Ekaterina Antipushina

Supervisor: Yury Koush
Program years: 2024-2028
Topic: Multimodal data analysis for reliable prediction of localized neuronal activity in real-time
Research interests: Neuroimaging, generative modeling, fMRI analysis, machine learning

MSc, Skoltech
Research advisor: Maxim Sharaev
Program years: 2022-2024
Topic: Interpretable machine learning models for the analysis of multimodal neuroimaging and biomedical data using data fusion methods in the diagnosis of schizophrenia

Honors & Publications:

• Papers:
  • Interpretable AI models for predicting distant metastasis development based on genetic data: Kidney cancer example
• Posters:
  
  • Rest2Task: Image-to-Image model for task-based fMRI prediction, RnD day, Sber 
  • The application of machine learning methods for multimodal neuroimaging data in the diagnosis of schizophrenia