Jump to navigation

The University of Arizona Wordmark Line Logo White
College of Engineering
Home
  • Home
  • Give Today
  • Contact Us

Search form

  • About
    • Welcome
    • Contact Us
  • Undergrad Programs
    • Admissions
    • Degrees
    • Courses
    • Advising
    • Scholarships & Financial Aid
    • Research & Internships
    • Student Clubs & Organizations
    • ABET Accreditation
  • Grad Programs
    • Admissions
    • On-Campus Degrees
    • Online Degrees
    • Courses
    • Advising
    • Research Focus Areas
    • Funding
  • Research
    • Focus Areas
  • Faculty & Staff
    • Faculty Directory
    • Staff Directory
    • Employee Resources
    • Open Positions
  • Alumni
    • Give Today
  • News & Events
    • SIE News Archive
    • Events
Faculty & Staff
Home / Faculty & Staff / Faculty / Jianqiang Cheng
Jianqiang Cheng
  • jqcheng@arizona.edu
    520.621.2686

    Engineering 123

    Jianqiang Cheng's website
    Full details at profiles.arizona.edu

Jianqiang Cheng

  • Assistant Professor of Systems and Industrial Engineering

Dr. Jianqiang Cheng is an assistant professor in the Department of Systems and Industrial Engineering 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 Maths 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.   

Degrees

  • Ph.D. Computer Science
    • PARIS-SACLAY University, Paris, France
  • B.S. Maths and Applied Maths
    • Shanghai University, Shanghai, Shanghai, China

Interests

Research

Stochastic programming; Robust optimization;Distributionally robust optimization; Semidefinite and copositive optimization; Energy management

Selected Publications

Chapters

  • Fathabad, A. M., Cheng, J., & Pan, K. (2021). Integrated power transmission and distribution systems. In Renewable-Energy-Driven Future(pp 169--199). Academic Press.

Journals/Publications

  • Cheng, J., Kosuch, S., & Lisser, A. (2022). Restricted Shortest Path Problems with Uncertain Delays.
  • Lisser, A., Cheng, J., & Houda, M. (2022). Elliptically distributed joint probabilistic constraints.
  • Cheramin, M., Chen, R. L., Cheng, J., & Pinar, A. (2021). Data-Driven Robust Optimization Using Scenario-Induced Uncertainty Sets. arXiv preprint arXiv:2107.04977.
  • Cheramin, M., Cheng, J., Jiang, R., & Pan, K. (2021). Computationally Efficient Approximations for Distributionally Robust Optimization under Moment and Wasserstein Ambiguity. INFORMS Journal on Computing.
  • Cheramin, M., Saha, A. K., Cheng, J., Paul, S. K., & Jin, H. (2021). Resilient NdFeB magnet recycling under the impacts of COVID-19 pandemic: Stochastic programming and Benders decomposition. Transportation Research Part E: Logistics and Transportation Review, 155, 102505.
  • Dashti, H., Cheng, J., & Krokhmal, P. (2021). Chance-constrained optimization-based solar microgrid design and dispatch for radial distribution networks. Energy Systems, 1--23.
  • Karimi, R., Cheng, J., & Lejeune, M. A. (2021). A Framework for Solving Chance-Constrained Linear Matrix Inequality Programs. INFORMS Journal on Computing, 33(3), 1015--1036.
  • Cheramin, M., Cheng, J., Jiang, R., & Pan, K. (2020). Computationally Efficient Approximations for Distributionally Robust Optimization. Optimization online.
  • Fathabad, A. M., Cheng, J., Pan, K., & Qiu, F. (2020). Data-driven planning for renewable distributed generation integration. IEEE Transactions on Power Systems, 35(6), 4357--4368.
  • Fathabad, A. M., Yazzie, C. B., Cheng, J., & Arnold, R. G. (2020). Optimization of solar-driven systems for off-grid water nanofiltration and electrification. Reviews on Environmental Health, 35(2), 211--217.
  • Bomze, I. M., Cheng, J., Dickinson, P. J., Lisser, A., & Liu, J. (2019). Notoriously hard (mixed-) binary QPs: empirical evidence on new completely positive approaches. Computational Management Science, 16(4), 593--619.
  • Cheng, J., Gicquel, C., & Lisser, A. (2019). Partial sample average approximation method for chance constrained problems. Optimization Letters, 13(4), 657--672.
  • Cheng, J., Chen, R. L., Najm, H. N., Pinar, A., Safta, C., & Watson, J. (2018). Chance-constrained economic dispatch with renewable energy and storage. Computational Optimization and Applications, 70(2), 479--502.
  • Cheng, J., Li-Yang, C. R., Najm, H. N., Pinar, A., Safta, C., & Watson, J. (2018). Distributionally Robust Optimization with Principal Component Analysis. SIAM Journal on Optimization, 28(2), 1817--1841.
  • Gicquel, C., & Cheng, J. (2018). A joint chance-constrained programming approach for the single-item capacitated lot-sizing problem with stochastic demand. Annals of Operations Research, 264(1-2), 123--155.

Proceedings Publications

  • Cheng, J., & Gicquel, C. (2018). Partial Sample Average Approximation Approach for Stochastic Lot-Sizing Problems. In 2018 IISE Annual Conference.
  • Karimi, R., Dabiri, A., Cheng, J., & Butcher, E. A. (2018). Probabilistic-Robust Optimal Control for Uncertain Linear Time-delay Systems by State Feedback Controllers with Memory. In 2018 Annual American Control Conference (ACC).
  • Xu, C., Cheng, J., & Lisser, A. (2015). A Sampling Method to Chance-constrained Semidefinite Optimization. In Proceedings of the International Conference on Operations Research and Enterprise Systems, 75-81.
  • Xu, C., Cheng, J., & Lisser, A. (2015). A Sampling Method to Chance-constrained Semidefinite Optimization. In Proceedings of the International Conference on Operations Research and Enterprise Systems.
  • Xu, C., Cheng, J., & Lisser, A. (2015). Stochastic Semidefinite Optimization Using Sampling Methods. In International Conference on Operations Research and Enterprise Systems, 93-103.
  • Xu, C., Cheng, J., & Lisser, A. (2015). Stochastic Semidefinite Optimization Using Sampling Methods. In International Conference on Operations Research and Enterprise Systems.
  • Cheng, J., Gicquel, C., & Lisser, A. (2014). A modified sample approximation method for chance constrained problems. In SIAM Conference on optimization 2014.
  • Cheng, J., Lisser, A., & Gicquel, C. (2014). A modified sample approximation approach for chance-constrained problems. In PGMO-COPI'14; Conference on Optimization and Practices in Industry.
  • Gicquel, C., & Cheng, J. (2014). Solving a stochastic lot-sizing problem with a modified sample approximation approach.. In 44th International Conference on Computers and Industrial Engineering.
  • Gicquel, C., Cheng, J., & Lisser, A. (2014). A joint chance-constraint programming approach for a stochastic lot-sizing problem. In Proceedings International Workshop on Lot-sizing, IWLS, Porto, Portuga.
  • Nu~nez, B., Adasme, P., Soto, I., Cheng, J., Letournel, M., & Lisser, A. (2014). A chance constrained approach for uplink wireless OFDMA networks. In Communication Systems, Networks & Digital Signal Processing (CSNDSP), 2014 9th International Symposium on.
  • Nuñez, B., Adasme, P., Soto, I., Cheng, J., Letournel, M., & Lisser, A. (2014). A chance constrained approach for uplink wireless OFDMA networks. In Communication Systems, Networks & Digital Signal Processing (CSNDSP), 2014 9th International Symposium on, 754-757.
  • Cheng, J., Gicquel, C., & Lisser, A. (2012). A second-order cone programming approximation to joint chance-constrained linear programs. In International Symposium on Combinatorial Optimization, 71-80.
  • Cheng, J., Gicquel, C., & Lisser, A. (2012). A second-order cone programming approximation to joint chance-constrained linear programs. In International Symposium on Combinatorial Optimization.
  • Cheng, J., Kosuch, S., & Lisser, A. (2012). Stochastic Shortest Path Problem with Uncertain Delays.. In ICORES, 256-264.

Others

  • Safta, C., Cheng, J., Chen, R. L., Pinar, A., Najm, H. N., & Watson, J. (2022). Surrogate-based model for optimization under uncertainty..
  • Fathabad, A. M., Cheng, J., Pan, K., & Yang, B. (2021). Tight Conic Approximations for Chance-Constrained AC Optimal Power Flow.
  • Cheng, J. (2013). Stochastic Combinatorial Optimization.

Awards

  • NSF CAREER Award
    • National Science Foundation, Spring 2022
  • Science Foundation Arizona's 2017 Bisgrove scholar
    • Science Foundation Arizona (SFAz), Spring 2017
  • FACULTY & STAFF
  • Faculty Directory
  • Staff Directory
  • Employee Resources
  • Open Positions
  • Employee Resources
The University of Arizona
Department of Systems & Industrial Engineering
1127 E. James E. Rogers Way
P.O. Box 210020
Tucson, AZ 85721-0020
520.621.6551

Facebook LinkedIn Instagram

 


University Privacy Statement

© 2022 The Arizona Board of Regents on behalf of The University of Arizona.