Artificial Intelligence & Robotics MSc student · Technische Universität Nürnberg (UTN)
Building and evaluating machine-learning systems in computer vision, robotics, geospatial analytics and applied AI.
Portfolio · LinkedIn · Repositories
I work across the full experimental lifecycle: data pipelines, baselines, model training, stress testing, reproducibility and usable interfaces. I prefer documented trade-offs and measured results to unsupported performance claims.
| Project | Engineering or research focus |
|---|---|
| SignBridge-ASL | American Sign Language recognition research across static images, isolated video, reliability and evaluation; no verified public model release yet |
| Deepfake robustness benchmark | Deepfake detection under compression, resizing and blur; video-grouped evaluation, calibration and uncertainty |
| Sim2Real-Bench | Panda manipulation in MuJoCo/robosuite, policy robustness, failure prediction and reproducible evaluation |
| Nuremberg land-cover intelligence | Sentinel-2, 100 m land-cover estimates, spatial cross-validation and an interactive analysis tool |
| European image geolocation | Compact hierarchical visual geolocation and spherical decoding |
| Cabin Atlas | Aircraft-aware passenger/crew service application, transactional inventory and explainable recommendations |
| Voice Comparison Studio | Reference-based comparison of multiple speech-synthesis engines with a separate FastAPI backend |
| Redshift streaming analytics | Streaming data ingestion and analytics workflows |
- Land-cover composition: built-up fraction, five-fold spatial CV: MAE 0.0489 ± 0.0029, RMSE 0.0979 ± 0.0050, R² 0.9146 ± 0.0091. Details in the land-cover project.
- Image geolocation: reported 150.67 km median validation error for a 4.63M-parameter model; see experiment details.
- ASL research: co-authored Deep Gesture Interpretation for American Sign Language (2024). The newer SignBridge repository is an evolving research implementation; its current code is not an independently validated deployment.
Project-level READMEs distinguish reported experiments, tests, prototypes and work still requiring external validation.
Languages: Python, C/C++, TypeScript, JavaScript, SQL
ML and vision: PyTorch, TensorFlow/Keras, scikit-learn, LightGBM, OpenCV, EfficientNet
Robotics: ROS 2, MuJoCo, NVIDIA Isaac Sim/Lab, Webots
AI applications: Hugging Face, retrieval-augmented generation, LangChain, FAISS, MCP
Data and delivery: PostgreSQL, DuckDB, Kafka, Docker, FastAPI, React, GitHub Actions
I am pursuing an MSc in Artificial Intelligence and Robotics at UTN in Nuremberg, Germany. Previously, as an AI & Automation R&D intern at ICAR–NBSS&LUP, I built Python APIs and automated data-access workflows.
I am interested in applied AI, robotics, computer vision and research engineering opportunities. The best entry points are the project READMEs, which contain the actual scope, setup instructions, evaluation methods and limitations.