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Jinbo Bi

University Of Connecticut Storrs

$3,011,014
Attributed
$4,673,011
Total exposure
4
Grants
3
Lead (contact PI)

Attributed= this PI's even-split share of every grant they're on (the fair, additive number). Exposure = full size of all those grants.

Funding over time

peak $897.1K · FY201523
$1M$750K$500K$250K$0
'15
'16
'17
'18
'19
'20
'21
'22
'23

Funding mix

By agency

NIH$4,673,011 · 4

By mechanism

R01$3,864,291 · 3
K02$808,720 · 1

Top collaborators

Most similar at University Of Connecticut Storrs

Same institution · by research overlap

Others in their field

Top investigators on “Sampling

Research focus

SamplingInnovationSymptomsTreatment ResponseMachine LearningStatistical ModelsGeneticStatistical MethodsEtiologyFoundationsDiagnostic And Statistical Manual Of Mental DisordersDiagnosisAlcohol DependenceDiagnosticPhenotypeCluster AnalysisGenetic StudyGenotypeGenesHeterogeneityAddictionInterdisciplinary StudyGenetic MarkersHeritability

Grant awards (17)

Multi-level statistical classification of substance use disorder$432,211
R01 · FY2023 · DA · contact PI
SCH: Personalized Depression Treatment Support by Mobile Sensor Analytics$233,742
R01 · FY2023 · MH
Multi-level statistical classification of substance use disorder$430,888
R01 · FY2022 · DA · contact PI
Multi-level statistical classification of substance use disorder$432,562
R01 · FY2021 · DA · contact PI
SCH: Personalized Depression Treatment Support by Mobile Sensor Analytics$245,551
R01 · FY2021 · MH
Classifying addictions using machine learning analysis of multidimensional data$158,923
K02 · FY2021 · DA · contact PI
Multi-level statistical classification of substance use disorder$465,370
R01 · FY2020 · DA · contact PI
SCH: Personalized Depression Treatment Support by Mobile Sensor Analytics$270,271
R01 · FY2020 · MH
Classifying addictions using machine learning analysis of multidimensional data$161,422
K02 · FY2020 · DA · contact PI
SCH: Personalized Depression Treatment Support by Mobile Sensor Analytics$292,411
R01 · FY2019 · MH
Classifying addictions using machine learning analysis of multidimensional data$162,123
K02 · FY2019 · DA · contact PI
Quantitative methods to subtype drug dependence and detect novel genetic variants$272,868
R01 · FY2018 · DA · contact PI
Classifying addictions using machine learning analysis of multidimensional data$162,800
K02 · FY2018 · DA · contact PI
Quantitative methods to subtype drug dependence and detect novel genetic variants$217,494
R01 · FY2017 · DA · contact PI
Classifying addictions using machine learning analysis of multidimensional data$163,452
K02 · FY2017 · DA · contact PI
Quantitative methods to subtype drug dependence and detect novel genetic variants$282,886
R01 · FY2016 · DA · contact PI
Quantitative methods to subtype drug dependence and detect novel genetic variants$288,037
R01 · FY2015 · DA · contact PI