Principal Component Analysis Experts in Germany

• Consulting on complex IT, accounting, controlling, and business intelligence projects from the analysis phase through implementation to go-live support
• Work as consultant, project manager, and subproject manager
• Focus on business process analysis and implementation
• Controlling: sales, marketing, project, financial, and cost controlling
• Accounting: General Ledger, Accounts Receivable, Accounts Payable, asset accounting
• Implementation and training of SAP R/3 FI/CO
• Operational experience in controlling, sales, human resources, and commercial project management before my consulting work

• Development of high-performance ad distribution via auction
• Holistic (multi-campaign & multi-channel) advertisement placement optimization
• Algorithmic optimization for NP-Hard/NP-e
• Multiple Knapsack Problem with constraints
• Online estimation of parameters in stochastic environments

Tools: Python, R, Kotlin, MILP/SAT/CP Solvers, Pytorch, Pandas, Docker

• Designed and implemented a full machine learning pipeline for churn prediction using the Telco dataset.
• Applied preprocessing techniques including missing value handling, categorical encoding, feature scaling, and PCA.
• Built and compared over 15 models (logistic regression, random forest, XGBoost, etc.) and evaluated them using accuracy, precision, recall, F1 score, ROC AUC, and PR AUC.
• Tuned hyperparameters with GridSearchCV, achieving 80.6% accuracy with random forest and XGBoost.
• Created visual reports (bar plots, heatmaps, radar charts) to interpret model performance and churn drivers.
• Exported reusable pipelines and trained models with joblib for deployment.

• Primary Research:
• Developed a compact (<700M parameters) generative vision-language model for whole slide image (WSI) by refining image tokenisation.
• Established an improved evaluation framework, including a curated question-answering dataset and metric selection.
• In preparation for submission.
• Collaboration:
• Conducting research in digital biomarker discovery in computational pathology (CPath) using AI methods.
• Collaborated on projects with international partners, including the Francis Crick Institute (Molecular biomarker prediction in Clear-cell renal carcinoma), HeCOG Greece (Lynch syndrome identification in colorectal carcinoma and Multimodal survival prediction for Prostate adenocarcinoma) and the National Cancer Center Hospital Japan (HIBIRD).
• The work with the Francis Crick Institute is currently being prepared for submission. The collaborative work in Japan has already been published, and the HeCOG projects are ongoing.
• Consortium:
• Manage inter-institutional collaboration and objectives as the KatherLab representative for the LiSYM Consortium.
• Teaching:
• Conducted online workshop sessions for two years at the Clinicum Digitale, educating physicians and medical students on the fundamentals of AI and Python skills.
• Led a multimodal foundation model workshop at the AI in Cancer Research Summer School in Corfu, organized as part of ESAC.
• Presented a talk on vision-language models at the AI in Medicine Summer School, a collaborative event by EKFZ, GENIAL, the TransformLiver Consortium, and ESAC.

• Gathered tire images and their feature descriptions.
• Cleaned dataset of image metadata using pandas.
• Stored image feature embeddings in Chroma vector db.
• Used image augmentations to increase dataset size.
• Used sklearn to create shuffled datasets and imbalanced-learn to balance class sizes in dataset.
• Used PyTorch to train and test different neural networks.
• Validated model using custom accuracy metric based on similarity search in ChromaDB.
• Visualized accuracy predictions using matplotlib.
• Plugged trained model into DreamBooth to train stable diffusion model and generate new images of tires.
• Created custom Docker image in Amazon Elastic Container Registry for machine learning script.

• Applied AI-driven CADD methodologies for biosynthetic pathway optimization and molecule screening.
• Integrated synthetic biology with computational chemistry workflows for rapid in-silico experimentation.
• Automated ML pipelines using Python, PyTorch, and Scikit-learn on Linux, improving model testing and reproducibility.

• Lead engineer for the MatchMaker Toolbox (KNIME): Index Builder, Approximate Matcher, Character Mapper, license nodes
• Designed M|ARS (MatchMaker Agentic Retrieval System) — hybrid retrieval combining deterministic search + LLM tooling
• Developed internal RAG and search prototypes (MatchMaker + vector search + LLM)

• Research and develop models for chatbots, NLP and LLMs (e.g. Llama, Qwen, OpenAI)
• Enhance chatbots with RAG, in-context learning
• Supervised fine-tuning (PEFT, LoRA), Huggingface or Unsloth
• Advanced training methods: Test-time training, (transductive) active learning, reinforcement learning
• High-throughput serving with vLLM
• Apply embedding models (e.g. SentenceTransformers), similarity/vector search or vector DB or ranking (e.g. LlamaIndex, Faiss, LangChain)
• Generate and filter synthetic data, clustering
• Detect hallucinations
• Evaluate chatbot models (Rouge, BLEU, F1-Score, Recall, Precision)
• Visualization of experiments (matplotlib)

• Managing the media agency
• Media strategy for various campaigns
• Campaign management
• Developing social media concept
• Reviewing reports and PCAs

• Developing an AI-based corrosion detection system for maritime infrastructure as part of the CHAI Research Project.
• Built a binary image classification model to detect corrosion using a dataset of 5,000+ metal surface images.
• Automated data labeling from segmentation masks and designed bounding box generation workflows for individual corrosion areas.
• Conducted data preprocessing, data augmentation, and model evaluation (accuracy, precision, recall, F1-score).
• Collaborated with the research team to integrate computer-vision workflows for corrosion monitoring and dataset enhancement.

• Mechanical engineering
• Post-merger integration
• Preparation for S4/HANA implementation
• VIM implementation

• Projects in Azure and Google Cloud
• Azure DevOps
• Google GKE and Azure AKS
• Workload Identity Federation
• Creation of all resources with Terraform and Ansible
• Available for projects in DevOps, Kubernetes, Rancher and OpenShift
• High-availability database solutions with Microsoft SQL Server

• CO Greenfield introduction on S/4 HANA (margin analysis, contribution margin accounting under two parallel rules)
• Data migration design for adding a company code
• Setup of parallel contribution margin accounting via extension ledger with alternative valuations
• Definition and implementation of derivation strategies
• User and key-user training

• Supported and guided several startup companies in the online services area
• Created business plans and required documents for company organization and marketing activities to obtain public funding
• Reviewed business models for plausibility, validity, marketability, and viability