Computational Biology

CNRS / École normale supérieure - PSL

cantini [at]

Laura CANTINI CNRS Research Scientist (Chargé de Recherches) at IBENS, specialized in multi-omics data integration in bulk and single-cell data

Short bio

CNRS permanent researcher at the Biology Institute of École Normale Supérieure (IBENS). Recipient of the ANR Young Researcher grant and the Sanofi iTech Awards in 2020. L’Oréal-UNESCO for Women in Science fellow (2018 edition).

Topics of interest

Single-cell omics data, multi-modal integration, network inference

Project in Prairie

Laura Cantini will develop computational methods for multi-modal single-cell data integration. She will in particular combine multi-omics joint dimensionality reduction, to identify the cell types and states present in a biological sample, and network-based methods to reconstruct the multi-omics regulatory mechanisms underlying each cell type/state. Finally, by applying the developed approaches to patient-derived data, she will contribute to improve our understanding of cancer heterogeneity and its underlying molecular mechanisms.


The timely detection and successful treatment of cancer depends on our ability to understand when, why, and how a subpopulation of cells deviates away from a healthy state or acquires drug resistance. Single-cell multi-modal data, produced at increasing peace, offer the opportunity to tackle these questions. The current major bottleneck is the crucial need for computational methods able to translate this wealth of information into actionable biological knowledge.


PhD Student

Sorbonne Université

rhassana96 [at]

Short bio

Master degree (Diplôme d’ingénieur) at Ecole Centrale de Lyon

Thesis title

Deep generative models for detection of anomalies in the brain.

Short abstract

Neuroimaging offers an unmatched description of the brain’s structure and physiology, however identifying subtle pathological changes simply by looking at images of the brain can be a difficult task. The aim of this PhD project is to develop innovative computational imaging tools to model abnormalities, defined as deviations from normal variability, from multimodal brain imaging. To that purpose, deep generative models will be used to generate pseudo-healthy images from real patients’ images for different modalities. Comparing pseudo-healthy and real images will provide individual maps of abnormalities.  

MASSON Jean-Baptiste

Statistical physics applied to biology

jean-baptiste.masson [at]

Jean-Baptiste Masson

Short bio

Principal investigator of the Decision and Bayesian computation laboratory, visiting scientist at Janelia research campus (2014-2019), visiting scientist at Institut Curie (2013), recipient of the StartULM (2018), C’nano innovation (2017) and Young researcher price of the French Biophysics Society (2009).

Topics of interest

Bayesian inference, neuroscience, biological decision-making, statistical physics and data processing in virtual reality

Project in Prairie

Jean-Baptiste Masson will focus on Bayesian induction, structured inference, physical environment modeling and statistical physics to probe learning in insect brains using neuronal, connectome and behavior imaging. He will organize every two years symposium on links between neuroscience, AI and physics; and a graduate class on structured inference.


How are insects able to perform complex probabilistic tasks by leveraging only small small neural networks, whereas machine-learning tasks often require large-scale architectures and extensive training on massive datasets? Evolution is able to shape decision-making in small neural circuits while maintaining high performance. By joining physical modelling, supervised and unsupervised structured inferences, Bayesian induction and numerical simulations, we can probe how evolution programmed robust decision making in the “brain“ of insects. In turn, we can extract key neural circuits from these insects and test their performance in real-world tasks.


Bioimage Informatics Computer Vision [at]

Thomas Walter

Short bio

Thomas Walter received his PhD from the Centre for Mathematical Morphology, Mines Paris-Tech. After 6 years of work at the EMBL Heidelberg, he joined the Centre for Computational Biology (CBIO, Mines ParisTech) in 2012. Since 2018, he is director of the CBIO and codirector of the department “Cancer and Genome: Bioinformatics, Biostatistics, Epidemiology of Complex Systems» (Institut Curie / Mines ParisTech / INSERM).

Topics of interest

Computer Vision, Bioimage Informatics, Histopathology, High Content Screening

Project in Prairie

Thomas Walter will work on problems in bioimage analysis. The main challenge he will address is to overcome massive annotations, which are often required for state-of-the-art computer vi-sion methods, but which seem unrealistic for many bioimaging projects. For this, he will investi-gate the experimental generation of ground truth data, weak supervision and image simulation. He will work on applications in fundamental cell biology, drug screening and histopathology.


Large-scale imaging approaches in biology and medicine are about to revolutionize basic life sciences and healthcare. Complementary to molecular approaches, they allow us to explore the spatial, morphological and multi-scale aspects of living systems. Artificial intelligence is the key technology today to transform this data deluge into knowledge. In this field, one of the major challenges for the next years is to overcome the need for massive annotation.