Haani Jessin

Master thesis student in the OS Kernel Lab at Huawei Dresden Research Center.

Developed a full-stack multi-platform desktop application using ReactJS, Python, and Rust for battery and power management through SMBus and CANBus. Configured microcontroller evaluation kits for performance testing and development. Automated build processes for embedded software using Python, improving deployment efficiency. Developed Python tools for automated testing and reporting, analyzing long-term battery storage performance.

Working Student on the SmartER Affiliations project. Contributed to improving the quality of institutional affiliation metadata in academic publications. Worked on labeling, verifying, and disambiguating organization names to align them with standardized identifiers such as ROR and Wikidata. This work supported the development of clean, structured data for integration into open scholarly knowledge infrastructures.

Worked as a Software Engineer in the AI Team at Applab Qatar. Developed end-to-end chatbot solutions using MS Copilot Studio and Azure OpenAI GPT, integrating MySQL and CosmosDB for RAG workflows. Built scalable data pipelines with Python on Azure Serverless Functions, enabling seamless AI Search integration. Automated CI/CD pipelines with Azure DevOps and ensured real-time data ingestion with Azure Functions. Documented deployment processes, including monitoring setups with Power BI.

IS/IT Assistant at FIFA World Cup Qatar 2022 under Host Broadcasting Services. Deployed 2500+ phones & 700 laptops for international staff using Microsoft Autopilot and Android for Work. Provided IT support at the International Broadcast Centre (IBC) and stadiums. Developed mobile apps using Flutter (Firebase backend) for transportation management and Microsoft PowerApps for attendance tracking, improving efficiency for 700+ event staff.

Worked on a data-driven application to recommend physical activities based on wearable data. Benchmarked kNN distance metrics for sleep quality prediction, exploring advanced optimal transport techniques. Integrated the recommendation system with an interactive Vue.js front-end and Flask backend. Deployed the web app on QCRI servers using Docker containers.