Marianna Rapsomaniki


Marianna Rapsomaniki




Staff Research Scientist


IBM Research Europe - Zurich Zurich, Switzerland


Marianna is leading the AI for single-cell research team at IBM Research Europe in Zurich. The overarching goal of her team is modeling spatiotemporal tumor heterogeneity across different scales of biological organization, and understanding how it affects cancer initiation, progression, and response to treatment. To achieve that, her team combines machine learning and deep learning approaches to develop computational methods able to extract biologically meaningful patterns from large-scale, heterogeneous, and noisy single-cell data, with or without spatial resolution. Marianna holds a Diploma in Computer Science and Engineering and a Master's in Bioinformatics, both from the University of Patras, Greece. Her PhD research was carried out working jointly in the Cell Cycle lab of the University of Patras and the Automatic Control Lab of ETH Zurich and involved stochastic hybrid modeling of biological systems, with applications in protein mobility within the nucleus, DNA re-replication, and parameter inference from live-cell imaging data. Her research has been supported by a Swiss Government Excellence Scholarship, the State Scholarships Foundation of Greece, and the Swiss National Science Foundation (SNSF).  

Check our team's Publications, and collection of open-source tools!


  • Our team's paper ChromFormer: A transformer-based model for 3D genome structure prediction selected as a spotlight talk at the Learning Meaningful Representations of Life workshop in NeurIPS - watch
  • Presentation of our team’s work in Applied Machine Learning Days (AMLD) - watch
  • Our team was awarded an SNSF Sinergia grant PROMETEX (collab with the Kruithof-de Julio Urogenus lab, University of Bern, and the Alexandrov Spatial Metabolomics lab, EMBL).
  • Read about our latest work on deciphering breast cancer heterogeneity here, or watch the related video from ReutersTV.





AI Methods for Precision Therapies

Developing AI methods for heterogeneity-aware precision therapies.

Modeling Spatial Heterogeneity of the Tumor Microenvironment

Combining spatial single-cell omics with AI to model the complexity of the tumor micorenvironment and enable novel spatial biomarker discovery.

Modeling the 4D Genome

Modeling the 4D genome with deep learning and stochastic simulations.


A novel computational method to quantify cell cycles and cell volume variability

Quantifying Biological Heterogeneity from Single-cell Data

Understanding, modeling and quantifying different sources of heterogeneity from single-cell measurements.

AI for Single-cell Research

Understanding spatiotemporal heterogeneity across different scales of biological organization.

Lab that Learns

Leveraging AI foundation models and multi-cloud computing to usher in a new era of reproducible and collaborative experimentation for scientific discovery.

AI for Scientific Discovery

Creating the AI-enabled lab for a new era of reproducible and collaborative experimentation

Top collaborators

Jannis Born

Jannis Born

Research Scientist in AI for Scientific Discovery
Sergiy Zhuk

Sergiy Zhuk

Senior Research Scientist and Manager, Optimization & Quantum Computing
Gavin Jones

Gavin Jones

Research Manager - Quantum Computational Science