Faculty

Institution

Unit

Venue

Venkat Chandrasekaran

Kiyo and Eiko Tomiyasu Professor of Computing and Mathematical Sciences and Electrical Engineering; Undergraduate Option Representative for Applied and Computational Mathematics

California Institute of Technology

Affiliations

Division of Engineering and Applied Science

Electrical Engineering

Venkat Chandrasekaran is currently serving as a Division Of Engineering And Applied Science Kiyo and Eiko Tomiyasu Professor of Computing and Mathematical Sciences and Electrical Engineering; Undergraduate Option Representative for Applied and Computational Mathematics at California Institute of Technology.

Honors

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Experiences

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Education

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Venkat Chandrasekaran

California Institute of Technology

Kiyo and Eiko Tomiyasu Professor of Computing and Mathematical Sciences and Electrical Engineering; Undergraduate Option Representative for Applied and Computational Mathematics

You can click a keyword to filter the displayed items

applied mathematics machine learning computer science statistics data science mathematical optimization mathematical programming graph theory signal processing computational optimization computational mathematics model selection automatic control foundation of mathematics convex optimization image compression data compression graphical models maximum entropy relaxation gaussian graphical models walk-sum analysis computer vision inference natural language processing control optimization transaction processing estimation theory supervised learning statistical signal processing model compression control science mathematical control theory bayesian inference hydrological modeling relative entropy optimization engineering optimization computational intelligence hydrometeorology water science spectral theory statistical methodology numerical simulations speech processing bayesian analysis water resource management convex analysis information theory message passing filtering technique data assimilation manifold modeling manifold learning computational learning theory structure discovery topology optimization neuroinformatics compression (physics) geometry compression wavelet theory discrete geometry curve fitting audio signal processing acoustic signal processing acoustic modeling convex geometry linear inverse problems statistical tradeoffs convex relaxation matrix decomposition rank-sparsity incoherence relative entropy relaxations signomial optimization low-rank matrix decompositions convex programming sparse probability measures tractable subgraphs combinatorial optimization combinatorics theoretical computer science mathematics algorithm design inverse problems

Notable Scholarly Works

Notable Federal Grants

Collaborators

Model Selection Over Partially Ordered Sets. Proceedings of the National Academy of Sciences (2024)

Armeen TaebPeter BühlmannVenkat Chandrasekaran

bayesian inferencebayesian analysiscomputer sciencemodel selectionprobability theory

Kronecker Product Approximation of Operators in Spectral Norm via Alternating Sdp. SIAM Journal on Matrix Analysis and Applications (2023)

Mareike DresslerAndré UschmajewVenkat Chandrasekaran

operator theorymathematical optimizationspectral normapplied mathematicsmatrix analysiskronecker product approximation

Fitting Tractable Convex Sets to Support Function Evaluations. Discrete & Computational Geometry (2021)

Yong Sheng SohVenkat Chandrasekaran

tractable convex setscurve fittingfunction evaluationscomputational geometrydiscrete geometryapplied mathematics

Newton Polytopes and Relative Entropy Optimization. Foundations of Computational Mathematics (2021)

Riley MurrayVenkat ChandrasekaranAdam Wierman

computational mathematicsfoundation of mathematicsmathematical optimizationapplied mathematicscomputer sciencerelative entropy optimization

Publisher Correction to: Signomial and Polynomial Optimization via Relative Entropy and Partial Dualization. Mathematical Programming Computation (2021)

Riley MurrayVenkat ChandrasekaranAdam Wierman

mathematical programmingpartial dualizationmachine learningpublisher correctionrelative entropycomputer sciencepolynomial optimization

Convex Graph Invariant Relaxations for Graph Edit Distance. Mathematical Programming (2020)

Utkan Onur CandoganVenkat Chandrasekaran

mathematical programminggraph theorygraph edit distanceapplied mathematics

False Discovery and Its Control in Low Rank Estimation. Journal of the Royal Statistical Society Series B (Statistical Methodology) (2020)

Armeen TaebParikshit ShahVenkat Chandrasekaran

false discoveryapplied statisticsstatisticslow rank estimationstatistical methodology

Learning Exponential Family Graphical Models With Latent Variables Using Regularized Conditional Likelihood.. (2020)

Armeen TaebParikshit ShahVenkat Chandrasekaran

data modelingregularized conditional likelihoodlatent variablesdata sciencemachine learning

Signomial and Polynomial Optimization via Relative Entropy and Partial Dualization. Mathematical Programming Computation (2020)

Riley MurrayVenkat ChandrasekaranAdam Wierman

computational optimizationmathematical programmingpartial dualizationmathematical optimizationapplied mathematicsrelative entropypolynomial optimization

Finding Planted Subgraphs With Few Eigenvalues Using the Schur--Horn Relaxation. SIAM Journal on Optimization (2018)

Utkan Onur CandoganVenkat Chandrasekaran

mathematical optimizationgraph theorytopology optimizationspectral theoryapplied mathematicsstructure discovery

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Venkat Chandrasekaran

Kiyo and Eiko Tomiyasu Professor of Computing and Mathematical Sciences and Electrical Engineering; Undergraduate Option Representative for Applied and Computational Mathematics

California Institute of Technology

Affiliations

Division of Engineering and Applied Science

Electrical Engineering

Honors

Create free account to unlock more features

Experiences

Create free account to unlock more features

Education

Create free account to unlock more features

Venkat Chandrasekaran

California Institute of Technology

Kiyo and Eiko Tomiyasu Professor of Computing and Mathematical Sciences and Electrical Engineering; Undergraduate Option Representative for Applied and Computational Mathematics

You can click a keyword to filter the displayed items

Notable Scholarly Works

Notable Federal Grants

Collaborators

Model Selection Over Partially Ordered Sets. Proceedings of the National Academy of Sciences (2024)

Armeen TaebPeter BühlmannVenkat Chandrasekaran

bayesian inferencebayesian analysiscomputer sciencemodel selectionprobability theory

Kronecker Product Approximation of Operators in Spectral Norm via Alternating Sdp. SIAM Journal on Matrix Analysis and Applications (2023)

Mareike DresslerAndré UschmajewVenkat Chandrasekaran

operator theorymathematical optimizationspectral normapplied mathematicsmatrix analysiskronecker product approximation

Fitting Tractable Convex Sets to Support Function Evaluations. Discrete & Computational Geometry (2021)

Yong Sheng SohVenkat Chandrasekaran

tractable convex setscurve fittingfunction evaluationscomputational geometrydiscrete geometryapplied mathematics

Newton Polytopes and Relative Entropy Optimization. Foundations of Computational Mathematics (2021)

Riley MurrayVenkat ChandrasekaranAdam Wierman

computational mathematicsfoundation of mathematicsmathematical optimizationapplied mathematicscomputer sciencerelative entropy optimization

Publisher Correction to: Signomial and Polynomial Optimization via Relative Entropy and Partial Dualization. Mathematical Programming Computation (2021)

Riley MurrayVenkat ChandrasekaranAdam Wierman

mathematical programmingpartial dualizationmachine learningpublisher correctionrelative entropycomputer sciencepolynomial optimization

Convex Graph Invariant Relaxations for Graph Edit Distance. Mathematical Programming (2020)

Utkan Onur CandoganVenkat Chandrasekaran

mathematical programminggraph theorygraph edit distanceapplied mathematics

False Discovery and Its Control in Low Rank Estimation. Journal of the Royal Statistical Society Series B (Statistical Methodology) (2020)

Armeen TaebParikshit ShahVenkat Chandrasekaran

false discoveryapplied statisticsstatisticslow rank estimationstatistical methodology

Learning Exponential Family Graphical Models With Latent Variables Using Regularized Conditional Likelihood.. (2020)

Armeen TaebParikshit ShahVenkat Chandrasekaran

data modelingregularized conditional likelihoodlatent variablesdata sciencemachine learning

Signomial and Polynomial Optimization via Relative Entropy and Partial Dualization. Mathematical Programming Computation (2020)

Riley MurrayVenkat ChandrasekaranAdam Wierman

computational optimizationmathematical programmingpartial dualizationmathematical optimizationapplied mathematicsrelative entropypolynomial optimization

Finding Planted Subgraphs With Few Eigenvalues Using the Schur--Horn Relaxation. SIAM Journal on Optimization (2018)

Utkan Onur CandoganVenkat Chandrasekaran

mathematical optimizationgraph theorytopology optimizationspectral theoryapplied mathematicsstructure discovery

1
2
3
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5
10 / page

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Edward M Reingold

Institution

University of Illinois Urbana-Champaign

computer science

discrete algorithms

John Iacono

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algorithm design

geometry

Alina Ene

Institution

Boston University

analysis of algorithm

computational optimization

Ming-Yang Kao

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computer science

applied mathematics

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industrial informatics

signal detection

Richard M. Karp

Institution

University of California, Berkeley

computer science

computational complexity

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Dartmouth College

algorithm design

technological forecasting

Maxim Raginsky

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University of Illinois Urbana-Champaign

applied mathematics

information theory

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Carnegie Mellon University

numerical mathematics

general systems

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multidisciplinary ai

computational intelligence