PDP Villoutreix lab
Dynamic multi-domain translation for
high-throughput sequencing data
Multi-domain translation is a problem of major interest in biology. When studying a biological system such as a developing embryo, many acquisition techniques are available. Each of them brings out unique features of the system, however, they are often incompatible and cannot be performed at the same time. To overcome this challenge we need to develop multidomain integration techniques. Current approaches rely either on the tools of optimal transport, or multiple autoencoders, however, they are not designed to address temporal data. With this project, we propose to take advantage of multi-domain dynamical data in high-dimensional spaces to infer a dynamical coupling between sequencing data acquisition techniques (such as sc-RNASeq) and microscopy data. This will include theoretical work and computational experiments on artificial and real data. The results of the project are expected to have large impact in the machine learning community and be of wide applicability in real world biological problems. The scientific environment for this project is ideal as it combines expertise in interdisciplinary approaches of machine learning applied to biological data, and expertise
in theoretical machine learning.
Optimal Transport, Autoencoders, Single Cell Transcriptomics
CENTURI offers a two-year contract from the University of Aix-Marseille. The salary will be based on Aix-Marseille University’s salary scale, depending on the candidate’s profile and experience.
If you are interested, we encourage you to apply by sending your application to Paul Villoutreix (firstname.lastname@example.org).
Applications must include:
- A CV
- A cover letter
- Two references we can contact
Deadline : until the position is filled
- Towards a general framework for spatio-temporal transcriptomics
Julie Pinol, Thierry Artières, Paul Villoutreix, NeurIPS, LMRL workshop, 2020
- Gene expression cartography Nitzan, Mor, et al., Nature, 2019
Multi-domain translation by learning uncoupled autoencoders Karren D Yang, Caroline Uhler, Arxiv, 2019
- Multi-domain translation by learning uncoupled autoencoders
Karren D Yang, Caroline Uhler, Arxiv, 2019