I'm an applied machine-learning engineer and M.Sc. Bioinformatics candidate at Saarland University. I build reproducible ML systems for scientific data, from experiment design and model evaluation through APIs, containers, CI/CD, and cloud deployment.
My current thesis work at the Helmholtz Institute for Pharmaceutical Research Saarland focuses on scaling leakage-aware dataset splitting for molecular machine learning.
- AFM Explorer - A tested, cloud-deployed platform for Atomic Force Microscopy analysis, with FastAPI, Streamlit, classical and supervised ML, Docker, AWS, CI/CD, and an MCP tool interface. Live demo
- Multilingual Spoken Language Identification - Fine-tuned MMS-300M and XLS-R for 22 Indian languages, diagnosing speaker shortcuts and improving top-1 accuracy to 43.4% with targeted augmentation.
- Seoul Bike Demand Forecasting - Built end-to-end classification and regression pipelines for corrupted temporal data, reaching 0.852 macro F1 with stacked ensembles and 0.371 OOF MSLE with a boosted-tree blend, with SHAP and robustness analysis.
- Scaling DataSAIL - A balance-aware approximation for leakage-conscious molecular dataset splitting, reducing large-dataset runtime while preserving split quality.
- Scribble-Supervised Image Segmentation - Classical and deep-learning segmentation from sparse labels, combining GrabCut and a compact U-Net to reach 75.8% mIoU.
Python · PyTorch · scikit-learn · FastAPI · SQL · Docker · AWS · GitHub Actions · MCP
I'm particularly interested in reliable applied ML, scientific ML, model evaluation, and production AI systems.