AIOLOS Project (Artificial Intelligence Tools for Outbreak Detection and Response)

The goal of the project is to develop a digital platform to allow for early detection of new respiratory pathogens epidemics, monitor their spread, and inform decision-makers on appropriate countermeasures.

My task was to perform opinion mining on German tweets to monitor public perception of measures taken by the government in the context of COVID-19 nonpharmaceutical interventions (NPIs).

Thesis Title : Disease-Symptom Relation Extraction from Biomedical Textual Content

The main purpose of this research was to extract relations between diseases and their corresponding symptoms from PubMed abstracts. To achieve this, we created a novel dataset by using various active learning (AL) methods which is a form of semi-supervised learning. After the construction of the dataset, we fine-tuned BERT based models which are pre-trained on the biomedical text.

Biotechnology with Focus on Laboratory Automation

• Programmed a pipetting robot to implement enzyme activity assay experiments
• Setted up half-throughput fluorescence assays for enzyme activity screening
• Implemented a Python-based data analysis and visualization pipeline