Dr Ymeti received a Bachelor's degree in Geography from the Faculty of History and Philology, University of Tirana, Albania (1999-2003). She completed the Master of Science (MSc) in Water Resources and Environmental Management at the Faculty of Geo-Information Science and Earth Observation (ITC), Enschede, The Netherlands (2005- 2007). The MSc project dealt with rainfall estimation by Remote Sensing for conceptual rainfall-runoff modelling in Ethiopia's Upper Blue Nile basin. In 2021, she received a doctoral degree in applied proximal sensing for soil science at the Faculty of Geo-Information Science and Earth Observation (ITC), the University of Twente, Enschede, The Netherlands. She investigated the seasonal effect on the soil surface stability using proximal sensing to understand better the soil aggregation dynamics as a precursor to soil erosion and degradation.

After her MSc graduation, she worked at the Institute of Geosciences (IGEO), the Polytechnic University of Tirana, from 2008 to 2013. Her work focused on implementing Geographic Information Systems and Remote Sensing technology for various applications. In 2021-2023, she rejoined the Institute of Geosciences, Polytechnic University of Tirana after obtaining her PhD. She was part of the Department of Meteorology. 

To further enhance her career, she pursued a post-doc at the Università Cattolica del Sacro Cuore, Italy (2023-2025). Using proximal/remote soil sensing and Machine Learning models to improve the accuracy of Soil Organic Carbon stock accounting in Northern Italy was the focus of her work.

In 2025, she returned to the Institute of Geosciences, joining the department of Seismology. Dr Ymeti's research interest is to extend the understanding of natural processes and their interactions in the environment, as well as their potential impact on society. Multi-source data integration, such as proximal/remote sensing and on-ground monitoring, in a Geographic InformationGIS) environment, allows for investigating natural hazards at an appropriate spatial and temporal scale. This approach is crucial for studying where and when seismic hazards (earthquakes) will occur and become hazardous.