Project acronym CRISP
Project Cognitive Aging: From Educational Opportunities to Individual Risk Profiles
Researcher (PI) Anja LEIST
Host Institution (HI) UNIVERSITE DU LUXEMBOURG
Country Luxembourg
Call Details Starting Grant (StG), SH3, ERC-2018-STG
Summary Cognitive impairment and dementia have dramatic individual and social consequences, and create high economic costs for societies. In order to delay cognitive aging of future generations as long as possible, we need evidence about which contextual factors are most supportive for individuals to reach highest cognitive levels relative to their potential. At the same time, for current older generations, we need scalable methods to exactly identify individuals at risk of cognitive impairment. The project intends to apply recent methodological and statistical advancements to reach two objectives. Firstly, contextual influences on cognitive aging will be comparatively assessed, with a focus on inequalities related to educational opportunities and gender inequalities. This will be done using longitudinal, population-representative, harmonized cross-national aging surveys, merged with contextual information. Secondly, the project will quantify the ability of singular and clustered individual characteristics, such as indicators of cognitive reserve and behaviour change, to predict cognitive aging and diagnosis of dementia. Project methodology will rely partly on parametric ‘traditional’ multilevel- or fixed-effects modelling, partly on non-parametric statistical learning approaches, to address objectives both hypothesis- and data-driven. Applying statistical learning techniques in the field of cognitive reserve will open new research avenues for efficient handling of large amounts of data, among which most prominently the accurate prediction of health and disease outcomes. Quantifying the role of contextual inequalities related to education and gender will guide policymaking in and beyond the project. Assessing risk profiles of individuals in relation to cognitive aging will support efficient and scalable risk screening of individuals. Identifying the value of behaviour change to delay cognitive impairment will guide treatment plans for individuals affected by dementia.
Summary
Cognitive impairment and dementia have dramatic individual and social consequences, and create high economic costs for societies. In order to delay cognitive aging of future generations as long as possible, we need evidence about which contextual factors are most supportive for individuals to reach highest cognitive levels relative to their potential. At the same time, for current older generations, we need scalable methods to exactly identify individuals at risk of cognitive impairment. The project intends to apply recent methodological and statistical advancements to reach two objectives. Firstly, contextual influences on cognitive aging will be comparatively assessed, with a focus on inequalities related to educational opportunities and gender inequalities. This will be done using longitudinal, population-representative, harmonized cross-national aging surveys, merged with contextual information. Secondly, the project will quantify the ability of singular and clustered individual characteristics, such as indicators of cognitive reserve and behaviour change, to predict cognitive aging and diagnosis of dementia. Project methodology will rely partly on parametric ‘traditional’ multilevel- or fixed-effects modelling, partly on non-parametric statistical learning approaches, to address objectives both hypothesis- and data-driven. Applying statistical learning techniques in the field of cognitive reserve will open new research avenues for efficient handling of large amounts of data, among which most prominently the accurate prediction of health and disease outcomes. Quantifying the role of contextual inequalities related to education and gender will guide policymaking in and beyond the project. Assessing risk profiles of individuals in relation to cognitive aging will support efficient and scalable risk screening of individuals. Identifying the value of behaviour change to delay cognitive impairment will guide treatment plans for individuals affected by dementia.
Max ERC Funding
1 148 290 €
Duration
Start date: 2019-01-01, End date: 2023-12-31
Project acronym STAMFORD
Project Statistical Methods For High Dimensional Diffusions
Researcher (PI) Mark Podolskij
Host Institution (HI) UNIVERSITE DU LUXEMBOURG
Country Luxembourg
Call Details Consolidator Grant (CoG), PE1, ERC-2018-COG
Summary In the past twenty years the availability of vast dimensional data, typically referred to as big data, has given rise to exciting challenges in various fields of mathematics and computer sciences. The increasing need for getting a better understanding of such data in internet traffic, biology, genetics, and economics, has lead to a revolution in statistical and machine learning, optimisation and numerical analysis. Due to high dimensionality of modern statistical models, parameter estimation is a difficult task and statisticians typically investigate estimation methods under sparsity constraints. While an abundance of estimation algorithms is now available for high dimensional discrete models, a rigorous mathematical investigation of estimation problems for high dimensional continuous-time processes is completely undeveloped.
The aim of STAMFORD is to provide a concise statistical theory for estimation of high dimensional diffusions. Such high dimensional processes naturally appear in modelling particle interactions in physics, neural networks in biology or large portfolios in economics, just to name a few. The methodological part of the project will require development of novel
advanced techniques in mathematical statistics and probability theory. In particular, new results will be needed in parametric and non-parametric statistics, and high dimensional probability, that are reaching far beyond the state-of-the-art. Hence, a successful outcome of STAMFORD will not only have a tremendous impact on statistical inference for continuous-time models in natural and applied sciences, but also strongly influence the field of high dimensional statistics and probability.
Summary
In the past twenty years the availability of vast dimensional data, typically referred to as big data, has given rise to exciting challenges in various fields of mathematics and computer sciences. The increasing need for getting a better understanding of such data in internet traffic, biology, genetics, and economics, has lead to a revolution in statistical and machine learning, optimisation and numerical analysis. Due to high dimensionality of modern statistical models, parameter estimation is a difficult task and statisticians typically investigate estimation methods under sparsity constraints. While an abundance of estimation algorithms is now available for high dimensional discrete models, a rigorous mathematical investigation of estimation problems for high dimensional continuous-time processes is completely undeveloped.
The aim of STAMFORD is to provide a concise statistical theory for estimation of high dimensional diffusions. Such high dimensional processes naturally appear in modelling particle interactions in physics, neural networks in biology or large portfolios in economics, just to name a few. The methodological part of the project will require development of novel
advanced techniques in mathematical statistics and probability theory. In particular, new results will be needed in parametric and non-parametric statistics, and high dimensional probability, that are reaching far beyond the state-of-the-art. Hence, a successful outcome of STAMFORD will not only have a tremendous impact on statistical inference for continuous-time models in natural and applied sciences, but also strongly influence the field of high dimensional statistics and probability.
Max ERC Funding
1 655 048 €
Duration
Start date: 2019-09-01, End date: 2024-08-31
Project acronym UpTEMPO
Project Ultrafast tunneling microscopy by optical field control of quantum currents
Researcher (PI) Daniele BRIDA
Host Institution (HI) UNIVERSITE DU LUXEMBOURG
Country Luxembourg
Call Details Consolidator Grant (CoG), PE2, ERC-2018-COG
Summary The project aims at imaging electronic dynamics in molecules with atomic precision and sub-femtosecond temporal resolution. This result will be achieved by establishing new experiments at the boundary of ultrafast optics and scanning probe microscopy where the electric field of single-cycle light pulses is harnessed to control currents in nanojunctions. The basic concept relies on the fact that state-of-the-art femtosecond optical wave packets exhibit only one cycle of radiation with a defined electric field maximum. These pulses need to be phase locked to a “cosine-like” electric field profile. If such radiation is focused onto a junction with a nonlinear current-voltage characteristics, a net charge flow results solely due to the bias induced by the optical field.
In detail, we want to exploit the time resolution provided by this new technique and induce electron transport at the probe tip of a scanning tunneling microscope (STM). The optical control of the current over a sub-optical-cycle interval will guarantee a temporal resolution better that one femtosecond, thus improving by several orders of magnitude what can be achieved with standard electronic bias.
The core of the experimental system will be an ultrabroadband and passively phase-locked Er:fiber laser that is designed to generate single-cycle optical pulses in the near/mid-infrared, i.e. off resonant to the transition energies of III-V and II-VI semiconductors and large molecules. This laser will operate at 80-MHz repetition rate for enhanced sensitivity and stability when coupled to an ultra-high-vacuum STM. The setup will allow for the direct combination of independent pulse trains to resonantly excite few-femtosecond dynamics and then probe the electron density via the optically driven tunneling. In this pump-probe scheme it will be possible to map with atomic resolution the coherent evolution of electronic wavefunctions that in molecules and nanosystems follows an impulsive photoexcitation.
Summary
The project aims at imaging electronic dynamics in molecules with atomic precision and sub-femtosecond temporal resolution. This result will be achieved by establishing new experiments at the boundary of ultrafast optics and scanning probe microscopy where the electric field of single-cycle light pulses is harnessed to control currents in nanojunctions. The basic concept relies on the fact that state-of-the-art femtosecond optical wave packets exhibit only one cycle of radiation with a defined electric field maximum. These pulses need to be phase locked to a “cosine-like” electric field profile. If such radiation is focused onto a junction with a nonlinear current-voltage characteristics, a net charge flow results solely due to the bias induced by the optical field.
In detail, we want to exploit the time resolution provided by this new technique and induce electron transport at the probe tip of a scanning tunneling microscope (STM). The optical control of the current over a sub-optical-cycle interval will guarantee a temporal resolution better that one femtosecond, thus improving by several orders of magnitude what can be achieved with standard electronic bias.
The core of the experimental system will be an ultrabroadband and passively phase-locked Er:fiber laser that is designed to generate single-cycle optical pulses in the near/mid-infrared, i.e. off resonant to the transition energies of III-V and II-VI semiconductors and large molecules. This laser will operate at 80-MHz repetition rate for enhanced sensitivity and stability when coupled to an ultra-high-vacuum STM. The setup will allow for the direct combination of independent pulse trains to resonantly excite few-femtosecond dynamics and then probe the electron density via the optically driven tunneling. In this pump-probe scheme it will be possible to map with atomic resolution the coherent evolution of electronic wavefunctions that in molecules and nanosystems follows an impulsive photoexcitation.
Max ERC Funding
1 999 509 €
Duration
Start date: 2019-09-01, End date: 2024-08-31
