Grant: Postgraduate Scholarships - Doctoral
University of Washington
$35,250.00
Validation for Unsupervised Learning Using a New Class of Data Decomposition
FY 2024-25
Summary
high-dimensional statistics, mathematical statistics, machine learning, unsupervised learning, biostatistics, clustering, validation, applied statistics, latent variables, inference
Program
Postgraduate Scholarships - Doctoral
Recipient
Location
Washington, UNITED STATES
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
577869-2023
Name nom
Dharamshi, Ameer AA
Department département
Biostatistics
Organization id
1607
Institution établissement
University of Washington
Province en
Washington
Country en
UNITED STATES
Country fr
ÉTATS-UNIS
Fiscal year exercice financier
2024
Competition year année de concours
2023
Award amount
35250
Program id
PGSD
Program name en
Postgraduate Scholarships - Doctoral
Program name fr
Bourses d'études supérieures - doctorat
Group en
RESEARCH TRAINING AND TALENT DEVELOPMENT
Group fr
FORMATION EN RECHERCHE ET PERFECTIONNEMENT DES COM
Committee code
177
Committee name en
S&F Selection Committee for Mathematical Sciences
Committee name fr
Comité de sélection des bourses en mathématiques
Area of application code
9999
Area of application group en
Not available
Area of application group fr
Non disponible
Area of application en
Not available
Area of application fr
Non disponible
Research subject code
9999
Research subject group en
Not available
Research subject group fr
Non disponible
Research subject en
Not available
Research subject fr
Non disponible
Field of research list names en
Not available
Field of research list names fr
Non disponible
Socioeconomic objective list names en
Not available
Socioeconomic objective list names fr
Non disponible
Application title
Validation for Unsupervised Learning Using a New Class of Data Decomposition
Application summary
high-dimensional statistics, mathematical statistics, machine learning, unsupervised learning, biostatistics, clustering, validation, applied statistics, latent variables, inference
