Jianqiang Cheng

Associate Professor, Systems and Industrial Engineering | Member of the Graduate Faculty

About

Dr. Jianqiang Cheng is an assistant professor in the Department of Systems and Industrial Engineerin at the University of Arizona UA) Tucson, Arizona. He completed his Ph.D. in 2013 at the PARIS-SACLAY University. He received his B.S. Degree in Math and Applied Maths in Shanghai University. He is particularly interested in Stochastic Programming, Robust Optimization, Semi-definite programming, as well as their applications. Before joining UA, he worked at Sandia National Laboratories as a postdoctroal researcher.

Research Area

optimization, probability, decision making, risk management, power systems, renewable energy, robust optimization, robustness, stochastic processes, stochastic modeling, supply chain management, operations research, data analysis

Collaborative Research: Distributionally Robust Quadratic Optimization and Its Application to Power Systems
Active·2024·$300K·
External
Principal Investigator (PI)
optimization, power systems, collaborative research, robustness, distributional theory
CAREER: Risk-Averse Decision Making via Chance-Constrained Programming for Power Systems
Active·2022·$500K·
External
Principal Investigator (PI)
decision making, power systems, risk management, programming, uncertainty
lll: Small: Learning to Hash Information Networks
Active·2020·$199.8K / $499.6K·
External
Co-Investigator (COI)
machine learning, hashing, information networks, data mining, computer science
Conic Programming Reformulations and Relaxations for Quadratically Constrained and Quadratic Programs
2020·$150K·
External
Principal Investigator (PI)
optimization, conic programming, quadratic programs, reformulations, relaxations
NITC: Data-Driven Optimization for E-Scooter System Design
2020·$54.8K / $67.6K·
External
Principal Investigator (PI)
data-driven optimization, e-scooter system, design, data analysis, transportation

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Recent

Resilient NdFeB magnet recycling under the impacts of COVID-19 pandemic: Stochastic programming and Benders decomposition

2021

magnet recycling, covid-19 impact, stochastic programming, benders decomposition, ndfeb magnet

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UA Researchers Win Four of Five State Bisgrove Scholar Awards
2017

Mathematical Foundations of Systems and Industrial Engineering
Fundamentals of Optimization

Jianqiang Cheng

About

Research Area

Grants(7)

Collaborative Research: Distributionally Robust Quadratic Optimization and Its Application to Power Systems

CAREER: Risk-Averse Decision Making via Chance-Constrained Programming for Power Systems

lll: Small: Learning to Hash Information Networks

Conic Programming Reformulations and Relaxations for Quadratically Constrained and Quadratic Programs

NITC: Data-Driven Optimization for E-Scooter System Design

Publications(40)

DC-Link Capacitance Design for Cascaded H-Bridge STATCOM With Sum of Squares Formulation

Data-Driven Chance-Constrained Planning for Distributed Generation: A Partial Sampling Approach

Submodule Capacitor Sizing for Cascaded H-Bridge STATCOM with Sum of Squares Formulation

Asymptotically Tight Conic Approximations for Chance-Constrained AC Optimal Power Flow

Chance-constrained optimization-based solar microgrid design and dispatch for radial distribution networks

Data-Driven Robust Optimization Using Scenario-Induced Uncertainty Sets

Resilient NdFeB magnet recycling under the impacts of COVID-19 pandemic: Stochastic programming and Benders decomposition

Computationally Efficient Approximations for Distributionally Robust Optimization under Moment and Wasserstein Ambiguity

Optimization of solar-driven systems for off-grid water nanofiltration and electrification

Data-driven planning for renewable distributed generation integration

Collaborations(21)

Daniel Apai

Lisa O'Neill

Yao-jan Wu

Jian Liu

Ashley Langer

News(1)

UA Researchers Win Four of Five State Bisgrove Scholar Awards

Courses(2)

Mathematical Foundations of Systems and Industrial Engineering

Fundamentals of Optimization