Research Overview

Advancing the frontiers of artificial intelligence through interdisciplinary research in computer vision, scientific machine learning, and enterprise AI applications.

Research Focus Areas

Bridging theoretical breakthroughs with practical applications

Computer Vision & Scientific Imaging

Developing novel algorithms for biological image analysis, super-resolution microscopy, and automated quality control with nanometer precision.

  • Super-resolution microscopy algorithms
  • Image registration and correlation
  • Automated biological structure analysis
  • Real-time image processing pipelines

AI/ML for Scientific Computing

Creating specialized neural network architectures that combine domain knowledge with deep learning for scientific applications.

  • Custom neural network architectures
  • Compressed sensing with deep learning
  • Physics-informed neural networks
  • Multi-modal learning approaches

Enterprise AI & Production Systems

Translating research breakthroughs into scalable, production-ready AI systems for real-world applications and business value.

  • MLOps and model deployment
  • Scalable data processing pipelines
  • Enterprise system integration
  • Performance optimization & GPU computing

Research Journey

From academic foundations to industry applications

2019-2023

PhD in Biophysics

University of Würzburg - Developed novel AI algorithms for super-resolution microscopy, combining compressed sensing with deep neural networks.

Deep Learning Computer Vision Scientific Computing
2024-Present

SAP AI Consultant

Enterprise AI implementations and showcases. Bridging academic research with real-world business applications in large-scale systems.

Enterprise AI MLOps System Integration
2024-Present

EndureXAI Platform

Independent development of full-stack AI platform for sports analytics. Demonstrating end-to-end AI product development capabilities.

Product Development Sports Analytics Full-Stack AI

Current Research

Active projects and ongoing investigations

AttentionAI

Transformers for Scientific Imaging

Investigating the application of transformer architectures to gain additional positional information over large timeframes in single-molecule localization microscopy datasets.

Novel attention mechanisms for temporal data
Enhanced spatial-temporal resolution
Long-range dependency modeling

Status: In Development - Expected publication Q3 2025

Collaborative Projects

Multi-Institutional Research

Ongoing collaborations with leading research institutions on AI applications in biological imaging, neuroscience, and medical diagnostics.

University of Würzburg

Super-resolution microscopy and AI integration

International Collaborations

Cross-institutional AI methodology development

Industry Partnerships

Enterprise AI implementation consulting

Research Methodology

Interdisciplinary approach combining theory and practice

Theoretical Foundation

Mathematical modeling and algorithm design based on solid theoretical principles

Implementation

High-performance implementations using modern frameworks and GPU acceleration

Validation

Rigorous testing on real datasets with quantitative performance metrics

Translation

Bridging research to practical applications and production systems

Research Impact

Quantifiable contributions to the field

16+
Publications
Peer-reviewed articles
372+
Citations
Google Scholar
5+
Open Source
Research tools
3
Nature Family
High-impact journals

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