div><\/div>
<\/span><\/div><\/div><\/div>The long-term perspective of the project lies in interconnection, model development, and understanding of all relevant aspects of the global innovation ecosystem, from the emergence of ideas to their potential implementation in business, as well as decision-making in research policy. The aim is to employ modern artificial intelligence (AI) techniques to support the modeling and analysis of the complex system of the global innovation lifecycle, with specialized approaches at the national level.<\/span><\/p>\n<\/div><\/div><\/div><\/i>Innovation Knowledge Graph<\/h1><\/div>\nBuilding an evolving knowledge graph from various data sources connected to a global model.<\/p>\n<\/div>\n<\/div><\/div><\/div>
<\/i>Causality<\/h1><\/div>\nConstructing a probabilistic, multi-resolution causal structure of the observed world derived from data streams.<\/p>\n<\/div>\n<\/div><\/div><\/div>
<\/i>Probabilistic Reasoning<\/h1><\/div>\nDeveloping scalable probabilistic reasoning that leverages the integration of knowledge graphs with multiple operators.<\/p>\n<\/div>\n<\/div><\/div><\/div>
<\/i>Representation Learning for Unsupervised Phenomena and Anomaly Detection<\/h1><\/div>Using existing representation learning to enhance the grounding power of reasoning and analysis techniques for unsupervised phenomena and anomaly detection.<\/p>\n<\/div><\/div><\/div>
<\/i>Use Case Validation<\/h1><\/div>Validating the proposed techniques in the field of innovation\u00a0management.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
<\/span><\/div><\/div><\/div><\/div>Our Platform<\/strong><\/h2>\nVisit our platform to find out more about it!<\/h6>\n
http:\/\/research.causefinder.ase.ro<\/a><\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div>Our Team<\/p><\/h1><\/div>
<\/div><\/div>Dumitru Roman<\/span><\/span><\/div><\/a><\/div><\/div><\/div>Researcher<\/div><\/div><\/div><\/div><\/div><\/div><\/div>Alina Dima<\/span><\/span><\/div><\/a><\/div><\/div><\/div>Researcher<\/div><\/div><\/div><\/div><\/div><\/div><\/div>Vasile Strat<\/span><\/span><\/div><\/a><\/div><\/div><\/div>Researcher<\/div><\/div><\/div><\/div><\/div><\/div><\/div>Adriana Davidescu<\/span><\/span><\/div><\/a><\/div><\/div><\/div>Researcher<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>Giani Gradinaru<\/span><\/span><\/div><\/a><\/div><\/div><\/div>
office<\/span>@causefinder.ase.ro<\/span><\/a><\/p>\n<\/div><\/div><\/div><\/div><\/div> Mission & Objectives<\/p><\/h1><\/div><\/div><\/div> The long-term perspective of the project lies in interconnection, model development, and understanding of all relevant aspects of the global innovation ecosystem, from the emergence of ideas to their potential implementation in business, as well as decision-making in research policy. The aim is to employ modern artificial intelligence (AI) techniques to support the modeling and analysis of the complex system of the global innovation lifecycle, with specialized approaches at the national level.<\/span><\/p>\n<\/div><\/div><\/div> Building an evolving knowledge graph from various data sources connected to a global model.<\/p>\n<\/div>\n<\/div><\/div><\/div> Constructing a probabilistic, multi-resolution causal structure of the observed world derived from data streams.<\/p>\n<\/div>\n<\/div><\/div><\/div> Developing scalable probabilistic reasoning that leverages the integration of knowledge graphs with multiple operators.<\/p>\n<\/div>\n<\/div><\/div><\/div> Using existing representation learning to enhance the grounding power of reasoning and analysis techniques for unsupervised phenomena and anomaly detection.<\/p>\n<\/div><\/div><\/div> Validating the proposed techniques in the field of innovation\u00a0management.<\/p>\n<\/div><\/div><\/div><\/div><\/div> http:\/\/research.causefinder.ase.ro<\/a><\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div> Our Team<\/p><\/h1><\/div><\/span><\/div><\/div><\/div>Innovation Knowledge Graph<\/h1><\/div>
Causality<\/h1><\/div>
Probabilistic Reasoning<\/h1><\/div>
Representation Learning for Unsupervised Phenomena and Anomaly Detection<\/h1><\/div>
Use Case Validation<\/h1><\/div>
<\/span><\/div><\/div><\/div>Our Platform<\/strong><\/h2>\n
Visit our platform to find out more about it!<\/h6>\n
<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>
![\"Marius](\"https:\/\/causefinder.ase.ro\/wp-content\/uploads\/2025\/08\/9.png\")
<\/div><\/div>![\"Catalina](\"https:\/\/causefinder.ase.ro\/wp-content\/uploads\/2025\/08\/4.png\")
<\/div><\/div>![\"Stere](\"https:\/\/causefinder.ase.ro\/wp-content\/uploads\/2025\/08\/12.png\")
<\/div><\/div>![\"Mihail](\"https:\/\/causefinder.ase.ro\/wp-content\/uploads\/2025\/08\/10.png\")
<\/div><\/div>![\"Mihai](\"https:\/\/causefinder.ase.ro\/wp-content\/uploads\/2025\/08\/7.png\")
<\/div><\/div>