Vlad Voroninski
#130,136
Most Influential Person Now
Russian-American mathematician
Vlad Voroninski's AcademicInfluence.com Rankings
Vlad Voroninskimathematics Degrees
Mathematics
#7173
World Rank
#9802
Historical Rank
Applied Mathematics
#385
World Rank
#413
Historical Rank
Measure Theory
#4909
World Rank
#5768
Historical Rank

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Mathematics
Why Is Vlad Voroninski Influential?
(Suggest an Edit or Addition)According to Wikipedia, Vlad Y. Voroninski is a Russian-American mathematician and entrepreneur. Academic biography Voroninski received his B.S. and M.A degrees in Applied Mathematics from UCLA in 2008, summa cum laude. He earned his Ph.D. in mathematics from UC Berkeley in 2013, under the supervision of Emmanuel Candes and John Strain. He was on the faculty at the MIT Mathematics Department from 2013 to 2016.
Vlad Voroninski's Published Works
Number of citations in a given year to any of this author's works
Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author
Published Works
- PhaseLift: Exact and Stable Signal Recovery from Magnitude Measurements via Convex Programming (2011) (1198)
- Phase Retrieval via Matrix Completion (2011) (979)
- A survey of structure from motion * . (2017) (265)
- The non-convex Burer-Monteiro approach works on smooth semidefinite programs (2016) (222)
- Sparse Signal Recovery from Quadratic Measurements via Convex Programming (2012) (214)
- Phase Retrieval Under a Generative Prior (2018) (142)
- On the low-rank approach for semidefinite programs arising in synchronization and community detection (2016) (133)
- Global Guarantees for Enforcing Deep Generative Priors by Empirical Risk (2017) (127)
- Deterministic Guarantees for Burer‐Monteiro Factorizations of Smooth Semidefinite Programs (2018) (87)
- An Elementary Proof of Convex Phase Retrieval in the Natural Parameter Space via the Linear Program PhaseMax (2016) (55)
- A strong restricted isometry property, with an application to phaseless compressed sensing (2014) (49)
- ShapeFit and ShapeKick for Robust, Scalable Structure from Motion (2016) (40)
- Compressed Sensing from Phaseless Gaussian Measurements via Linear Programming in the Natural Parameter Space (2016) (35)
- Determination of all pure quantum states from a minimal number of observables (2013) (32)
- A Provably Convergent Scheme for Compressive Sensing Under Random Generative Priors (2018) (28)
- Corruption Robust Phase Retrieval via Linear Programming (2016) (23)
- Rate-optimal denoising with deep neural networks (2018) (21)
- Deep Denoising: Rate-Optimal Recovery of Structured Signals with a Deep Prior (2018) (18)
- Quantum Tomography From Few Full-Rank Observables (2013) (16)
- ShapeFit: Exact Location Recovery from Corrupted Pairwise Directions (2015) (12)
- Nonasymptotic Guarantees for Spiked Matrix Recovery with Generative Priors (2020) (8)
- Exact Simultaneous Recovery of Locations and Structure from Known Orientations and Corrupted Point Correspondences (2015) (7)
- Compressive Phase Retrieval: Optimal Sample Complexity with Deep Generative Priors (2020) (5)
- No Statistical-Computational Gap in Spiked Matrix Models with Generative Network Priors † (2021) (5)
- Nonasymptotic Guarantees for Low-Rank Matrix Recovery with Generative Priors (2020) (3)
- Implementations of control laws for motion camouflage in a pursuit-evasion system (2006) (2)
- Optimal Sample Complexity of Gradient Descent for Amplitude Flow via Non-Lipschitz Matrix Concentration (2020) (1)
- Optimally Sample-Efficient Phase Retrieval with Deep Generative Models (2019) (0)
- Exact Simultaneous Recovery of Locations and Structure from Known Orientations and Corrupted Point Correspondences (2017) (0)
- PhaseLift: A Novel Methodology for Phase Retrieval (2013) (0)
- Approximation by crystal invariant subspaces (2019) (0)
- A Provably Convergent Scheme for Compressive Sensing Under Random Generative Priors (2021) (0)
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What Schools Are Affiliated With Vlad Voroninski?
Vlad Voroninski is affiliated with the following schools: