I'm an Erasmus Mundus Joint Master's student in Software Engineering for Green Deal (SE4GD), graduating in summer 2025. Passionate about technology that drives impact, I'm currently exploring opportunities in software engineering, with a keen interest in mobile-based development—but always open to new tech stacks!

• 🌱 Looking for graduate roles in software engineering (preferably mobile-based) but open to new challenges.
• 📱 Currently learning Kotlin to build Android-based projects, but I also have experience with Python, Java (Android), and Kotlin Multiplatform.
• 🎨 Love designing UI that makes apps functional, enjoyable, and intuitive for users.
• 🔍 Exploring innovative ways to bridge sustainability and technology.

Elisa-Kirja is a single-page application(SPA) built using Kotlin Multiplatform and Webassembly to build a POC. In the web app, you can see the books under different categories. The app also supports dark themes/light themes. You can find a medium article on Ktor-client integration fix here. This app was deployed using GitHub pages.

This app is built using Jetpack Compose for the UI, Room for local database management, Hilt for dependency injection, and Ktor for networking. It has features such as restaurant listing, adding restaurants to favorites, and handling different app states like loading and no internet connection.

This project is a system design using Service Oriented Architecture concepts utilizing UMl Diagrams. It tries to build a system fostering energy independence in local communities through knowledge sharing, electricity trading, consumption analysis, supporting communities & reducing reliance on the national grid. My focus is specifically on providing anonymized data to the researchers in the form of trends. It also details the essential endpoints, request and response data formats, and parameters wherever needed to interact with the API effectively.

This project is focused on the DingNet system which is a self-adaptive IoT exemplar designed to support research in adaptive and self-managing IoT applications. It utilizes a Q-learning-based strategy that dynamically adjusts transmission power based on real-time signal strength and packet loss, enabling the system to learn optimal actions through the reward mechanism used by Q-learning. It uses Python as a programming language, EnergiBridge to measure the energy consumption and ExperimentRunner to run the experiments.

• 💚 Love nature walks, yoga, traveling, and capturing endless photos.
• 🛠 Always curious about how technology can create meaningful change.

📩 Drop me a message on LinkedIn or reach out via email: [email protected].