← Leaderboards
Zhangyang Wang
Texas A&M Engineering Experiment Station
$1,735,916
Attributed
$2,062,416
Total exposure
9
Grants
8
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 $653K · FY2018–22$1M$750K$500K$250K$0
'18
'19
'20
'21
'22
Funding mix
By agency
NSF$2,062,416 · 9
By mechanism
—$2,062,416 · 9
Top collaborators
- Diana Marculescu2 shared
Grant awards (9)
CAREER: Learning Optimization Algorithms from Data: Interpretability, Reliability, and Scalability$500,000
· FY2022 · ENG
Collaborative Research: Probabilistic, Geometric, and Topological Analysis of Neural Networks, From Theory to Applications$153,000
· FY2022 · MPS · contact PI
Collaborative Research: CCSS: Learning to Optimize: From New Algorithms to New Theory$220,000
· FY2021 · ENG · contact PI
RTML: Large: Collaborative: Harmonizing Predictive Algorithms and Mixed-Signal/Precision Circuits via Computation-Data Access Exchange and Adaptive Dataflows$248,511
· FY2020 · CSE · contact PI
Collaborative Research: Enabling Intelligent Cameras in Internet-of-Things via a Holistic Platform, Algorithm, and Hardware Co-design$219,373
· FY2020 · ENG · contact PI
CRII: RI: Learning with Low-Quality Visual Data: Handling Both Passive and Active Degradations$77,253
· FY2020 · CSE · contact PI
RTML: Large: Collaborative: Harmonizing Predictive Algorithms and Mixed-Signal/Precision Circuits via Computation-Data Access Exchange and Adaptive Dataflows$248,511
· FY2019 · CSE · contact PI
Collaborative Research: Enabling Intelligent Cameras in Internet-of-Things via a Holistic Platform, Algorithm, and Hardware Co-design$222,725
· FY2019 · ENG · contact PI
CRII: RI: Learning with Low-Quality Visual Data: Handling Both Passive and Active Degradations$173,043
· FY2018 · CSE · contact PI