Projects and fellows

Giulia Muzio
ETH Zürich in Basel, Switzerland
PI: Karsten Borgwardt
Project 1: Machine Learning for Biological Network Analysis

Bowen Fan
ETH Zürich in Basel, Switzerland
PIs: Karsten Borgwardt and Caroline Uhler
Project 2: Machine Learning and Causal Inference to Optimize Genomic Interventions using Disease State Representations

Diane Duroux
University of Liege in Liege, Belgium
PI: Kristel Van Steen
Project 3: Comparison of heterogeneous or uncertain network structuresinal data

Lucas Miranda
Max Planck Institute of Psychiatry in Munich, Germany
PI: Bertram Müller-Myhsok
Project 4: Methods for subtype detection in high-dimensional data with a special focus on longitudinal data

Giovanni Visonà
Max Planck Institute for Intelligent Systems in Tübingen, Germany
PIs: Bernhard Schölkopf and Gabriele Schweikert
Project 5: Deep representations of somatic mutations and germline variants for cancer research

Rime Raissouni
Siemens Healthcare GmbH in Erlangen, Germany
PIs: Tobias Heimann and Volker Tresp
Project 6: Clinical decision support for precision medicine

Kadri Ligi
STACC Ltd in Tartu, Estonia
PIs: Jaak Vilo, Meelis Kull and Sven Laur
Project 8: Predicting patient trajectories and outcomes from national level data

Pelin Gündoğdu
Fundación Pública Andaluza Progreso y Salud in Sevilla, Spain
PI: Joaquin Dopazo
Project 9: Machine learning for the discovery of new functional and regulatory gene networks

Emese Sukei
Universidad Carlos III de Madrid in Madrid, Spain
PI: Antonio Artés
Project 10: Personalized health trajectories

Ndèye Maguette Mbaye
ARMINES/Mines ParisTech in Paris, France
PI: Chloé-Agathe Azencott
Project 11: Learning from multi-modal data to improve cancer treatment

Pradeep Eranti
Université de Paris in Paris, France
PI: Florence Demenais
Project 12: Integration of multi-omics data and disease-related phenotypes for better disease risk prediction

Christopher Heje Grønbech
Qlucore in Lund, Sweden
PIs: Magnus Fontes and Carl-Johan Ivarsson
Project 13: Visualisation of Deep Learning on Biomedical Data for Improved Interpretability

Vesna Resende Barros
Healthcare Informatics Department in IBM Research – Haifa, Israel
PIs: Michal Rosen-Zvi and Tal El-Hay
Project 14: Development and application of causal inference methods to high dimensional healthcare data