Erfan Yazdandoost Hamedani
Dr. Yazdandoost Hamedani is an assistant professor at the University of Arizona, Department of Systems and Industrial Engineering. He received his PhD in industrial engineering and operations research from The Pennsylvania State University. Prior to that, he was a research intern at the Department of Mathematics, Penn State University. He received his BSc degree in mathematics and applications from The University of Tehran, Tehran, Iran. His research interests include distributed optimization, saddle point problems, bilevel optimization, machine learning, and data science. His research focuses on developing and analyzing algorithms for solving convex and non-convex large-scale optimization problems in machine learning and data science problems.
Degrees
- PhD Industrial Engineering and Operations Research
- The Pennsylvania State University, University Park, Pennsylvania, United States
- BS Mathematics
- University of Tehran, Tehran, Iran, Islamic Republic of
Work Experience
- The University of Arizona, Tucson, Arizona (2020 - Ongoing)
Interests
Teaching
Optimization, Operations Research, Machine Learning, Probability
Research
Large-scale Optimization, Distributed Optimization, Bilevel Optimization, Saddle point problems, Machine Learning, and Dynamical Systems
Selected Publications
Journals/Publications
- Yazdandoost Hamedani, E., & Jalilzadeh, A. (2023). A Stochastic Variance-reduced Accelerated Primal-dual Method for Finite-sum Saddle-point Problems. Computational Optimization and Applications.
- Yazdandoost Hamedani, E., & Aybat, N. S. (2021). A Primal-dual Algorithm with Linesearch for General Convex-Concave Saddle Point Problems. SIAM Journal on Optimization, 31(2), 1299-1329. doi:doi.org/10.1137/18M1213488
- Yazdandoost Hamedani, E., & Aybat, N. S. (2021). A decentralized primal-dual method for constrained minimization of a strongly convex function. IEEE Transaction on Automatic Control. doi:10.1109/TAC.2021.3130082
- Aybat, N. S., & Yazdandoost Hamedani, E. (2019). A distributed ADMM-like method for resource sharing over time-varying networks. SIAM Journal on Optimization.
Proceedings Publications
- Yazdandoost Hamedani, E., & Aybat, N. S. (2017, May). Multi-agent constrained optimization of a strongly convex function over time-varying directed networks. In 2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton).
- Yazdandoost Hamedani, E., & Aybat, N. S. (2017, May). Multi-agent constrained optimization of a strongly convex function. In 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP).
- Aybat, N. S., & Yazdandoost Hamedani, E. (2016, Aug). A primal-dual method for conic constrained distributed optimization problems. In Advances in Neural Information Processing Systems.
- Aybat, N. S., & Yazdandoost Hamedani, E. (2016, May). Distributed primal-dual method for multi-agent sharing problem with conic constraints. In 2016 50th Asilomar Conference on Signals, Systems and Computers.
Presentations
- Yazdandoost Hamedani, E. (2022). Primal-Dual Methods for Convex-concave Saddle-Point Problems: Acceleration and Variance Reduction. MIDO seminar at Rensselaer Polytechnic Institute, Department of Mathematics. Virtual.
- Yazdandoost Hamedani, E. (2022). Randomized Block coordinate Primal-Dual Methods for Saddle-point Problems. International Conference on Continuous Optimization. Bethlehem, PA.
- Yazdandoost Hamedani, E. (2022). Variance Reduced Primal-Dual Method for Convex-concave Saddle-point Problems. IISE annual meeting. Seattle, WA.