Afrooz Jalilzadeh
- Assistant Professor, Systems and Industrial Engineering
- Member of the Graduate Faculty
- Assistant Professor, Applied Mathematics - GIDP
- (520) 621-2342
- Engineering, Rm. 318B
- Tucson, AZ 85721
- afrooz@arizona.edu
Biography
Dr. Jalilzadeh is an assistant professor in the Department of Systems and Industrial Engineering at The University of Arizona. She is also a member of the Applied Mathematics GIDP and Statistics GIDP. She received her bachelor's degree in Mathematics from the University of Tehran and earned her Ph.D. in Industrial Engineering and Operations Research from Pennsylvania State University. Her research is focused on the design, analysis, and implementation of stochastic approximation methods for solving stochastic optimization and variational inequality problems, with applications in machine learning, game theory, and power systems. She leads the Optimization and Mathematical Analysis (OPTIMA) lab at UofA https://sites.arizona.edu/afrooz
Degrees
- Ph.D. Industrial Engineering and Operations Research
- The Pennsylvania State University, University Park, Pennsylvania, United States
- B.S. Mathematics
- The University of Tehran, Iran, Islamic Republic of
Work Experience
- The University of Arizona, Tucson, Arizona (2020 - Ongoing)
Awards
- Teacher of The Year
- College of Engineering, University of Arizona, Spring 2022
- James E. Marley Graduate Fellowship in Engineering
- College of Engineering, The Pennsylvania State University, Spring 2020
- Max and Joan Schlienger Graduate Scholarship
- College of Engineering, The Pennsylvania State University, Spring 2019
- Third Place winner in poster competition
- INFORMS, Fall 2018
- H.Marcus Dean’s Chair in Engineering Scholarship
- College of Engineering, The Pennsylvania State University, Fall 2015
- University Graduate Fellowship (UGF)
- The Pennsylvania State University, Fall 2015
Interests
Teaching
Linear and Nonlinear Programming,Stochastic Optimization,Probability and Statistics
Research
Stochastic optimization,Variational inequalities and Nash games,Risk averse optimization,Machine Learning,Healthcare optimization
Courses
2024-25 Courses
-
Dissertation
SIE 920 (Spring 2025) -
Dissertation
STAT 920 (Spring 2025) -
Optimization for ML
SIE 449 (Spring 2025) -
Optimization for ML
SIE 549 (Spring 2025) -
Deterministic Oper Rsrch
SIE 340 (Fall 2024) -
Dissertation
SIE 920 (Fall 2024) -
Independent Study
SIE 599 (Fall 2024)
2023-24 Courses
-
Dissertation
SIE 920 (Summer I 2024) -
Math Foundation Of SIE
SIE 270 (Summer I 2024) -
Special Topics in SIE
SIE 496 (Spring 2024) -
Special Topics in SIE
SIE 596 (Spring 2024) -
Deterministic Oper Rsrch
SIE 340 (Fall 2023) -
Directed Research
SIE 492 (Fall 2023) -
Dissertation
SIE 920 (Fall 2023)
2022-23 Courses
-
Math Foundation Of SIE
SIE 270 (Summer I 2023) -
Directed Research
SIE 492 (Spring 2023) -
Dissertation
SIE 920 (Spring 2023) -
Math Foundation Of SIE
SIE 270 (Spring 2023) -
Deterministic Oper Rsrch
SIE 340 (Fall 2022) -
Dissertation
SIE 920 (Fall 2022)
2021-22 Courses
-
Directed Research
SIE 492 (Spring 2022) -
Research
SIE 900 (Spring 2022) -
Stochastic Modeling I
SIE 520 (Spring 2022) -
Deterministic Oper Rsrch
SIE 340 (Fall 2021) -
Research
SIE 900 (Fall 2021)
2020-21 Courses
-
Directed Research
SIE 492 (Summer I 2021) -
Directed Research
SIE 492 (Spring 2021) -
Research
SIE 900 (Spring 2021) -
Deterministic Oper Rsrch
SIE 340 (Fall 2020) -
Research
SIE 900 (Fall 2020)
Scholarly Contributions
Journals/Publications
- Alizadeh, Z., Jalilzadeh, A., & Yousefian, F. (2023). Randomized Lagrangian Stochastic Approximation for Large-Scale Constrained Stochastic Nash Games”. Optimization Letters.
- Jalilzadeh, A., Yousefian, F., & Ebrahimi, M. (2023). Stochastic Approximation for Estimating the Price of Stability in Stochastic Nash Games. ACM Transactions on Modeling and Computer Simulation.
- Yazdandoost Hamedani, E., & Jalilzadeh, A. (2023). A Stochastic Variance-reduced Accelerated Primal-dual Method for Finite-sum Saddle-point Problems. Journal of Computational Optimization and Applications.
- Bardakci, I. E., Jalilzadeh, A., Lagoa, C., & Shanbhag, U. V. (2022). Probability Maximization via Minkowski Functionals: Convex Representations and Tractable Resolution. Mathematical Programming.
- Jalilzadeh, A., Shanbhag, U. V., Blanchet, J. H., & Glynn, P. W. (2022). Smoothed variable sample-size accelerated proximal methods for nonsmooth stochastic convex programs. Stochastic Systems.
- Jalilzadeh, A. (2021). Primal-Dual Incremental Gradient Method for Nonsmooth and Convex Optimization Problems. Optimization Letters.
- Jalilzadeh, A., Nedich, A., Shanbhag, U. V., & Yousefian, F. (2021). A variable sample-size stochastic quasi-Newton method for smooth and nonsmooth stochastic convex optimization. Mathematics of Operations Research.
- Jalilzadeh, A., Lei, J., & Shanbhag, U. V. (2019). Open Problem—Iterative Schemes for Stochastic Optimization: Convergence Statements and Limit Theorems. Stochastic Systems.
Proceedings Publications
- Yazdandoost Hamedani, E., Jalilzadeh, A., & Aybat, N. S. (2023). Randomized Primal-Dual Methods with Adaptive Step Sizes. In Artificial Intelligence and Statistics (AISTATS).
- Alizadeh, Z., Otero, B. M., & Jalilzadeh, A. (2022). An Inexact Variance-Reduced Method For Stochastic Quasi-Variational Inequality Problems With An Application In Healthcare. In 2022 Winter Simulation Conference (WSC).
- Boroun, M., & Jalilzadeh, A. (2021). Inexact-Proximal Accelerated Gradient Method for Stochastic Nonconvex Constrained Optimization Problems. In 2021 Winter Simulation Conference (WSC).
- Jalilzadeh, A., & Shanbhag, U. V. (2019). Smoothed First-order Algorithms for Expectation-valued Constrained Problems. In 2019 53rd Annual Conference on Information Sciences and Systems (CISS).More infoWe consider the development of first-order algorithms for convex stochastic optimization problems with expectation constraints. By recasting the problem as a solution to a monotone stochastic variational inequality problem, we note that a solution to this problem can be obtained as a solution to an unconstrained nonsmooth convex stochastic optimization problem. We utilize a variance-reduced smoothed first-order scheme for resolving such a problem and derive rate statements for such a scheme.
- Jalilzadeh, A., & Shanbhag, U. V. (2019, December). A proximal-point algorithm with variable sample-sizes (PPAWSS) for monotone stochastic variational inequality problems. In 2019 Winter Simulation Conference (WSC).
- Jalilzadeh, A., Nedich, A., Shanbhag, U. V., & Yousefian, F. (2018, December). A variable sample-size stochastic quasi-Newton method for smooth and nonsmooth stochastic convex optimization. In 2018 IEEE Conference on Decision and Control (CDC).
- Jalilzadeh, A., & Shanbhag, U. V. (2016, December). eg-VSSA: An extragradient variable sample-size stochastic approximation scheme: Error analysis and complexity trade-offs. In 2016 Winter Simulation Conference (WSC).
Presentations
- Jalilzadeh, A. (2022, Summer). Complexity Guarantees for Nonlinearly Constrained Nonsmooth Stochastic Convex-Concave Minimax Optimization. International Conference on Continuous Optimization (ICCOPT).
- Jalilzadeh, A. (2021, Fall). Primal-Dual Incremental Gradient Method for Nonsmooth and Convex Optimization Problems. INFORMS Annual Meeting.
- Jalilzadeh, A. (2020, Fall). Presenting "Iteration Complexity Of Randomized Primal-dual Methods For Convex-concave Saddle Point Problems". INFORMS annual meeting.
- Jalilzadeh, A. (2019, Fall). Rate Analysis For Variance-reduced Stochastic Quasi-newton Schemes For Stochastic Convex Optimization. INFORMS annual meeting.
- Jalilzadeh, A. (2018, Fall). Smoothing and Acceleration for Stochastic Convex Optimization. INFORMS annual meeting.
- Jalilzadeh, A. (2017, Fall). On Variable Sample-sizeStochastic Mirror-descent and Fista-like Schemes for Nonsmooth Stochastic Optimization. INFORMS annual meeting.
Poster Presentations
- Jalilzadeh, A. (2016, June). A Variable Sample-Size Stochastic Approximation Scheme (VSSA) : Rate analysis and Complexity Trade-offs. ICML: Optimization Methods for the Next Generation of Machine Learning.