About Me
Hi, I’m Laura, currently a research assistant at the University of Vienna. I have many professional interests, including sustainability, data visualization and storytelling and urban development that I try to bring together in my various activities. At the moment, my focus is completing my PhD in data science at the University of Vienna, but who knows what will come next?
Work Experience
Bringing together Data Science and the Environmental Sciences.
I am working as a predoctoral research assistant at the Research Platform The Challenges of Urban Futures at the University of Vienna. My doctoral research is carried out at the intersection of data science and environmental sciences, ranging from visualization in groundwater modelling to statistical analysis of pharmaceutical pollution data. My main research interest is explaining human influence on our environment using data that is available from a variety of sources.
Data Storage and Management System
Coordinated and led development of the internal data storage and management system for training data for various deep learning applications.
Deep Learning Versatile Platform
Coordinated development and internal sales of the Deep Learning Versatile Platform (DLVP) video-based traffic monitoring system.
Education
University of Vienna
PhD Data Science
2021 - 2025
Urban Futures Platform
University of Vienna
University Course "Cooperative Regional and Urban Planning"
2023 - 2025
Postgraduate Center
Publications
Towards a better understanding of sorption of persistent and mobile contaminants to activated carbon: Applying data analysis techniques with experimental datasets of limited size
Water Research
2024
Full Text
We demonstrate how tools such as distance correlation and clustering can be used effectively to identify the key parameters driving the sorption process.
The complex sorption mechanisms of carbon adsorbents for the diverse group of persistent, mobile, and potentially toxic substances (PMs or PMTs) present significant challenges in understanding and predicting adsorption behavior. While the development of quantitative predictive tools for adsorbent design often relies on extensive training data, there is a notable lack of experimental sorption data for PMs accompanied by detailed sorbent characterization. Rather than focusing on predictive tool development, this study aims to elucidate the underlying mechanisms of sorption by applying data analysis methods to a high-quality dataset. This dataset includes more than 60 isotherms for 22 PM candidates and well-characterized high-surface-area activated carbon (AC) materials. We demonstrate how tools such as distance correlation and clustering can be used effectively to identify the key parameters driving the sorption process. Using these approaches, we found that aromaticity, followed by hydrophobicity, are key sorbate descriptors for sorption, overshadowing steric and charge effects for a given sorbent. Aromatic PMs, although classified as mobile contaminants based on their sorption to soil, are well adsorbed by AC as engineered adsorbent via π-π interactions. Non-aromatic and especially anionic compounds show much greater variability in sorption. The influence of ionic strength and natural organic matter on adsorption was considered. Our approach will help in the analysis of solute-sorption systems and in the development of new adsorbents beyond the specific examples presented here.
The Challenge of Interdisciplinarity at the Intersection of Groundwater Management and Visualization Research
IEEE Computer Graphics and Applications
2023
Full Text
Still, outside the framework of a design study, our study was very informative and raised several interesting questions to be answered in future research.
This design study presents an analysis and abstraction of temporal and spatial data, and workflows in the domain of hydrogeology and the design and development of an interactive visualization prototype. Developed in close collaboration with a group of hydrogeological researchers, the interface supports them in data exploration, selection of data for their numerical model calibration, and communication of findings to their industry partners. We highlight both pitfalls and learnings of the iterative design and validation process and explore the role of rapid prototyping. Some of the main lessons were that the ability to see their own data changed the engagement of skeptical users dramatically and that interactive rapid prototyping tools are thus powerful to unlock the advantage of visual analysis for novice users. Further, we observed that the process itself helped the domain scientists understand the potential and challenges of their data more than the final interface prototype.
Side Projects
From Idea to Prototype in 3 Days.
With our idea “Verkehr findet Stadt” my team won second place in the Vienna ClimateChallenge Hackathon by the University of Vienna. We got the chance to present it at the Vienna Digital Days 2023 and discuss the project with the City of Vienna.
A Little More About Me
In my freetime I also have many different interests and hobbies. Some of them are
- Yoga (practicing and teaching)
- (Mountain) Hiking
- Reading