Mehdi Karimi
Assistant Professor
Mathematics
Office
STV Stevenson Hall 309b
Office Phone
Email
- About
- Education
- Research
Current Courses
260.001Discrete Mathematics
205.001Introduction to Deep Learning
147.001Calculus III
260.001Discrete Mathematics
285.003Honors Undergraduate Research I
499.001Independent Research For The Master's Thesis
Research Interests & Areas
- Optimization and mathematical programming
- Optimization software and applications
- Interplay of optimization and machine learning
- Hyperbolic programming and sum-of-squares techniques
- Optimization in power systems, communications, and smart grids
- Convex analysis
- Interior-point methods
PhD Mathematics/Optimization
University of Waterloo
MS Mathematics/Optimization
University of Waterloo
PhD Electrical Engineering
Sharif University of Technology
MS Electrical Engineering
Sharif University of Technology
BS Electrical Engineering
Isfahan University of Technology
Journal Article
Karimi, M., & Tuncel, L. Domain-Driven Solver (DDS) Version 2.1: a MATLAB-based software package for convex optimization problems in domain-driven form. Mathematical Programming Computation (2023): 1-55.
O’Donnell, B., Sanchez-Pupo, R., Sayedyahossein, S., Karimi, M., Bahmani, M., Zhang, C., Johnston, D., Kelly, J., Wakefield, C., Barr, K., Dagnino, L., & Penuela, S. PANX3 Channels Regulate Architecture, Adhesion, Barrier Function, and Inflammation in the Skin. Journal of Investigative Dermatology 143.8 (2023): 1509-1519.
Karimi, M., & Tuncel, L. Status determination by interior-point methods for convex optimization problems in domain-driven form. Mathematical Programming 194.1-2 (2022): 937-974.
Karimi, M., & Tuncel, L. Primal–Dual Interior-Point Methods for Domain-Driven Formulations. Mathematics of Operations Research 45.2 (2020): 591-621.
Presentations
Efficient Implementation of Interior-Point Methods for Quantum Relative Entropy. SIAM Conference on Optimization (OP23). (2023)
Grants & Contracts
Intelligent Modeling and Parameter Selection in Distributed Optimization for Power Networks. National Science Foundation. Federal. (2024)