Research

Understanding Knowledge.Building Intelligent Systems.Preparing Organizations for AI.

Our research sits at the intersection of artificial intelligence, scientific discovery, organizational transformation, and business-process innovation.

We investigate how AI systems can work with structured knowledge, scientific evidence, images, organizational data, management processes, and commercial information. Our objective is not only to develop better technologies, but also to understand how these technologies can be introduced responsibly and effectively into research, business, and institutional environments.

Our work combines conceptual research, applied experimentation, prototype development, organizational assessment, doctoral research, and collaboration with academic and industry partners.

Our Research Focus Areas

Knowledge Graphs, Language Models, and Explainable Reasoning

Large language models can interpret and generate human language, while knowledge graphs can represent entities, relationships, evidence, and context in a structured form.

We investigate how these technologies can be combined to create more reliable, explainable, and context-aware AI systems.

Our research interests include

Knowledge-graph construction and enrichment
Large language models and retrieval-augmented generation
Graph-based retrieval and reasoning
Temporal and contextual knowledge representation
Evidence provenance and source traceability
Explainable recommendations
Domain-specific AI assistants
Human–AI knowledge interaction

The intended outcome is a new generation of AI systems that do more than generate fluent answers. They should also help users understand where information originated, how concepts are connected, and how knowledge changes over time.

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AI for Scientific Discovery and the Development of Knowledge

Science does not progress simply by accumulating publications. It develops through debate, criticism, comparison, replication, interpretation, and the revision of previous ideas.

We study the processes through which scientific knowledge develops and investigate how AI can support those processes without replacing scientific judgment.

Our research interests include

The evolution of scientific ideas
Scientific debate and disagreement
Critical thinking and peer review
Literature synthesis and evidence mapping
Research-gap and contradiction detection
Citation relationships and intellectual development
AI-supported hypothesis generation
Chronological analysis of scientific fields
Human and machine reasoning in research

This research connects the philosophy and sociology of science with scientometrics, natural-language processing, knowledge graphs, and AI-assisted discovery.

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AI-Enabled Project Management

AI changes how projects are selected, planned, delivered, monitored, and evaluated. It can support decision-making and automation, but it also introduces uncertainty, data dependencies, ethical concerns, model risks, and rapidly changing technical requirements.

We examine how ISO 21502 project management principles and established project processes can be adapted for AI-supported and AI-intensive environments.

Our research interests include

AI-assisted project planning
Project governance and accountability
Benefits and value management
Risk, uncertainty, and model-performance management
Stakeholder engagement in AI projects
Data and model dependencies
AI-supported monitoring and reporting
Human oversight and decision authority
Project-management maturity for AI initiatives
Integration of AI into organizational project processes

The goal is to help organizations use AI within project management while maintaining clear responsibility, transparency, quality, and strategic alignment.

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European Funding Intelligence and Proposal Automation

European funding applications require organizations to interpret complex calls, demonstrate eligibility, identify partners, design credible work plans, prepare budgets, and comply with extensive administrative requirements.

We investigate how AI and workflow automation can make these processes more systematic, transparent, and accessible.

Our research interests include

Automated funding-opportunity discovery
Call-text analysis
Eligibility and strategic-fit assessment
Partner and consortium matching
Proposal-structure generation
Work-package and task design
Budget-development support
Compliance and document checking
Evaluation-criteria mapping
Submission workflow management
Grant implementation and reporting support
Funding portfolio intelligence

The objective is not to automate judgment or guarantee funding. It is to reduce administrative workload, improve application quality, and help organizations make better decisions about which opportunities to pursue.

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Intelligent B2B Sales Pipeline Generation

B2B sales frequently depends on fragmented information, inconsistent prospecting, manual qualification, and limited visibility across the sales pipeline.

We study how AI, structured data, and automation can support systematic and evidence-based business development.

Our research interests include

Ideal-customer-profile development
Market and account segmentation
Prospect and lead discovery
Data enrichment
Lead scoring and qualification
Buying-signal identification
Relationship and stakeholder mapping
Personalized outreach support
Pipeline forecasting
Sales-process standardization
Human–AI collaboration in business development
Measurement and continuous improvement

Our goal is to develop sales systems that improve targeting and productivity while maintaining human oversight, professional judgment, and responsible use of customer data.

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Computer Vision and Data Enhancement

Many scientific and professional datasets contain images that are incomplete, inconsistent, difficult to classify, or available only in limited quantities.

We investigate how computer vision and generative AI can improve the analysis and usability of these datasets.

Our research interests include

Image recognition and classification
Feature extraction and representation learning
Multimodal AI
Image-quality enhancement
Synthetic and augmented data
Data harmonization
Dataset bias and representativeness
Model validation and reproducibility
Responsible AI for sensitive datasets

A central objective is to determine when AI-generated or AI-enhanced data can create genuine research value and how its reliability should be evaluated.

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AI-Ready Organizations

Introducing AI is not simply a software-purchasing decision. Organizations require suitable data, leadership, processes, skills, governance, infrastructure, and a culture that supports experimentation and responsible adoption.

We assess the strategic, technological, organizational, and human capabilities that companies need to adopt AI successfully and responsibly.

Our research interests include

AI strategy and leadership
Data and technology readiness
Employee capabilities and AI literacy
Organizational culture
Process maturity
Governance and responsible AI
Change management
Human–AI collaboration
AI-adoption measurement frameworks
Sector-specific readiness models

This area aims to produce practical assessment frameworks that help organizations identify their current maturity, major barriers, priority investments, and appropriate path toward AI adoption.

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How the Research Areas Connect

These focus areas are connected by a common question:

How can AI transform complex knowledge and organizational processes while remaining understandable, manageable, and useful to people?

Knowledge graphs create structured representations of evidence and relationships. Language models provide flexible interaction and interpretation. Computer vision expands AI into image-based information. Organizational-readiness research examines whether institutions can adopt these technologies. Project-management research examines how transformation can be delivered. Funding intelligence helps innovative ideas secure resources, while intelligent sales systems help valuable solutions reach suitable organizations.

Together, these areas form an integrated research agenda connecting scientific knowledge, AI technology, organizational capability, and practical innovation.

Research Methods

Depending on the research question, our work may use:

Systematic and structured literature reviews
Scientometric and bibliometric analysis
Knowledge-graph modelling
Natural-language processing
Large language models
Machine learning and computer vision
Case studies
Surveys and organizational assessments
Design-science research
Prototype development
Comparative evaluation
Expert interviews
Human-in-the-loop experimentation

From Research to Application

We aim to translate research into practical outcomes, including:

Academic publications
Research frameworks
Assessment instruments
AI prototypes
Knowledge models
Decision-support tools
Organizational methodologies
Training and educational materials
Industry and academic collaborations
Doctoral and early-career research opportunities

Collaborate With Us

We welcome collaboration with universities, research groups, public institutions, companies, technology providers, and European innovation partners.

Potential forms of collaboration include joint research, proposal development, doctoral research, pilot studies, organizational assessments, data partnerships, prototype validation, publications, and applied experimentation.

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