Uday V. Shanbhag

Uday V. Shanbhag

Penn State University

H-index: 33

North America-United States

About Uday V. Shanbhag

Uday V. Shanbhag, With an exceptional h-index of 33 and a recent h-index of 27 (since 2020), a distinguished researcher at Penn State University, specializes in the field of Optimization, variational inequalities, stochastic optimization, game theory, power markets.

His recent articles reflect a diverse array of research interests and contributions to the field:

Stochastic Approximation for Multi-period Simulation Optimization with Streaming Input Data

No-regret distributed learning in subnetwork zero-sum games

Data-Driven Compositional Optimization in Misspecified Regimes

Zeroth-Order Methods for Nondifferentiable, Nonconvex, and Hierarchical Federated Optimization

A Distributed Iterative Tikhonov Method for Networked Monotone Aggregative Hierarchical Stochastic Games

Global Resolution of Chance-Constrained Optimization Problems: Minkowski Functionals and Monotone Inclusions

Complexity guarantees for an implicit smoothing-enabled method for stochastic MPECs

Distributed Gradient Tracking Methods with Guarantees for Computing a Solution to Stochastic MPECs

Uday V. Shanbhag Information

University

Position

Professor Industrial and Manufacturing Engineering

Citations(all)

4026

Citations(since 2020)

2397

Cited By

2767

hIndex(all)

33

hIndex(since 2020)

27

i10Index(all)

82

i10Index(since 2020)

58

Email

University Profile Page

Penn State University

Google Scholar

View Google Scholar Profile

Uday V. Shanbhag Skills & Research Interests

Optimization

variational inequalities

stochastic optimization

game theory

power markets

Top articles of Uday V. Shanbhag

Title

Journal

Author(s)

Publication Date

Stochastic Approximation for Multi-period Simulation Optimization with Streaming Input Data

ACM Transactions on Modeling and Computer Simulation

Linyun He

Uday V Shanbhag

Eunhye Song

2023

No-regret distributed learning in subnetwork zero-sum games

IEEE Transactions on Automatic Control

Shijie Huang

Jinlong Lei

Yiguang Hong

Uday V Shanbhag

Jie Chen

2024/3/19

Data-Driven Compositional Optimization in Misspecified Regimes

Operations Research

Shuoguang Yang

Ethan X Fang

Uday V Shanbhag

2024/2/16

Zeroth-Order Methods for Nondifferentiable, Nonconvex, and Hierarchical Federated Optimization

Yuyang Qiu

Uday Shanbhag

Farzad Yousefian

2024

A Distributed Iterative Tikhonov Method for Networked Monotone Aggregative Hierarchical Stochastic Games

arXiv preprint arXiv:2304.03651

Jinlong Lei

Uday V Shanbhag

Jie Chen

2023/4/7

Global Resolution of Chance-Constrained Optimization Problems: Minkowski Functionals and Monotone Inclusions

Peixuan Zhang

Uday V Shanbhag

Constantino M Lagoa

Ibrahim E Bardakci

2023/12/13

Complexity guarantees for an implicit smoothing-enabled method for stochastic MPECs

Mathematical Programming

Shisheng Cui

Uday V Shanbhag

Farzad Yousefian

2023/4

Distributed Gradient Tracking Methods with Guarantees for Computing a Solution to Stochastic MPECs

arXiv preprint arXiv:2310.09356

Mohammadjavad Ebrahimi

Uday V Shanbhag

Farzad Yousefian

2023/10/13

On the computation of equilibria in monotone and potential stochastic hierarchical games

Mathematical Programming

Shisheng Cui

Uday V Shanbhag

2023/4

A regularized variance-reduced modified extragradient method for stochastic hierarchical games

arXiv preprint arXiv:2302.06497

Shisheng Cui

Uday V Shanbhag

Mathias Staudigl

2023/2/13

Variance-reduced splitting schemes for monotone stochastic generalized equations

IEEE Transactions on Automatic Control

Shisheng Cui

Uday V Shanbhag

2023/6/28

Zeroth-order Gradient and Quasi-Newton Methods for Nonsmooth Nonconvex Stochastic Optimization

arXiv preprint arXiv:2401.08665

Luke Marrinan

Uday V Shanbhag

Farzad Yousefian

2023/12/27

Probability maximization via Minkowski functionals: convex representations and tractable resolution

Mathematical programming

IE Bardakci

Afrooz Jalilzadeh

C Lagoa

Uday V Shanbhag

2023/5

Stochastic relaxed inertial forward-backward-forward splitting for monotone inclusions in Hilbert spaces

Computational Optimization and Applications

Shisheng Cui

Uday Shanbhag

Mathias Staudigl

Phan Vuong

2022/11

Stochastic Nash equilibrium problems: Models, analysis, and algorithms

IEEE Control Systems Magazine

Jinlong Lei

Uday V Shanbhag

2022/7/19

Uncertainty-aware optimal dispatch of building thermal storage portfolios via smoothed variance-reduced accelerated gradient methods

Journal of Energy Storage

Min Gyung Yu

Gregory S Pavlak

Uday V Shanbhag

2022/7/1

A variable sample-size stochastic quasi-Newton method for smooth and nonsmooth stochastic convex optimization

Mathematics of Operations Research

Afrooz Jalilzadeh

Angelia Nedić

Uday V Shanbhag

Farzad Yousefian

2022/2

Asynchronous variance-reduced block schemes for composite non-convex stochastic optimization: block-specific steplengths and adapted batch-sizes

Optimization Methods and Software

Jinlong Lei

Uday V Shanbhag

2022/1/2

Smoothed variable sample-size accelerated proximal methods for nonsmooth stochastic convex programs

Stochastic Systems

Afrooz Jalilzadeh

Uday Shanbhag

Jose Blanchet

Peter W Glynn

2022/12

On the analysis of variance-reduced and randomized projection variants of single projection schemes for monotone stochastic variational inequality problems

Set-Valued and Variational Analysis

Shisheng Cui

Uday V Shanbhag

2021/6

See List of Professors in Uday V. Shanbhag University(Penn State University)

Co-Authors

H-index: 64
Angelia Nedich

Angelia Nedich

Arizona State University

H-index: 48
George Kesidis

George Kesidis

Penn State University

H-index: 45
Sheldon H. Jacobson

Sheldon H. Jacobson

University of Illinois at Urbana-Champaign

H-index: 42
Bhuvan Urgaonkar

Bhuvan Urgaonkar

Penn State University

H-index: 35
Harrison Kim (Hyung Min Kim)

Harrison Kim (Hyung Min Kim)

University of Illinois at Urbana-Champaign

H-index: 33
Prashant Mehta

Prashant Mehta

University of Illinois at Urbana-Champaign

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