Santanu Dey

Professor

Georgia Institute of Technology

United States

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Santanu Dey is a Professor at the Georgia Institute of Technology in the United States. His research areas encompass optimization in energy systems, mixed-integer programming, and algorithm development for network design and decentralized power systems. He has published numerous articles, including works focused on integrating energy storage systems and the complexities of nonlinear optimization problems.

Articles (19)

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2025

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2020

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2020

Collaborators (4)

Alberto Del Pia

University of Wisconsin-Madison

UNITED STATES

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Aleksandr Kazachkov

University of Florida

UNITED STATES

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Weijun Xie

Assistant Professor

Georgia Institute of Technology

UNITED STATES

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Guanyi Wang

Assistant Professor

National University of Singapore

SINGAPORE

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Santanu Dey

Research Interests

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About

Articles (19)

Regularized MIP Model for Integrating Energy Storage Systems and Its Application for Solving a Trilevel Interdiction Problem

Code and Data Repository for Regularized MIP Model for Integrating Energy Storage Systems and its Application for Solving a Trilevel Interdiction Problem

Non-Monotonicity of Branching Rules with Respect to Linear Relaxations

A reformulation-enumeration MINLP algorithm for gas network design

Solving Sparse Separable Bilinear Programs Using Lifted Bilinear Cover Inequalities

Aggregations of Quadratic Inequalities and Hidden Hyperplane Convexity

Code and Data Repository for Decomposable Formulation of Transmission Constraints for Decentralized Power Systems Optimization

Decomposable Formulation of Transmission Constraints for Decentralized Power Systems Optimization

Solving sparse principal component analysis with global support

Lifting convex inequalities for bipartite bilinear programs

Lower bounds on the size of general branch-and-bound trees

Lower bound on size of branch-and-bound trees for solving lot-sizing problem

A Scalable Lower Bound for the Worst-Case Relay Attack Problem on the Transmission Grid

Cutting Plane Generation through Sparse Principal Component Analysis

Hyperbolic Relaxation of $k$-Locally Positive Semidefinite Matrices

On Obtaining the Convex Hull of Quadratic Inequalities via Aggregations

Using ℓ1-Relaxation and Integer Programming to Obtain Dual Bounds for Sparse PCA

Subset Selection in Sparse Matrices

The Convex Hull of a Quadratic Constraint over a Polytope

Collaborators (4)

Alberto Del Pia

Aleksandr Kazachkov

Weijun Xie

Guanyi Wang

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