Venkat Chandrasekaran
Kiyo and Eiko Tomiyasu Professor of Computing and Mathematical Sciences and Electrical Engineering; Undergraduate Option Representative for Applied and Computational Mathematics
Overview
Venkat Chandrasekaran is an applied mathematician with research interests broadly in optimization and the information sciences. Specific areas of focus include convex optimization, statistical inference, inverse problems, graphs and combinatorial optimization, and applied algebra and geometry. His research group studies the mathematical foundations of these topics to develop principled methods for applications in science and engineering.
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- Taeb, Armeen;Bühlmann, Peter et al. (2024) Model selection over partially ordered setsProceedings of the National Academy of Sciences
- Saunderson, James;Chandrasekaran, Venkat (2022) Terracini convexityMathematical Programming
- Candogan, Utkan Onur;Chandrasekaran, Venkat (2022) Convex graph invariant relaxations for graph edit distanceMathematical Programming
- Taeb, Armeen;Shah, Parikshit et al. (2020) False Discovery and Its Control in Low Rank EstimationJournal of the Royal Statistical Society: Series B
- Soh, Yong Sheng;Chandrasekaran, Venkat (2019) Learning Semidefinite-Representable RegularizersFoundations of Computational Mathematics
- Candogan, Utkan-Onur;Chandrasekaran, Venkat (2018) Finding Planted Subgraphs with Few Eigenvalues using the Schur-Horn RelaxationSIAM Journal on Optimization
- Taeb, Armeen;Chandrasekaran, Venkat (2018) Interpreting Latent Variables in Factor Models via Convex OptimizationMathematical Programming
- Taeb, A.;Reager, J. T. et al. (2017) A Statistical Graphical Model of the California Reservoir SystemWater Resources Research
- Soh, Yong Sheng;Chandrasekaran, Venkat (2017) High-dimensional change-point estimation: Combining filtering with convex optimizationApplied and Computational Harmonic Analysis
- Soh, Yong Sheng;Chandrasekaran, Venkat (2017) A Matrix Factorization Approach for Learning Semidefinite-Representable Regularizers
Related Courses
2023-24
EE 121 – Great Ideas in Data Science
CMS/ACM/EE 122 – Mathematical Optimization
2022-23
CMS/ACM/IDS 113 – Mathematical Optimization
CMS/ACM/EE 122 – Mathematical Optimization
EE 55 – Mathematics of Electrical Engineering
2021-22
CMS/ACM/IDS 113 – Mathematical Optimization
CMS/ACM/EE 122 – Mathematical Optimization
2020-21
CMS/ACM/IDS 113 – Mathematical Optimization
EE 55 – Mathematics of Electrical Engineering