I was helping the DeepSense (Textmining) Team by further developing a Data pipeline that checks all OTTO product texts and images for legal issues. We also migrated the complete pipeline to the Google Cloud Platform, made it future proof by scaling it horizontally and massively reduce the time needed to run the whole pipeline.Technologies: - GCP: IAM, Composer (Airflow), Kubernetes (GKE), Cloud Storage, Cloud Build- Python, Pandas, gRPC & REST- Hadoop, BigQuery

Implemented a Data processing pipeline that is SEO-optimizing texts.Technologies: Docker, Pandas, MongoDB

Implemented projects:2020: Map Matching of vehicle position dataInput: Billions of noisy vehicle telemetry data points (position, direction etc.)Output: Vehicle trajectories matched to streetThe algorithm is scheduled periodically on AWS Batch to process historic telemetry data stored on S3. I used GraphHopper Map Matching and implemented a Particle Filter that leads to matched trajectories of high quality. The quality of the processed trajectories is measured and can be monitored using AWS CloudWatch.TechnologiesAWS Batch, Lambda, S3, CloudWatch, Docker, Python, Pandas, OSM, Graphhopper-------2019: Frontend and Backend for managing models / clusters for a Fleet ML serviceA UI (and Backend for the Frontend) was built to make an ML solution easy to expand to new cities and easy maintanable. The UI makes it possible to manage and extend the services for people that are not deeply involved into the technical details. A specialty about the project is the storage of the meta data (cities, clusters, POIs) via GitHub API to allow the 4-eyes principle via GitHub PRs and enable versioning and rollbacks.TechnologiesAWS ECS, S3, Docker, GitHub API, Python, ol3js, Vue.js, tailwindcss-------2019: Interactive Vehicle ETA prediction visualization.For a shuttle service 3 different estimations of arrival time (ETA) are used. To track their performance over time and by the given circumstances these ETAs were collected and compared by their actual arrival time. An interactive visualization (Backend & Frontend) was created to analyze the different ETAs.Technologies:data collection: AWS Lambda, S3backend: AWS ECS, Docker, Python Flaskfrontend: Vue.js, ol3js, d3.js-------

After our company (ROOMAPS) and team was acquired by moovel Group we started to migrate our Indoor Navigation Stack to the moovel Maps technologies. Also we further extended our technologies to indoor mapping via Tango devices and 3D rendering of buildings. In July 2016 I started my journey as a Machine Learning Engineer in the Data Science Team building projects like Bus Delay Prediction, Demand Prediction for Shuttle services and intelligent relocation of shuttles based on estimated demands.

ROOMAPS was an innovative indoor navigation & information system for smartphones with individual map-design and modern technology for positioning indoors.The core of the ROOMAPS technology consisted of:Map Import & Rendering. Import map features based on AutoCAD files and store as PostGIS geometries. The tile server renders the tiles on-the-fly when requested. Served through AWS CloudFront they are cached for the next requests. For rendering the Java Topology Suite and GeoTools have been used.Navigation Mesh Building & Routing. Based on the walkable area a navigation mesh (a graph which consist of convex connected Polygons) is created automatically. This is used for the A* routing algorithm to find shortest pathes.Indoorpositioning. An extensible approach of a particle filter and Map Matching based on Navigation Meshs was used to get the positioning of a smartphone. Depending on the environment all the available information a smartphone can gather was fed into the algorithm to improve its accuracy: Acceleration, Gyro, Magnetic Field, Bluetooth, WiFi, Barometers.

Java IT-Business Software Entwicklung

Java IT-Business Software Entwicklung