← Leaderboards
Angela Zhang
Stanford University
$118,289
Attributed
$118,289
Total exposure
1
Grants
1
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. They are the sole PI on all grants (the two match).
Funding over time
peak $40.8K · FY2021–23$50K$37.5K$25K$12.5K$0
'21
'22
'23
Funding mix
By agency
NIH$118,289 · 1
By mechanism
F30$118,289 · 1
Top collaborators
No co-investigators on record.
Most similar at Stanford University
Same institution · by research overlap
- Joseph C Wu$80,370,369
- Mark Mercola$44,191,191
- Katherine W Ferrara$45,937,231
- Anshul Kundaje$16,927,829
- Thomas D Wang$22,418,344
Others in their field
Other Emerging Leaders on “Transcriptomics”
- Evan Z Macosko · Broad Institute, Inc.$38,970,817
- Chris Karlovich · Leidos Biomedical Research, Inc.$38,534,220
- Ethan Dmitrovsky · Leidos Biomedical Research, Inc.$30,173,550
- Kathleen Maletic Neuzil · Emory University$26,258,144
- Hemali Phatnani · New York University School Of Medicine$24,538,801
- Patricia Shifflett · Westat, Inc.$16,650,520
Research focus
TranscriptomicsArtificial IntelligenceAtlasesProductionProtocols DocumentationRegenerative MedicineResearch PersonnelScientistTrainingCardiac MyocytesCardiac Tissue EngineeringCardiovascular SystemCell LineCombatComputer ModelsComputer Vision SystemsConsumptionContrast ImagingCostData SetDeep LearningDifferentiation AntigensDisease ModelDoctor Of Philosophy
Grant awards (3)
Using Deep Learning to Predict Induced Pluripotent Stem Cell-Derived Cardiomyocyte (iPSC-CM) Differentiation Outcomes$40,772
F30 · FY2023 · HL · contact PI
Using Deep Learning to Predict Induced Pluripotent Stem Cell-Derived Cardiomyocyte (iPSC-CM) Differentiation Outcomes$39,523
F30 · FY2022 · HL · contact PI
Using Deep Learning to Predict Induced Pluripotent Stem Cell-Derived Cardiomyocyte (iPSC-CM) Differentiation Outcomes$37,994
F30 · FY2021 · HL · contact PI