publications & talks

The next generation of galaxy surveys has the potential to significantly deepen our understanding of the Universe, though this potential hinges on our ability to rigorously address systematic uncertainties. This was previously beyond reach in field-based implicit likelihood cosmological inference frameworks. We aim at inferring the initial matter power spectrum after recombination to diagnose a variety of systematic effects in galaxy surveys prior to inferring the cosmological parameters. Our approach is built upon a two-step framework. First, we employ the SELFI algorithm to infer the initial matter power spectrum, which we utilise to comprehensively investigate and disentangle how systematic effects influence the power spectrum reconstruction, using a single set of N-body simulations. Second, we obtain posterior cosmological parameters via implicit likelihood inference, recycling the simulations from the first step for data compression. We rely on a model of large-scale spectroscopic galaxy surveys that incorporates fully non-linear gravitational evolution and simulates multiple systematic effects typically encountered in astrophysical surveys. We demonstrate along with a practical guide how the SELFI posterior can be utilised to thoroughly assess the impact of misspecified linear galaxy bias parameters, selection functions, survey masks and inaccurate redshifts on the initial power spectrum after recombination. We show that a subtly misspecified model can lead to a bias greater than 2sigma in the (Omega_m,sigma8) plane, which we are able to detect and avoid using SELFI prior to inferring the cosmological parameters. This framework has the potential to significantly enhance the robustness of physical information extraction from full-forward models of large-scale galaxy surveys such as DESI, Euclid, and LSST.

We present methodological advances to perform implicit likelihood inference in cosmology from arbitrarily complex probabilistic forward models of cosmological surveys.

We present methodological advances to perform implicit likelihood inference of cosmology from arbitrarily complex models of cosmological surveys, while efficiently and extensively checking for systematics. This novel approach makes it possible to fully utilise our prior theoretical understanding of the initial matter power spectrum after inflation, in order to investigate the effects of known sources of systematics at play in the data generating process.

We present methodological advances to perform implicit likelihood inference of cosmology from arbitrarily complex models of galaxy surveys, while efficiently checking for systematics. This novel approach makes it possible to fully utilise our prior theoretical understanding of the initial matter power spectrum, in order to investigate the effects of known sources of systematics at play in the complex data generating process. It is currently being used for Additional Galaxy Clustering probes in preparation for the first Euclid data release.

We present methodological advances to perform implicit likelihood inference of cosmology from any forward model of galaxy surveys, while efficiently checking for systematics. The approach is based on a two-steps framework, and does not require any inner knowledge of the forward data model. First, we use SELFI (Simulator expansion for likelihood-free inference) to infer the initial matter power spectrum from any probe, and we use it to check whether all systematics are correctly accounted for based on qualitative and quantitative criteria. Second, cosmological parameters are inferred using implicit likelihood inference. Simulations used in the first step are recycled for optimal data compression, which is required for the second step. We show that mis-modelled systematic effects that would result in a biased posterior are unambiguously detected before performing the inference of cosmological parameters. The method is currently being used for Additional Galaxy Clustering probes in preparation for the first Euclid data release.

Differences in cells’ functions arise from differential activity of regulatory elements, including enhancers. Enhancers are cis-regulatory elements that cooperate with promoters through transcription factors (TF) to activate the expression of one or several genes by getting physically close to them in the 3D space of the nucleus. There is increasing evidence that genetic variants associated with common diseases are enriched in enhancers active in cell types relevant to these diseases. Identifying the enhancers associated with genes and conversely, the sets of genes activated by each enhancer (the so-called enhancer/gene or E/G relationships) across cell types, can help understanding the genetic mechanisms underlying human diseases. There are three broad approaches for the genome-wide identification of E/G relationships in a cell type: (1) genetic link methods or eQTL, (2) functional link methods based on 1D functional data such as open chromatin, histone mark or gene expression and (3) spatial link methods based on 3D data such as HiC. Since (1) and (3) are costly, the current strategy is to develop functional link methods and to use data from (1) and (3) as reference to evaluate them. However, there is still no consensus on the best functional link method to date, and method comparison remain seldom. Here, we compared the relative performances of three recent methods for the identification of18 enhancer-gene links, TargetFinder, Average-Rank, and the ABC model, using the three19 latest benchmarks from the field: a reference that combines 3D and eQTL data, called BENGI,20 and two genetic screening references, called CRiFF and CRiSPRi. Overall, none of the three21 methods performed best on the three references. CRiFF and CRISPRi reference sets are likely22 more reliable, but CRiFF is not genome-wide and CRiFF and CRISPRi are mostly available on23 the K562 cancer cell line. The BENGI reference set is genome-wide but likely contains many false24 positives. This study therefore calls for new reliable and genome-wide E/G reference data rather25 than new functional link E/G identification methods.

To build an augmented view of an organ during surgery, it is essential to have a biomechanical model with appropriate material parameters and boundary conditions, able to match patient specific properties. Adaptation to the patient’s anatomy is obtained by exploiting the image-rich context specific to our application domain. While information about the organ shape, for instance, can be obtained preoperatively, other patient-specific parameters can only be determined intraoperatively. To this end, we are developing data-driven simulations, which exploit information extracted from a stream of medical images. Such simulations need to run in realtime. To this end we have developed dedicated numerical methods, which allow for real-time computation of finite element simulations.