Tobias Olenyi

Tobias Olenyi is a researcher at the Department of Informatics, Technical University of Munich (TUM), working under the Chair for Bioinformatics led by Prof. Dr. Burkhard Rost. Holding an M.Sc. degree, he contributes to computational biology research with focus on protein structure analysis and machine learning applications. His office is located at Boltzmannstr. 3, 85748 Garching bei München (Room 5609.01.055) with office hours Tuesday and Wednesday 10:00-17:00.

Education:

• M.Sc. in relevant computational/biological field

Research Interests:

Olenyi's work centers on bioinformatics and computational biology, specifically protein structure prediction, visualization, and functional analysis. He leverages deep learning (protein language models, embeddings) and structural bioinformatics to develop tools for protein space visualization (FlatProt, ProtSpace), transmembrane protein analysis (TMVisDB), and phenotype prediction (LambdaPP). His research bridges computational methods with biological applications, emphasizing accessibility through interactive platforms and crowdsourced data collection.

Publication Trends:

From 2018-2025, Olenyi's work shows a clear trajectory toward protein-centric machine learning. Early projects focused on crowdsourced data platforms (VoiLA, 2018; gamified collection, 2019), while 2021-2022 publications established his expertise in protein embeddings for variant effect prediction and Gene Ontology annotation. His 2025 output demonstrates maturation into specialized visualization tools (FlatProt, TMVisDB), reflecting a cohesive research program that integrates AlphaFold2 structures and deep learning to solve protein analysis challenges.

Laboratory and Team Affiliation:

Olenyi operates within the Rost Lab at TUM—a globally recognized bioinformatics group specializing in protein structure prediction. The lab maintains strong industry/academic collaborations and emphasizes open-source tool development (e.g., PredictProtein server). His work directly supports the chair's mission to translate computational advances into biological insights, particularly in protein function and disease mechanisms.