معرفی
Dr. Olga Zolotareva is a Group Leader at the University of Hamburg's Faculty of Mathematics, Informatics and Natural Sciences, within the Department of Computational Systems Biology. She holds a postdoctoral position at TU Munich's Chair of Experimental Bioinformatics, focusing on federated machine learning and proteomics analysis in Alzheimer's disease. Her research integrates multi-omics data, drug repurposing strategies, and privacy-preserving computational methods across institutions.
Education and Training: She is completing her PhD at Bielefeld University's DiDy Research Training Group under Professors Ralf Hofestaedt and Martin Ester (Simon Fraser University, Canada). Her doctoral work developed a network-constrained biclustering method applied to breast cancer expression data. In 2018, she conducted visiting research at Simon Fraser University, contributing to multi-omics integration for drug response prediction.
Research Interests: Her work bridges computational biology and federated learning, emphasizing privacy-aware tools for collaborative biomedical research. Key areas include:
- Federated learning frameworks for omics data analysis
- Drug repurposing via heterogeneous molecular networks
- Unsupervised patient stratification using biclustering
- Privacy-preserving differential expression analysis
Publications Highlight Trends: Recent work emphasizes federated learning applications (e.g., FedRBE, FedProt), drug discovery tools (NeDRex-Web, ProHarMeD), and precision medicine strategies. Her articles span proteomics normalization, oncology biomarkers, and AI-driven biomedical collaboration.
Grants and Collaborations: She contributes to projects like CLINSPECT-M and FeatureCloud, advancing decentralized healthcare analytics. Her tools like Flimma and DESMOND 2.0 exemplify methodological innovations in distributed biomedical research.
Labs and Teams: Part of the Experimental Bioinformatics group at TU Munich and the Computational Systems Biology unit at Hamburg, she collaborates internationally on federated learning and omics integration initiatives.
