Overview Funded Projects Research Domains Teaching

Biography

Professor of AI and Data Science stationed at XU Exponential University of Applied Sciences in Potsdam, Germany. Research methodologies concentrate on the intersection of deep learning interpretability, distributed systems architecture, and applied real-world implementation.

Specialization areas encompass Large Language Models (LLMs), Explainable AI (XAI), and applied machine learning. Additional independent research vectors include algorithmic trading mechanisms, Web 3.0 technology integration, and the fine-tuning of LLMs utilizing cybersecurity and SIEM datasets for autonomous threat detection.

Funded Research & Major Projects

FAIRWORK Germany

Collaborators: WZB Berlin, University of Oxford | Domain: Social Computing
An international action-research initiative analyzing the digital labor economy. The methodology utilizes computational and behavioral analytics to evaluate platform work conditions against established global fair work metrics, advancing regulatory resilience and algorithmic fairness within the gig economy.
WZB Project Overview ↗ Team Profile ↗

Resilient Infrastructure Technology Suite (RITS)

Funding: Brandenburg Model Region Initiative | Domain: Digital Twins & IoT
Investigation into the operational nexus of energy, water, and food infrastructure grids. The project deploys digital twins, Decentralized Physical Infrastructure Networks (DePIN), and Green Swarming IoT integrated with AI to architect self-sustaining, fault-tolerant regional infrastructure systems.
Project Site ↗ Official One-Pager ↗

XAI for Medical Health Diagnostics

Collaborators: Abu Dhabi University, Zayed University, MIT | Domain: Healthcare AI
Development of Explainable AI (XAI) frameworks calibrated for medical diagnostics. The research integrates methodologies including SHAP, LIME, and Grad-CAM to establish transparency, legal compliance, and clinical trustworthiness for AI inferences derived from cardiovascular and generalized clinical data.

Quantitative AI Voluntary Carbon Market (QAVCM) Analytics

Domain: Climate Tech & Real-Time Analytics
Engineering of a high-fidelity analytics architecture for the quantitative, real-time evaluation of voluntary carbon market (VCM) CO₂ certificates. The system leverages AI assistance to ensure data transparency, verify ecological impacts, and algorithmically detect and mitigate greenwashing via real-time data ingestion.
VCM Team Page ↗

Core Research Domains

Large Language Models & RAG

Architectural optimization, retrieval-augmented generation pipelines, and domain-specific fine-tuning, with a primary focus on cybersecurity integration and Security Information and Event Management (SIEM) data processing.

Explainable AI (XAI)

Bridging the performance-interpretability gap in high-stakes environments. Developing methodologies to render complex neural inferences transparent for healthcare, finance, and public sector applications.

Social Computing & Systems

Deployment of behavioral analytics and sentiment mining to measure and quantify the sociological and economic impacts of algorithmic deployment in public spaces and labor markets.

Decentralized Systems & Digital Twins

Integration of blockchain applications, Web 3.0 infrastructure, and resilient digital twin modeling aimed at securing and optimizing critical physical resources and infrastructure.

Teaching & Supervision Responsibilities

Instruction and supervision across Bachelor’s and Master’s degree programs, focusing on the synthesis of theoretical computer science and applied, industry-standard methodologies.

Generative AI & Large Language Models
Text Mining & Information Retrieval
Explainable AI (XAI)
Algorithms & Data Structures
Machine Learning I & II
Blockchain Applications
Deep Neural Networks
Social Media Mining
Data Mining & Big Data Analytics
Special Topics in AI Integration