Afrooz Jalilzadeh
- Assistant Professor, Systems and Industrial Engineering
- Member of the Graduate Faculty
Contact
- (520) 621-2342
- Engineering, Rm. 318B
- Tucson, AZ 85721
- afrooz@email.arizona.edu
Biography
Dr. Jalilzadeh is an assistant professor at The University of Arizona, Department of Systems and Industrial Engineering. She received her bachelor's degree in Mathematics from the University of Tehran and has a 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 convex optimization and stochastic variational inequality problems with applications in machine learning, game theory, and power systems.
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
- 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
Research
Stochastic optimization,Variational inequalities and Nash games,Risk averse optimization,Machine Learning,Healthcare optimization
Teaching
Linear and Nonlinear Programming,Stochastic Optimization,Probability and Statistics
Courses
2022-23 Courses
-
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
- 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. (2020). 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
- Boroun, M., & Jalilzadeh, A. (2021). Inexact-Proximal Accelerated Gradient Method for Stochastic Nonconvex Constrained Optimization Problems. In Winter Simulation Conference.
- 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. (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.
