ERC FUNDED PROJECTS

Four years ago, the LIGO/Virgo observation of a black-hole binary merger heralded the dawn of gravitational-wave astronomy. The promise of future observations calls for an invigorated effort to underpin the theoretical framework and supply the predictions needed for detecting future signals and exploiting them for astronomical and astrophysical studies. Ampl2Einstein will take ideas and techniques from recent years' dramatic advances in Quantum Scattering Amplitudes, creating new tools for taking their classical limits and using it for gravitational physics. The powerful `square root' relation between gravity and a generalization of electrodynamics known as Yang--Mills theory will play a key role in making this route simpler than direct classical calculation. We will transfer these ideas to classical General Relativity to compute new perturbative orders, spin-dependent observables, and the dependence on the internal structure of merging objects. We will exploit symmetries and structure we find in order to extrapolate to even higher orders in the gravitational theory. We will make such calculations vastly simpler, pushing the known frontier much further in perturbation theory and in complexity of observables. These advances will give rise to a new generation of gravitational-wave templates, dramatically extending the observing power of detectors. They will allow observers to see weaker signals and will be key to resolving long-standing puzzles about the internal structure of neutron stars. We will apply novel technologies developed for scattering amplitudes to bound-state calculations in both quantum and classical theory. Our research will also lead to a deeper understanding of the classical limit of quantum field theory, relevant to gravitational-wave observations and beyond. The transfer of ideas to the new domain of General Relativity will dramatically enhance our ability to reveal new physics encoded in the subtlest of gravitational-wave signals.

2 372 571 €

Start date: 2021-01-01, End date: 2025-12-31

To obtain data-driven dynamic models for simulation, prediction, monitoring, classification or control tasks, in applications e.g. in Industry 4.0 and eHealth, most identification methods ‘solve’ an optimization problem, relying on some nonlinear iterative algorithm. Undeniably, too many heuristics prevail: What do we mean by ‘solved’? Where did the algorithm converge to? Is the model globally optimal, unique and reproducible? To tackle these scientific deficiencies, we design a framework to deal with inexact data. We solve a longstanding open problem of least squares optimality in system identification: for polynomial dynamical models, the optimal model derives from an eigenvalue problem. Hereto, we generalize notions from Algebraic Geometry (multivariate polynomials), Operator Theory (model spaces), System Theory (multidimensional realization) and Numerical Linear Algebra (matrix computations). The first objective is to develop a mathematically rigorous realization approach that maps data onto new mathematical structures (multi-shift invariant projective subspaces). The second objective is to conceive a ‘misfit-latency’ framework to optimally map inexact data to these mathematical structures. We prove this to be a multiparameter eigenvalue problem. We expect breakthroughs in system theoretic characterizations of optimality (covering all existing methods), in the generalization to multiple input-output and multidimensional models and in finding the global optimum in the linear dynamic H2 model reduction problem. The third objective is to implement matrix computation algorithms for the results of the first two objectives, to root sets of multivariate polynomials, to solve multiparameter eigenvalue problems and to isolate only the minimizing roots. We focus on matrix aspects of large scale, sparsity and structure. Deliverables will be publications, software, graduate course material and science outreach initiatives, in line with the PI’s excellent track record

2 485 925 €

Start date: 2021-01-01, End date: 2025-12-31

Influenza A viruses (IAVs) are zoonotic pathogens that frequently cross the species barrier into humans, often causing severe morbidity and even global pandemics. This cross-species transmission is facilitated in large part by alterations in the interaction between the viral surface proteins hemagglutinin (HA) and neuraminidase (NA) and sialic acid, a ubiquitous glycan that serves as the cellular entry receptor. Although avian hosts have generally been thought to be the primary reservoir for all influenza A viruses, this dogma has recently been challenged by the identification of two novel IAV subtypes in bats, H17N10 and H18N11. Despite an otherwise high degree of functional homology to conventional IAVs, the surface proteins of bat IAVs demonstrate several unprecedented characteristics. Specifically, these proteins are unable to interact with sialic acid; rather, we recently showed that bat IAVs use the major histocompatibility complex class II (MHC-II) protein to gain entry into host cells. Unexpectedly, we observed that N11 downregulates surface expression of MHC-II, suggesting that it potentially harbors a receptor-destroying function. Most surprisingly, bat IAV could replicate to even higher titers the absence of functional NA, a capability which has never been observed among influenza viruses. These findings suggest that the surface glycoproteins of bat IAV may possess a structural plasticity that is much broader than that of conventional IAV. In light of the critical importance of the surface proteins for cross-species transmission of IAV, the goal of this project will be to probe this plasticity, first by determining the mode of interaction between H17/H18 and MHC-II and elucidating the mechanism of N10/N11-dependent downregulation of MHC-II, but most importantly by using forced evolution to explore the plasticity of IAV for new cellular entry factors. The insights from these studies will have a major impact on our understanding of influenza virus tropism.

2 499 638 €

Start date: 2020-11-01, End date: 2025-10-31

All day long, our fingers touch, grasp and move objects in various media such as air, water, oil. We do this almost effortlessly - it feels like we do not spend time planning and reflecting over what our hands and fingers do or how the continuous integration of various sensory modalities such as vision, touch, proprioception, hearing help us to outperform any other biological system in the variety of the interaction tasks that we can execute. Largely overlooked, and perhaps most fascinating is the ease with which we perform these interactions resulting in a belief that these are also easy to accomplish in artificial systems such as robots. However, there are still no robots that can easily hand-wash dishes, button a shirt or peel a potato. Our claim is that this is fundamentally a problem of appropriate representation or parameterization. When interacting with objects, the robot needs to consider geometric, topological, and physical properties of objects. This can be done either explicitly, by modeling and representing these properties, or implicitly, by learning them from data. The main scientific objective of this project is to create new informative and compact representations of deformable objects that incorporate both analytical and learning-based approaches and encode geometric, topological, and physical information about the robot, the object, and the environment. We will do this in the context of challenging multimodal, bimanual object interaction tasks. The focus will be on physical interaction with deformable objects using multimodal feedback. To meet these objectives, we will use theoretical and computational methods together with rigorous experimental evaluation to model skilled sensorimotor behavior in bimanual robot systems.

2 424 186 €

Start date: 2020-09-01, End date: 2025-08-31