ERC FUNDED PROJECTS

The scientific goal of the proposal is to answer central questions related to diffeomorphism groups of manifolds of dimension 2 and 3, and to their deformation invariant analogs, the mapping class groups. While the classification of surfaces has been known for more than a century, their automorphism groups have yet to be fully understood. Even less is known about diffeomorphisms of 3-manifolds despite much interest, and the objects here have only been classified recently, by the breakthrough work of Perelman on the Poincar\'e and geometrization conjectures. In dimension 2, I will focus on the relationship between mapping class groups and topological conformal field theories, with applications to Hochschild homology. In dimension 3, I propose to compute the stable homology of classifying spaces of diffeomorphism groups and mapping class groups, as well as study the homotopy type of the space of diffeomorphisms. I propose moreover to establish homological stability theorems in the wider context of automorphism groups and more general families of groups. The project combines breakthrough methods from homotopy theory with methods from differential and geometric topology. The research team will consist of 3 PhD students, and 4 postdocs, which I will lead.

724 992 €

Start date: 2009-11-01, End date: 2014-10-31

By 2040, all major sectors of the European economy will be deeply digitalized. By then, the EU aims at reducing greenhouse gas emissions by 60% with respect to 1990 levels. Digitalization will affect decarbonization efforts because of its impacts on energy demand, employment, competitiveness, trade patterns and its distributional, behavioural and ethical implications. Yet, the policy debates around these two transformations are largely disjoint. The aim of the 2D4D project is ensure that the digital revolution acts as an enabler – and not as a barrier – for decarbonization. The project quantifies the decarbonization implications of three disruptive digitalization technologies in hard-to-decarbonize sectors: (1) Additive Manufacturing in industry, (2) Mobility-as-a-Service in transportation, and (3) Artificial Intelligence in buildings. The first objective of 2D4D is to generate a one-of-a-kind data collection to investigate the technical and socio-economic dynamics of these technologies, and how they may affect decarbonization narratives and scenarios. This will be achieved through several data collection methods, including desk research, surveys and expert elicitations. The second objective of 2D4D is to include digitalization dynamics in decarbonization narratives and pathways. On the one hand, this entails enhancing decarbonization narratives (specifically, the Shared Socio-economic Pathways) to describe digitalization dynamics. On the other hand, it requires improving the representation of sector-specific digitalization dynamics in Integrated Assessment Models, one of the main tools available to generate decarbonization pathways. The third objective of 2D4D is to identify no-regret, robust policy portfolios. These will be designed to ensure that digitalization unfolds in an inclusive, climate-beneficial way, and that decarbonization policies capitalize on digital technologies to support the energy transition.

1 498 375 €

Start date: 2020-10-01, End date: 2025-09-30

Parkinson’s disease (PD) is the second most common neurodegenerative disorder primarily caused by the progressive loss of dopaminergic (DA) neurons in the substantia nigra (SN). Despite the advances in gene discovery associated with PD, the knowledge of the PD pathogenesis is largely limited to the involvement of these genes in the generic cell death pathways, and why degeneration is specific to DA neurons and why the degeneration is progressive remain enigmatic. Broad goal of our work is therefore to elucidate the mechanisms underlying specific and progressive DA neuron degeneration in PD. Our new Drosophila model of PD ⎯Fer2 gene loss-of-function mutation⎯ is unusually well suited to address these questions. Fer2 mutants exhibit specific and progressive death of brain DA neurons as well as severe locomotor defects and short life span. Strikingly, the death of DA neuron is initiated in a small cluster of Fer2-expressing DA neurons and subsequently propagates to Fer2-negative DA neurons. We therefore propose a novel two-step model of the neurodegeneration in PD: primary cell death occurs in a specific subset of dopamindegic neurons that are genetically defined, and subsequently the failure of the neuronal connectivity triggers and propagates secondary cell death to remaining DA neurons. In this research, we will test this hypothesis and investigate the underlying molecular mechanisms. This will be the first study to examine circuit-dependency in DA neuron degeneration. Our approach will use a combination of non-biased genomic techniques and candidate-based screening, in addition to the powerful Drosophila genetic toolbox. Furthermore, to test this hypothesis beyond the Drosophila model, we will establish new mouse models of PD that exhibit progressive DA neuron degeneration. Outcome of this research will likely revolutionize the understanding of PD pathogenesis and open an avenue toward the discovery of effective therapy strategies against PD.

1 518 960 €

Start date: 2013-06-01, End date: 2018-05-31

Despite their amazing success, we believe that computer vision algorithms have only scratched the surface of what can be done in terms of modeling and understanding our world from images. We believe that novel image analysis techniques will be a major enabler and driving force behind next-generation technologies, enhancing everyday life and opening up radically new possibilities. And we believe that the key to achieving this is to develop algorithms for reconstructing and analyzing the 3D structure of our world. In this project, we will focus on three lines of research: A) We will develop algorithms for 3D reconstruction from standard color cameras and from RGB-D cameras. In particular, we will promote real-time-capable direct and dense methods. In contrast to the classical two-stage approach of sparse feature-point based motion estimation and subsequent dense reconstruction, these methods optimally exploit all color information to jointly estimate dense geometry and camera motion. B) We will develop algorithms for 3D shape analysis, including rigid and non-rigid matching, decomposition and interpretation of 3D shapes. We will focus on algorithms which are optimal or near-optimal. One of the major computational challenges lies in generalizing existing 2D shape analysis techniques to shapes in 3D and 4D (temporal evolutions of 3D shape). C) We will develop shape priors for 3D reconstruction. These can be learned from sample shapes or acquired during the reconstruction process. For example, when reconstructing a larger office algorithms may exploit the geometric self-similarity of the scene, storing a model of a chair and its multiple instances only once rather than multiple times. Advancing the state of the art in geometric reconstruction and geometric analysis will have a profound impact well beyond computer vision. We strongly believe that we have the necessary competence to pursue this project. Preliminary results have been well received by the community.

2 000 000 €

Start date: 2015-09-01, End date: 2020-08-31