Afrooz Jalilzadeh
 Assistant Professor, Systems and Industrial Engineering
 Member of the Graduate Faculty
 Assistant Professor, Applied Mathematics  GIDP
 (520) 6212342
 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
202425 Courses

Deterministic Oper Rsrch
SIE 340 (Fall 2024) 
Dissertation
SIE 920 (Fall 2024) 
Independent Study
SIE 599 (Fall 2024)
202324 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)
202223 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)
202122 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)
202021 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 LargeScale 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 Variancereduced Accelerated Primaldual Method for Finitesum Saddlepoint 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 samplesize accelerated proximal methods for nonsmooth stochastic convex programs. Stochastic Systems.
 Jalilzadeh, A. (2021). PrimalDual Incremental Gradient Method for Nonsmooth and Convex Optimization Problems. Optimization Letters.
 Jalilzadeh, A., Nedich, A., Shanbhag, U. V., & Yousefian, F. (2021). A variable samplesize stochastic quasiNewton 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 PrimalDual Methods with Adaptive Step Sizes. In Artificial Intelligence and Statistics (AISTATS).
 Alizadeh, Z., Otero, B. M., & Jalilzadeh, A. (2022). An Inexact VarianceReduced Method For Stochastic QuasiVariational Inequality Problems With An Application In Healthcare. In 2022 Winter Simulation Conference (WSC).
 Boroun, M., & Jalilzadeh, A. (2021). InexactProximal Accelerated Gradient Method for Stochastic Nonconvex Constrained Optimization Problems. In 2021 Winter Simulation Conference (WSC).
 Jalilzadeh, A., & Shanbhag, U. V. (2019). Smoothed Firstorder Algorithms for Expectationvalued Constrained Problems. In 2019 53rd Annual Conference on Information Sciences and Systems (CISS).More infoWe consider the development of firstorder 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 variancereduced smoothed firstorder scheme for resolving such a problem and derive rate statements for such a scheme.
 Jalilzadeh, A., & Shanbhag, U. V. (2019, December). A proximalpoint algorithm with variable samplesizes (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 samplesize stochastic quasiNewton method for smooth and nonsmooth stochastic convex optimization. In 2018 IEEE Conference on Decision and Control (CDC).
 Jalilzadeh, A., & Shanbhag, U. V. (2016, December). egVSSA: An extragradient variable samplesize stochastic approximation scheme: Error analysis and complexity tradeoffs. In 2016 Winter Simulation Conference (WSC).
Presentations
 Jalilzadeh, A. (2022, Summer). Complexity Guarantees for Nonlinearly Constrained Nonsmooth Stochastic ConvexConcave Minimax Optimization. International Conference on Continuous Optimization (ICCOPT).
 Jalilzadeh, A. (2021, Fall). PrimalDual Incremental Gradient Method for Nonsmooth and Convex Optimization Problems. INFORMS Annual Meeting.
 Jalilzadeh, A. (2020, Fall). Presenting "Iteration Complexity Of Randomized Primaldual Methods For Convexconcave Saddle Point Problems". INFORMS annual meeting.
 Jalilzadeh, A. (2019, Fall). Rate Analysis For Variancereduced Stochastic Quasinewton 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 SamplesizeStochastic Mirrordescent and Fistalike Schemes for Nonsmooth Stochastic Optimization. INFORMS annual meeting.
Poster Presentations
 Jalilzadeh, A. (2016, June). A Variable SampleSize Stochastic Approximation Scheme (VSSA) : Rate analysis and Complexity Tradeoffs. ICML: Optimization Methods for the Next Generation of Machine Learning.