← Leaderboards
Dhavan Shah
University Of Wisconsin-Madison
$1,708,364
Attributed
$3,416,728
Total exposure
1
Grants
0
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 $685.6K · FY2019–23$1M$750K$500K$250K$0
'19
'20
'21
'22
'23
Funding mix
By agency
NIH$3,416,728 · 1
By mechanism
R01$3,416,728 · 1
Top collaborators
- John J. Curtin5 shared
Most similar at University Of Wisconsin-Madison
Same institution · by research overlap
- Andrew Quanbeck$9,848,658
- Todd David Molfenter$10,297,551
- Michael Ronan Lucey$1,166,224
- Kimberly Ann Johnson$1,008,318
- Ryan J Herringa$9,458,170
Others in their field
Other Emerging Leaders on “Diagnosis”
- Bambra Strokes · Ppd Development Lp$526,656,217
- Leonard Freedman · Leidos Biomedical Research, Inc.$337,858,596
- Ethan Dmitrovsky · Leidos Biomedical Research, Inc.$141,100,782
- Lynn Briscoe · Leidos Biomedical Research, Inc.$93,294,869
- Marlene Ann Cooper · Harvard University D/B/A Harvard School Of Public Health$71,912,072
- Leonard Freedmand · Leidos Biomedical Research, Inc.$66,248,714
Research focus
DiagnosisDigitalAdvertisingData SourcesAddictionAbstinenceAlcoholsAlcohol TestingAffectAcousticsBehaviorCare ProvidersCare SystemsCaringCellular PhoneCharacteristicsChronic PainClinical CareCommunicationCommunitiesComorbidityCostCravingDistal
Grant awards (5)
Contextualized daily prediction of lapse risk in opioid use disorder by digital phenotyping$685,565
R01 · FY2023 · DA
Contextualized daily prediction of lapse risk in opioid use disorder by digital phenotyping$683,287
R01 · FY2022 · DA
Contextualized daily prediction of lapse risk in opioid use disorder by digital phenotyping$683,287
R01 · FY2021 · DA
Contextualized daily prediction of lapse risk in opioid use disorder by digital phenotyping$681,159
R01 · FY2020 · DA
Contextualized daily prediction of lapse risk in opioid use disorder by digital phenotyping$683,430
R01 · FY2019 · DA