Hola,
¿eres nuevo aquí?

Regístrate gratis y conecta tu empresa con financiación pública, partners y proyectos.

Tengo cuenta

Regístrate

Ver video

ScReeningData

Financiado

Cerrado

Scalable Learning for Reproducibility in High-Dimensional Biomedical Signal Proc...

Scalable Learning for Reproducibility in High-Dimensional Biomedical Signal Processing: A Robust Data Science Framework Data science has quickly expanded the boundaries of signal processing and statistical learning beyond their accustomed domains. Powerful and complex machine learning architectures have evolved to distinguish relevant information f... ver más

31/08/2027

TECHNISCHE UNIVERS...

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

TECHNISCHE UNIVERSITAT DARMSTADT No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2022-04-01

ERC-2021-STG: ERC STARTING GRANTS Scope:Objectives

I+D

Cerrada hace 3 años

0% 100% 100%

Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

1 Participantes

TECHNISCHE UNIVERSITAT DARMS...

1.50M€ | Lider

Conecta tu I+D

¿Tienes un proyecto y buscas un partner? Gracias a nuestro motor inteligente podemos recomendarte los mejores socios y ponerte en contacto con ellos. Te lo explicamos en este video

Proyectos interesantes

PRinHDD Pattern Recognition in High Dimensional Data 234K€ Cerrado

CONTESSA COuNt data TimE SerieS Analysis significance tests and sequ... 195K€ Cerrado

SMARTBAYES Intelligent Stochastic Computation Methods for Complex Stati... 550K€ Cerrado

HiDDaProTImA High dimensional data processing from theory to imaging app... 183K€ Cerrado

PrSc-HDBayLe Provable Scalability for high-dimensional Bayesian Learning 1M€ Cerrado

SMAC Statistical machine learning for complex biological data 1M€ Cerrado

Duración del proyecto: 64 meses Fecha Inicio: 2022-04-01
Fecha Fin: 2027-08-31

Líder del proyecto

TECHNISCHE UNIVERSITAT DARMSTADT No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 2M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Data science has quickly expanded the boundaries of signal processing and statistical learning beyond their accustomed domains. Powerful and complex machine learning architectures have evolved to distinguish relevant information from randomness, artifacts and irrelevant data. However, existing learning frameworks lack computationally scalable, tractable, and robust methods for high-dimensional data. Consequently, discoveries, for example, in genomic data can be the result of coincidental findings that happen to reach statistical significance. As long as groundbreaking advances in biotechnology are not accompanied by appropriate learning frameworks, valuable efforts are spent on researching false positives. ScReeningData develops a coherent fast and scalable learning framework that jointly addresses the fundamental challenges of drastically reducing computational complexity, providing statistical and robustness guarantees, and quantifying reproducibility in large-scale and high-dimensional settings. An unprecedented approach is developed that builds upon very recent work of the PI. The underlying concept is to repeat randomized controlled experiments that use computer-generated fake variables as negative controls to trigger an early stopping of the learning algorithms, thereby mitigating the so-called curse of dimensionality. In contrast to existing methods, the proposed methods are completely tractable and scalable to ultra-high dimensions. The gains of developing advanced robust learning methods that are computed ultra-fast and with tight guarantees on the targeted rate of false positives are enormous. They lead to new reproducible discoveries that can be made with high statistical power. Due to the fundamental nature and the broad applicability of the proposed learning methods, the impacts of this project extend far beyond the considered biomedical signal processing use-cases, benefitting all scientific domains that analyze high-dimensional data.

Conecta tu I+D

Entra hoy

¿Olvidé mi contraseña?

Financiación

Empresas

CTIs/Universidades

Proyectos

Investigadores