AI and Data Architect

“Big Data and Data Science lift the value of data ensures competitiveness, decision-making and digital transformation.”

PhD in Management

Master Thesis: From Automation to Autonomy: Cognitive Multi-Agent Systems for Enterprise AI Adoption

The thesis explores how cognitive Multi-Agent Systems can enable the shift from automation to autonomy in enterprise environments by embedding reasoning, adaptability, and collaboration into AI architectures. It introduces the Enterprise Cognitive Autonomy Framework, integrating theories from AI, cognitive science, and technology adoption to identify factors such as human-system interaction, symbolic–subsymbolic alignment, and strategic resource use. The study concludes that achieving true enterprise autonomy requires both advanced AI design and a fundamental rethinking of how cognitive capabilities are integrated into organizational systems.

Master of Science in Information Technology (M.Sc.)

Master Thesis: Ontology-based Information Extraction for Reporting of Unstructured Data in Business Intelligence Systems

The exponential growth of global data, particularly from the Internet and social media, has created an urgent need for businesses to derive meaningful knowledge from both structured and unstructured information. Traditional Business Intelligence systems, once focused on structured data from ERP and CRM systems, must now evolve to integrate diverse data sources for effective knowledge management. Ontology-based information extraction and semantic processing offer a promising approach to unify structured and unstructured data within Business Warehouse architectures, enabling more intelligent and adaptive decision-making. 

Bachelor of Science in IT-Engineering (B.Sc.)

Bachelor Thesis: Service-oriented Architecture as Implementation of a Process-oriented Integration Architecture of Distributed Enterprises

Knowledge and Expertise:

• Analytics and Big Data Architecture
• Data Mesh and Data Lake Distributed Architectures
• Containerized Architectures and Deployments with Docker and Kubernetes
• Domain Driven Design and Entity Component System (ECS) Architecture
• Cloud Architectures and Infrastructure on AWS and Azure
• Apache Spark (Spark, Scala) and Databricks
• Event-driven systems using Kafka and Confluent
• Hadoop core components (Hive, Flume, Oozie, HBase, HDFS, Yarn)
• Distributed Query-Engine Presto (Starburst)

Languages:

• Python
• Scala
• Java
• SQL (kSQL)
• R