Education

I hold a B.Sc. in Biology (major Microbiology and Genetics), an M.Sc. in Computational Science, and a Ph.D. in Life Sciences (Biology) from the University of Vienna. My doctoral research focused on machine learning in high-dimensional spaces, and properties of the protein sequence space.

Professional experience

From 2021 to 2025, I worked as Senior Data Scientist at Proxygen GmbH, where I designed and developed deep learning methods for high-throughput drug screening. I engineered large-scale multiplexed screens for molecular glue degraders in close collaboration with wet-lab and dry-lab teams.

Research

Prior to that, I was a pre-doc at the Austrian Research Institute for Artificial Intelligence (OFAI) and at the Centre for Microbiology and Environmental Systems Science at the University of Vienna (2015–2021), where I investigated high-dimensional data, hubness reduction, and the protein sequence space. I developed contrastive learning approaches for protein similarity search, and created open-source software including scikit-hubness and DeepNOG.

My research focuses on developing machine learning methods for complex biological problems, particularly in high-dimensional spaces. I am a contributor to major open-source projects including SciPy and scikit-learn.

Please visit my Google Scholar profile for a complete list of my publications. You can also find me on ORCID or explore my open-source projects on at GitHub.

Awards and achievements

• University of Vienna: Performance scholarship (4 times between 2011-2016)
• University of Vienna: Participation in “NaturTalente” high potential program (2014)
• Springer: BestMaster program (2015)

Memberships

• Austrian Neuroscience Association (ANA), since 2013–2024
• Austrian Bioinformatics Platform (ATBI), since 2018
• Institute of Electrical and Electronics Engineers (IEEE), 2018–2021