Engineering professional passionate about developing innovative software systems and exploring data analytics. Skilled in Python, C, C++, and Linux, with strong teamwork and interpersonal abilities.

Education

Software Engineering and Management

  • Grade: Master’s graduated with distinction
  • Major: Intelligent Systems
  • Minor: Management and Strategy
  • Thesis: Embedded Image-based Localization For Assessment Of Critical Infrastructure



Recent Roles

For a comprehensive overview of my professional roles and education, visit my GitHub repository where I maintain updated versions of my resume, CV, and cover letter.

Innovation Engineer at SkySpecs

  • Developing a machine learning application to detect unusually high rotor blade imbalance in wind turbines by analyzing high-frequency time-series data. Leveraging predictive models such as Random Forest, Gradient Boosting, and Ridge Regression, incorporating feature engineering, and hyperparameter optimization to enhance prediction accuracy.
  • Building an LLM-based knowledge base, enabling clients to interact with their specific data analysis results. Currently evaluating low-code solutions, libraries like Langflow, and various vector storage and embedding technologies as part of a comprehensive RAG (Retrieval-Augmented Generation) pipeline.
  • Engineered and deployed a FastAPI-driven Python microservice architecture that delivers RESTful APIs for custom data processing, analytics, and machine learning tasks.

Data Scientist at SkySpecs

  • Performed data manipulation, analysis, and visualization of large-scale time-series data, including SCADA (Supervisory Control and Data Acquisition) data from individual wind turbines, integrating additional sources such as meteorological data, error logs, and maintenance records. Applied statistical and machine learning methods to classify, cluster, and interpret data, identifying performance anomalies and enhancing operational efficiency. Utilized Python with libraries such as pandas, Matplotlib, Seaborn, Plotly, scikit-learn, NumPy, and SciPy.
  • Developed algorithms to address critical challenges in the wind industry, such as production loss assessment, pitch misalignment detection, and performance analysis, ensuring efficiency and applicability for monthly execution across more than 6,000 client wind turbines.
  • Introduced processes for coding standards, version control, and documentation, ensuring high-quality, maintainable, and scalable codebases, and facilitating collaboration among team members.

Research Assistant at Joanneum Research

  • Engineered computer vision applications to enhance real-time object detection and pose estimation capabilities of a drone, using only single 2D imagery from its calibrated camera system.
  • Constructed a robust data acquisition pipeline from a highly accurate infrared-based tracking system, utilizing the Robot Operating System (ROS), to generate a suitable dataset for algorithm development and testing.
  • Developed and deployed Docker-based containerized services optimized for NVIDIA Jetson embedded computing platforms.
  • Prepared and delivered detailed interim reports and presentations for the FFG (Austrian research funding agency), highlighting project progress, key findings, and strategic recommendations to ensure stakeholder alignment and continued project funding.

Publications