Venkat Chandrasekaran | Academic Influence

Venkat Chandrasekaran's AcademicInfluence.com Rankings

Venkat Chandrasekaran

Mathematics

#6806

World Rank

#9357

Historical Rank

#1991

USA Rank

Applied Mathematics

#466

World Rank

#498

Historical Rank

#94

USA Rank

Measure Theory

#3722

World Rank

#4388

Historical Rank

#1069

USA Rank

mathematics Degrees

Download Badge

Mathematics

Venkat Chandrasekaran's Degrees

Bachelors Mathematics Stanford University

Similar Degrees You Can Earn

Best Online Bachelor's in Math 2025

Why Is Venkat Chandrasekaran Influential?

(Suggest an Edit or Addition)

According to Wikipedia, Venkat Chandrasekaran is a Professor in the Computing and Mathematical Sciences Department at the California Institute of Technology. He is known for work on mathematical optimization and its application to the information sciences.

(See a Problem?)

Venkat Chandrasekaran'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

The Convex Geometry of Linear Inverse Problems (2010) (1260)
Rank-Sparsity Incoherence for Matrix Decomposition (2009) (1075)
Latent variable graphical model selection via convex optimization (2010) (485)
Computational and statistical tradeoffs via convex relaxation (2012) (205)
Sparse and low-rank matrix decompositions (2009) (157)
Complexity of Inference in Graphical Models (2008) (118)
Symmetries and charges of general relativity at null boundaries (2018) (99)
Relative Entropy Relaxations for Signomial Optimization (2014) (78)
Diagonal and Low-Rank Matrix Decompositions, Correlation Matrices, and Ellipsoid Fitting (2012) (71)
Relative entropy optimization and its applications (2017) (64)
Including contributions from entanglement islands to the reflected entropy (2020) (60)
The Convex algebraic geometry of linear inverse problems (2010) (59)
An algebra of observables for de Sitter space (2022) (54)
Recovery of Sparse Probability Measures via Convex Programming (2012) (50)
Representation and Compression of Multidimensional Piecewise Functions Using Surflets (2008) (47)
Estimation in Gaussian Graphical Models Using Tractable Subgraphs: A Walk-Sum Analysis (2008) (47)
Regularization for Design (2014) (45)
Feedback Message Passing for Inference in Gaussian Graphical Models (2010) (40)
Anomalies in gravitational charge algebras of null boundaries and black hole entropy (2020) (35)
Regularization for design (2016) (34)
Surflets: a sparse representation for multidimensional functions containing smooth discontinuities (2004) (34)
Signomial and polynomial optimization via relative entropy and partial dualization (2019) (31)
Iterative projections for signal identification on manifolds: Global recovery guarantees (2011) (31)
Newton Polytopes and Relative Entropy Optimization (2018) (31)
Gaussian Multiresolution Models: Exploiting Sparse Markov and Covariance Structure (2010) (30)
High-dimensional change-point estimation: Combining filtering with convex optimization (2014) (27)
Group symmetry and covariance regularization (2011) (26)
Higher-point positivity (2018) (25)
Multiscale stochastic modeling for tractable inference and data assimilation (2008) (24)
From black hole entropy to energy-minimizing states in QFT (2019) (22)
Quantum null energy condition, entanglement wedge nesting, and quantum focusing (2017) (22)
Large N algebras and generalized entropy (2022) (20)
Symmetries, charges and conservation laws at causal diamonds in general relativity (2019) (19)
A general framework for gravitational charges and holographic renormalization (2021) (19)
Convex graph invariants (2010) (19)
Gravity dual of Connes cocycle flow (2020) (18)
Learning Markov Structure by Maximum Entropy Relaxation (2007) (18)
Asymptotic charges cannot be measured in finite time (2017) (17)
Brown-York charges at null boundaries (2021) (17)
Entropy variations and light ray operators from replica defects (2019) (16)
The embedded triangles algorithm for distributed estimation in sensor networks (2003) (15)
Maximum Entropy Relaxation for Graphical Model Selection Given Inconsistent Statistics (2007) (13)
Resource Allocation for Statistical Estimation (2014) (12)
Fitting Tractable Convex Sets to Support Function Evaluations (2019) (11)
Scattering strings off quantum extremal surfaces (2021) (10)
Compression of Higher Dimensional Functions Containing Smooth Discontinuities (2004) (9)
Compressing Piecewise Smooth Multidimensional Functions Using Surflets: Rate-Distortion Analysis (2004) (9)
Finding Planted Subgraphs with Few Eigenvalues using the Schur-Horn Relaxation (2016) (8)
Conic geometric programming (2013) (8)
Phase and magnitude perceptual sensitivities in nonredundant complex wavelet representations (2003) (8)
Exploiting sparse Markov and covariance structure in multiresolution models (2009) (7)
Adaptive Embedded Subgraph Algorithms using Walk-Sum Analysis (2007) (6)
Islands for Reflected Entropy (2020) (6)
Interpreting latent variables in factor models via convex optimization (2016) (6)
Convex optimization methods for graphs and statistical modeling (2011) (5)
REJOINDER: LATENT VARIABLE GRAPHICAL MODEL SELECTION VIA CONVEX OPTIMIZATION (2017) (5)
Quantum error correction in SYK and bulk emergence (2022) (5)
Maximum entropy relaxation for multiscale graphical model selection (2008) (5)
Learning Semidefinite Regularizers (2017) (5)
False discovery and its control in low rank estimation (2018) (4)
Few Numerical Algorithms for Minimization Unconstrained Non Linear Functions (2012) (3)
Tree-structured statistical modeling via convex optimization (2011) (3)
Summarizing data through a piecewise linear growth curve model (2005) (3)
Terracini convexity (2020) (2)
Optimal Cost-Aware Paradigm for Off-Grid Green Cellular Networks in Oman (2021) (2)
Erratum: Group symmetry and covariance regularization (2013) (1)
Efficiently Characterizing Games Consistent with Perturbed Equilibrium Observations (2016) (1)
Message passing and distributed statistical inference (2007) (1)
Recovering Games from Perturbed Equilibrium Observations Using Convex Optimization (2016) (1)
Computational and Sample Tradeoffs via Convex Relaxation (2012) (1)
Learning Exponential Family Graphical Models with Latent Variables using Regularized Conditional Likelihood (2020) (1)
Convex graph invariant relaxations for graph edit distance (2019) (1)
Modeling and estimation in Gaussian graphical models : maximum-entropy methods and walk-sum analysis (2007) (1)
Kronecker Product Approximation of Operators in Spectral Norm via Alternating SDP (2022) (1)
Resource Allocation for Statistical Estimation Adoptingageneralviewofthenotionofaresourceanditseffectonthequalityofadata source, the authors describe in this paper aframework for the allocation of a given set of resources to a collection of sources in order to optimize a specified metric of statisti (2016) (0)
Supplementary Material: Interpreting Latent Variables in Factor Models via Convex Optimization (2017) (0)
Brown-York charges at null boundaries (2022) (0)
Filling the gap: Estimation of soil composition using InSAR, groundwater depth, and precipitation data in California’s Central Valley (2021) (0)
A tale of two saddles (2022) (0)
A note on convex relaxations for the inverse eigenvalue problem (2019) (0)
Learning Semidefinite-Representable Regularizers (2019) (0)
Fitting Tractable Convex Sets to Support Function Evaluations (2021) (0)
Higher-point positivity (2018) (0)
Latent Variable Graphical Modeling for High Dimensional Data Analysis (2019) (0)
A Matrix Factorization Approach for Learning Semidefinite-Representable Regularizers (2017) (0)
California Reservoir Drought Sensitivity and Exhaustion Risk Using Statistical Graphical Models (2016) (0)
Anomalies in gravitational charge algebras of null boundaries and black hole entropy (2021) (0)
Learning Semidefinite Regularizers (2018) (0)
Optimal Convex and Nonconvex Regularizers for a Data Source (2022) (0)
Title Asymptotic charges cannot be measured in finite time Permalink (2018) (0)
Relative entropy optimization and its applications (2016) (0)
Symmetries and charges of general relativity at null boundaries (2018) (0)
Spectrahedral Regression (2021) (0)
Interpreting latent variables in factor models via convex optimization (2017) (0)
Sufficient Dimension Reduction and Modeling Responses Conditioned on Covariates: An Integrated Approach via Convex Optimization (2015) (0)

This paper list is powered by the following services:

Other Resources About Venkat Chandrasekaran

en.wikipedia.org

What Schools Are Affiliated With Venkat Chandrasekaran?

Venkat Chandrasekaran is affiliated with the following schools: