- Systems Biology
- Oncology
- Computational Biology
- +۵ مورد دیگر
Federica Eduati is an Associate Professor in Systems Biology for Oncology at the Department of Biomedical Engineering at Eindhoven University of Technology (TU/e). She leads a research group focused on developing interpretable computational models to understand differential patient responses to cancer therapies and create personalized treatment strategies. Her work integrates multi-modal data including omics, imaging, and microfluidics-generated experimental data to study tumor-immune interactions and signaling dynamics. Dr. Eduati holds a BSc in Biomedical Engineering (2006) and MSc in Bioengineering (2008) from the University of Padova, Italy (both cum laude), and completed her PhD in Systems Biology in 2013. Her postdoctoral training included an EMBL EIPOD Marie Curie fellowship split between EMBL-EBI (Cambridge) and EMBL-HD (Heidelberg). Her research focuses on: Developing multiscale spatial models of tumor microenvironments Creating patient-specific models for precision oncology Identifying predictive biomarkers for immunotherapy response Integrating computational modeling with microfluidics technology Decoding tumor-immune cell interactions through spatial omics Her publications demonstrate strong emphasis on computational oncology approaches including spatial modeling of tumor microenvironments, machine learning for biomarker discovery, integration of multi-modal data sources, and developing personalized therapy prediction systems. Recent work focuses on agent-based modeling, radiogenomics, and interpretable AI for clinical applications. Scientific awards and recognitions: Marie Curie Fellowship (EMBL EIPOD) Co-organizer of NIEHS-NCATS-UNC DREAM Toxicogenetics Challenge (2013) Multiple research outputs featured in press/media including expert commentaries on prostate cancer research and personalized cancer therapy She leads the Computational Biology group at TU/e and collaborates extensively with international research teams. Her lab develops advanced droplet microfluidics platforms for high-throughput drug screening and utilizes public data repositories for model validation.
