Leiden, South Holland, Netherlands
Creative and data-driven MSc Computer Science graduate specialized in AI and Machine Learning. Passionate about leveraging statistical analysis, data mining, and evolutionary algorithms to solve complex problems. Experienced in architecting and optimizing machine learning pipelines, with a focus on achieving scalability and performance. Eager to apply my skills to contribute to innovative projects where data is leveraged to drive business decisions and growth.
Connected Universiteit Leiden and Nederlandse Spoorwegen in a pilot project to enhance railway reliability. I designed and implemented a machine learning pipeline to predict the remaining useful life (RUL) of the pantograph’s current collector, which is a key wear component. The system combines multiple data sources to establish a baseline wear pattern, enabling flagging of early degradation to proactively trigger inspections, reducing operational risk. Through this collaboration, I learned how to navigate differing stakeholder goals and align them toward a shared outcome: safer, more reliable rail transport containing a significant scientific contribution.
As a statistics teaching assistant for the second year computer science course, I supported students in mastering foundational statistical concepts essential to data science (from probability theory to descriptive statics, to statistical inference and hypothesis testing). My responsibilities included leading workgroup sessions, grading assignments, and providing detailed feedback to help students improve.
During this short time period I again resolved bugs in PL/SQL code and performed data mutations.
Worked in an agile team using Scrum methodology to support critical infrastructure and field operations. Key contributions included: - Debugged and optimized numerous PL/SQL scripts, ensuring data integrity and performance. - Executed data mutations to further ensure data integrity and support evolving business logic. - Developed user-focused dashboards that enabled field engineers to quickly locate and update key operational data, significantly reducing manual overhead. - Configured a secure reverse proxy with NGINX, integrating JWT-based authentication from a third-party provider to safeguard communications.
As a data structures student assistant for the second-year computer science course, I supported students in mastering abstract data types such as lists, stacks, queues, trees, and graphs. I provided both in-person guidance during workgroups and written feedback on programming assignments. My role focused on improving student understanding and performance while reducing the grading and support workload for the course lecturer.