Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : AutoML (Horizon 2020, appel 2019, Proof of concept grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : University of Freiburg — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

Machine learning has become a key technology for modern data-driven industrial applications. This success is built on recent research advances in the field of artificial intelligence and more specifically was enabled by key advances in machine learning. Unfortunately, the performance of many machine learning methods is very sensitive to a myriad of design decisions and thus requires a significant amount of machine learning expertise which is often rare and makes this technology inaccessible for small and medium-sized companies that cannot afford their own team of machine learning experts. My ERC grant BeyondBlackbox on automated machine learning (AutoML) addresses this problem from a research perspective. In it, my team and I developed methods which systematically and efficiently adapt and tune machine learning pipelines and implemented them into a research prototype. This resulting research prototype, in principle, allows ML novices easy and affordable access to the most advanced ML methods, automatically customized for the user's own data, and with this research prototype, my team and I have won several competitions, including competitions against up to 130 teams of human ML experts. The potential economic impact is substantial since AutoML technology saves computational resources and human time and therefore reduces the cost of creating value from ML. In this POC project, I and my team will transform our existing research prototype to a professional prototype, perform a technical validation, perform market research and build up business contacts to evaluate this prototype in an industrial setting. Furthermore, we will develop a sustainable business model and assess ways of commercializing the advances made in my ERC grant in order to bring them to market.

Mots-clés : automated machine learning  automl,machine learning,data science,artificial intelligence,ease of use,efficiency,software,predictive modeling
Projet ERC : FUN2MODEL (Horizon 2020, appel 2018, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : University of Oxford — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2376236.36 €

Machine learning is revolutionising computer science and AI. Much of its success is due to deep neural networks, which have demonstrated outstanding performance in perception tasks such as image classification. Solutions based on deep learning are now being deployed in real-world systems, from virtual personal assistants to self-driving cars. Unfortunately, the black-box nature and instability of deep neural networks is raising concerns about the readiness of this technology. Efforts to address robustness of deep learning are emerging, but are limited to simple properties and function-based perception tasks that learn data associations. While perception is an essential feature of an artificial agent, achieving beneficial collaboration between human and artificial agents requires models of autonomy, inference, decision making, control and coordination that significantly go beyond perception. To address this challenge, this project will capitalise on recent breakthroughs by the PI and develop a model-based, probabilistic reasoning framework for autonomous agents with cognitive aspects, which supports reasoning about their decisions, agent interactions and inferences that capture cognitive information, in presence of uncertainty and partial observability. The objectives are to develop novel probabilistic verification and synthesis techniques to guarantee safety, robustness and fairness for complex decisions based on machine learning, formulate a comprehensive, compositional game-based modelling framework for reasoning about systems of autonomous agents and their interactions, and evaluate the techniques on a variety of case studies. Addressing these challenges will require a fundamental shift towards Bayesian methods, and development of new, scalable, techniques, which differ from conventional probabilistic verification. If successful, the project will result in major advances in the quest towards provably robust and beneficial AI.

Mots-clés : deep learning,multi agent systems,probabilistic verification,bayesian inference,temporal logic,stochastic games,equilibria
Projet ERC : MIRA (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Imperial College London — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499292.0 €

Machines capable of analysing and interpreting medical scans with super-human performance would transform healthcare as much as medical imaging itself did over the last century. With an increasing complexity and volume of data the interpretation of images and extraction of clinically useful information push human abilities to the limit. There is high risk that critical patterns of disease go undetected. We require powerful and trustworthy computational tools based on machine intelligence to support experts and go beyond human performance to tackle the major challenges in clinical practice. Two key ingredients are currently missing: 1) interpretable statistical representations that capture important information while reducing complexity; 2) intelligent algorithms that leverage knowledge across multiple tasks to solve the most challenging problems such as early detection of pathology. This project is devoted to redefine the state-of-the-art in medical image analysis by developing a new generation of machine intelligence using powerful techniques of representation learning. Key to the project is its unique access to some of the largest and most comprehensive imaging databases combined with world-leading expertise in machine learning and medical imaging. An overarching objective is to harvest information from population data to construct what will be the most advanced statistical models of anatomy. In contrast to previous attempts that focus primarily on specific organs or pathology, here shared representations are learned from highly complex data by jointly solving multiple tasks. Linking the representations with demographics, lifestyle, genetics and disease allows probing of genetic and environmental determinants related to specific anatomical and pathological phenotypes across organs. This will provide insights into complex diseases, and enables a novel approach to abnormality detection that aims to automatically find subtle signs of pathology in new medical scans.

Mots-clés : medical image analysis
Projet ERC : MADEFUN (Horizon 2020, appel 2018, Consolidator grants)
Domaine scientifique : Life Science
Panel : Immunity, Infection and Immunotherapy
Entité participante : University of Zurich — Pays : Suisse — Rôle : PI
Acronyme de l'entité : UZH
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1995500.0 €

Macrophages are part of the mononuclear phagocyte system and play critical roles in innate immune responses. They further exert crucial organ-specific functions to warrant tissue homeostasis. Most macrophages are long-lived and derive from embryonic precursors. The central nervous system (CNS) contains several macrophage populations, of which microglia are the most prominent and abundant population residing in the CNS parenchyma. Besides being implicated in CNS pathologies, microglia guide neuronal development, are critical for neurogenesis and contribute to the maintenance of tissue homeostasis. At the interface between the brain and the periphery reside choroid plexus, perivascular and meningeal macrophages (‘border-associated’ macrophages, BAMs). While they mostly also originate from embryonic precursors, the differentiation cues and their specific roles in development and in health and disease are poorly understood. Here we will delineate the spatiotemporal emergence of BAMs and microglia, the impact of the local microenvironment on their phenotype and function, and their role in brain development. We will further analyze the role of BAMs and microglia in physiological and neuropathological conditions (neurodegeneration, CNS infection and inflammation). We have recently described a novel ‘microglia-specific’ conditional mouse and we are currently establishing an inducible system to specifically manipulate BAMs. We will use these to genetically intervene in different cytokine signaling pathways in combination with fate-mapping in vivo. The proposed research project will provide a deeper insight into microglia and BAM biology and their functional specializations at different stages of life.

Mots-clés : macrophage,microglia,brain,development,function
Projet ERC : MajoranaTopIn (Horizon 2020, appel 2016, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Condensed matter physics
Entité participante : University of Cologne — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2406250.0 €

Majorana fermions were recently discovered in topological superconductors as exotic quasiparticles having the curious property of being their own antiparticles. They are not only interesting as novel relativistic quasiparticles, but are also useful for realizing fault-tolerant quantum computers. However, currently available platforms to materialize Majorana fermions are limited, and the existing platforms have respective drawbacks for actually building qubits for a scalable quantum computer. Also, various unusual properties are predicted for Majorana fermions, but few have been experimentally addressed. To make a leap in the Majorana-fermion research which is technically highly demanding, one needs to grow state-of-the-art materials and tightly combine them with mesoscopic device research. By performing such an integrated research efforts in the same laboratory, this project aims to explore new platforms for Majorana qubits and to establish new methodologies to address peculiar physics of Majorana fermions. As new platforms, we pursue (i) three-dimensional topological-insulator nanoribbons and (ii) ferromagnetic topological-insulator thin films, both of which will be proximity-coupled to an s-wave superconductor. Each of them allows for conceiving Majorana qubits based on different principles, which will be tested in this project. Also, by developing new methodologies, we will elucidate (i) non-Abelian statistics probed by interferometry and (ii) quantized/universal heat transport phenomena probed by thermal conductance. These works will be complemented by materials growth efforts involving molecular beam epitaxy and detailed characterizations of the local electronic states using scanning tunnelling spectroscopy. If successful, this project will not only contribute to the realization of scalable quantum computers, but also elucidate the non-Abelian statistics, which is a fundamentally new property of particles and is ground breaking in physics.

Mots-clés : topological insulators
Projet ERC : MultiSeroSurv (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : Institut Pasteur de Dakar — Pays : Sénégal — Rôle : Partner
Type d'entité : Recherche
Financement ERC de l'entité : 325500.0 €

Malaria and neglected tropical diseases (NTDs) such as lymphatic filariasis, onchocerciasis, trachoma and schistosomiasis affect almost 2 billion people every year. Coordinated targeting of these diseases will aid future control and elimination campaigns, but this will require integrated surveillance strategies. Malaria surveillance is routinely implemented through cross-sectional surveys where blood samples are tested for parasites. A major barrier to routine NTD surveillance is the range of samples required for parasite diagnosis: stool, urine, blood, eye swabs and skin snips. Instead of direct detection of parasites, there is an opportunity to implement serological surveillance by measuring antibodies to multiple pathogens in blood samples collected for malaria surveillance. This proposal has four aims: (i) Characterise the sero-epidemiology of malaria and NTDs in longitudinal cohort studies in Senegal, Ethiopia, Cambodia and Papua New Guinea; (ii) Measure antibodies to 34 antigens from 12 pathogens from single blood samples; (iii) Model antibody kinetics and validate the use of serology for detecting infections; and (iv) Demonstrate how a population’s concurrent and past exposure to multiple parasites can be estimated by analysing multiplex data from cross-sectional surveys. These aims will be achieved through innovative epidemiological studies, new technologies, and especially developed analytic methods, including: (1) utilisation of multiplex bead-based Luminex assays; (2) mixed-effects models in a Bayesian framework with data augmentation to identify serologically suspected infections, with validation against confirmed infections; and (3) statistical algorithms for reconstructing long-term and recent transmission trends. This interdisciplinary project will undertake fundamental research in analytic methods for processing complex multiplex data and converting it to the actionable information needed for integrated serological surveillance of malaria and NTDs.

Mots-clés : malaria,neglected tropical diseases,serology,statistical modelling,mathematical modelling,surveillance,trachoma,onchocerciasis,lymphatic filariasis,schistosomiasis
Projet ERC : SexAntag (Horizon 2020, appel 2016, Starting grants)
Domaine scientifique : Life Science
Panel : Environmental Biology, Ecology and Evolution
Entité participante : Institute of Science and Technology Austria — Pays : Autriche — Rôle : PI
Acronyme de l'entité : ISTA
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1444394.0 €

Males and females display profound differences in phenotype, physiology and behavior, and understanding the evolutionary forces driving this diversity is a long-standing goal in evolutionary biology. Sexually antagonistic conflict, resulting from traits and/or mutations that are beneficial to one sex but harmful to the other, can in theory lead to strong dimorphism, and has been invoked to explain many evolutionary features, including the large number of genes that acquire sex-biased expression. Quantifying it, however, has proved difficult, and only a few experimental studies have attempted to systematically identify genes under sexual conflict. Species groups with both sexual and asexual populations offer a promising approach to tackle this question, as they allow for a direct comparison of gene expression when selection occurs ½ of the time in females and ½ of the time in males (in sexual species), versus a female-only selective regime (asexual species). Here, we will test the hypothesis that widespread sexual conflict maintains sex-biased gene expression, using the brine shrimp Artemia as a model. Specifically, we will: 1. Compare gene expression levels of closely related Artemia sexual and asexual species, to test if the absence of selection on males (in the female-only species) leads to general shifts in expression, and if these changes are consistent with predictions made under sexual antagonism. 2. Characterize the Z (sex) chromosome of the sexual and asexual species, to test if this chromosome is particularly prone to maintaining genes under sexual conflict, as predicted by theory. 3. Investigate the population genomics profile of genes under sexual conflict, to find signatures of sexual conflict that may be diagnostic for sexually antagonistic genes in other systems. Together, these analyses will provide us with a global overview of the prevalence of sexual antagonism, and of its influence on gene expression and genome evolution.

Mots-clés : sexual conflict,genomics,population genetics,transcriptomics
Projet ERC : C-CLEAR (Horizon 2020, appel 2017, Advanced grants)
Domaine scientifique : Life Science
Panel : Molecules of Life: Biological Mechanisms, Structures and Functions
Entité participante : Utrecht University — Pays : Pays-Bas — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2332500.0 €

Mammalian complement recognizes a variety of cell-surface danger and damage signals to clear invading microbes and injured host cells, while protecting healthy host cells. Improper complement responses contribute to diverse pathologies, ranging from bacterial infections up to paralyzing Guillain-Barré syndrome and schizophrenia. What determines the balance between complement attack reactions and host-cell defense measures and, thus, what drives cell fate is unclear. My lab has a long-standing track record in elucidating molecular mechanisms underlying key complement reactions. We have revealed, for example, how the interplay between assembly and proteolysis of these large multi-domain protein complexes achieves elementary regulatory functions, such as localization, amplification and inhibition, in the central (so-called alternative) pathway of complement. Results from my lab underpin research programs for the development of novel therapeutic approaches in academia and industry. Here the goal is to understand how the molecular mechanisms of complement attack and defense on cell membranes determine clearance of a cell. Enabled by new mechanistic insights and preliminary data we can now address both long-standing and novel questions. In particular, we will address the role of membrane organization and dynamics in complement attack and defense. Facilitated by recent technological developments, we will combine crystallography, cryo-EM, cryo-ET and high-resolution microscopy to resolve complement complex formations and reactions on membranes. Thus, this project aims to provide an integrative understanding of the molecular complement mechanisms that determine cell fate. Results will likely be of immediate importance for novel therapeutic approaches for a range of complement-related diseases. Furthermore, it will provide clarity into the general, and possibly fundamental, role of complement in tissue maintenance in mammals.

Mots-clés : complement,membrane dynamics,membrane proteins,structural biology,immunology
Projet ERC : AVIAN MIND (Horizon 2020, appel 2020, Advanced grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
Entité participante : Ruhr University Bochum — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : RUB
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2226477.0 €

Mammals and birds evolved vastly different forebrains. While mammals have a cortex and can reach large brain weights, bird brains are very small and are constituted by seemingly homogeneous nuclear clusters. These glaring anatomical differences cast a dim prospect on avian cognition. However, the last years showed that several avian taxa are cognitively on par with apes and show abilities like planning, theory-of-mind, and mirror-self-recognition. How is that possible? Our inability to answer this question yet, shows that we still are far away from properly understanding the link between brain structure and cognitive functions in generic ways. To gain deeper insights into these issues, we should dare to study in depth non-standard model animals. I therefore propose to conduct a detailed neurocognitive analysis of executive cognition, memory, and consciousness in pigeons. I hypothesise that despite major macroscopic differences, the pallial cognitive network of birds is highly similar to that of mammals while their memory systems differ. To test this hypothesis, I will combine behavioural analyses, fMRI, single unit recording, and optogenetics to characterize the neural mechanisms of cognition, consciousness, and memory in pigeons. I aim to: 1) reveal the pallial network for the three executive functions: inhibition, cognitive flexibility, and working memory and will subsequently search for an avian equivalent of the default mode and executive network. 2) identify an avian pallial associative area for movement intentions. 3) examine if avian associative neurons code high dimensional event representations. 4) test if birds have evolved a non-spatial memory system that is independent of hippocampus. 5) and investigate if pigeons show signatures of consciousness akin to primates. These studies will serve a deeper understanding of the link between neural structure and cognition – a link that might be less bound to neuroanatomical specificities than hitherto assumed.

Mots-clés : evolution of cognition,comparative neuroscience,avian cognition,neuroanatomy,executive network,executive function s,motor function,default mode network,neural networks
Projet ERC : CLOCK (Horizon 2020, appel 2015, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Social World and Its Diversity
Entité participante : Basque Centre for Climate Change — Pays : Espagne — Rôle : Partner
Acronyme de l'entité : BERC
Type d'entité : Recherche
Financement ERC de l'entité : 135364.0 €

Management of marine fisheries is still far from incorporating adaptation to climate change, even though global stocks are heavily overexploited and climate change is adding additional pressure to the resource. In fact, there is growing evidence that current fisheries management systems may no longer be effective under climate change, and this will translate into both ecological and socioeconomic impacts. This research project argues that the combination of fisheries management science and socio-ecological systems thinking is necessary in order to advance in fisheries adaptation to climate change. To this end, the main objectives are set to: 1) Identify and understand the new challenges raised by climate change for current sustainable fisheries management; 2) Develop a novel approach to fisheries adaptation within a socio-ecological framework; 3) Provide empirical evidence on potential solutions for the adaptation of fisheries management systems; and 4) Help introduce fisheries adaptation at the top of the regional and international adaptation policy agendas. To do this, I will combine model and simulation approaches to fisheries with specific case studies where both biophysical and economic variables will be studied an modelled, but also individuals will be given the opportunity to participate in an active way, learning from participatory methods their preferences towards adaptation and the consequences of the new scenarios climate change poses. Three potential case studies are identified for property rights over stocks, property rights over space, and Marine Reserves in two European and one international case study areas. As a result, I expect to develop a new Adaptation Framework for fisheries management that can be scalable, transferable and easily operationalized, and a set of case study examples on how to integrate theory and participatory processes with the aim of increasing social, ecological and institutional resilience to climate change.

Mots-clés : adaptation,climate change,fisheries,marine reserves,property rights,resilience