Welcoming Two New Senior Scientists to dataTUdiscovery
As dataTUdiscovery, we are pleased to welcome our two new Senior Scientists, Dr. Catharina Hollauer and Dr. Christian Fellinger, to the team. Their expertise will strengthen dataTUdiscovery's mission to support researchers across TU Wien's disciplines and faculties in integrating Artificial Intelligence (AI) and Machine Learning (ML) into their scientific workflows.
As a cross-faculty Research+AI service center, dataTUdiscovery connects AI expertise with domain-specific research challenges in fields ranging from chemistry and physics to engineering and architecture.
Dr. Catharina Hollauer
Dr. Catharina Hollauer earned her PhD in Computational Methods and an M.S. in Operations Research from the Georgia Institute of Technology.
Her research focuses on computational methods for social and environmental good. She develops frameworks at the intersection of operations research, ML, and AI to support decision-making under uncertainty, with applications in industrial ecology and circular systems, low-carbon transitions and sustainable policy development, as well as the planning and design of resilient supply chains and business operations.
Prior to joining TU Wien, she spent nearly a decade leading enterprise digital transformation initiatives and developing advanced analytics and ML products across the IoT, healthcare, energy, and consumer goods sectors.
At dataTUdiscovery, she is excited to help establish a world-class, cross-faculty hub for advanced analytics and AI and to collaborate across disciplines to accelerate scientific discovery.
Dr. Christian Fellinger
Dr. Christian Fellinger recently earned his PhD in Pharmacy, with support from the Cheminformatics research group and the Institute of Computational Biological Chemistry, and an MSc in Chemistry from the University of Vienna.
His research focuses on combining computational chemistry and ML methods to better understand protein-ligand interactions. As part of his work, he develops computational models, scientific software, and reproducible workflows that help researchers effectively apply data-driven methods.
Throughout his research, he collaborated closely with experimental scientists and researchers from different disciplines, translating scientific questions into practical computational solutions. He particularly values the collaborative and interdisciplinary nature of this work and enjoys developing tools and workflows that are accessible and useful for others.
At dataTUdiscovery, he is excited to contribute to collaborative ML and AI projects across TU Wien and to support researchers in integrating AI methods into their scientific workflows. He is especially looking forward to engaging with researchers from different fields, learning about their scientific challenges, and developing solutions together.
Strengthening Research+AI at TU Wien
With the team's expansion, we ramp up our capabilities to build a sustainable Research+AI ecosystem at TU Wien. We warmly welcome our new colleagues and look forward to exciting collaborations and research projects ahead.