Rahman Khorramfar
Senior Postdoctoral Associate
MIT Energy Initiative (MITEI)
&
Laboratory for Information and Decision Systems (LIDS)
Email: khorram@mit.edu CV Google Scholar GitHub LinkedIn
Hi! I am currently a postdoc at MIT working with Saurabh Amin and Dharik Mallapragada. My research is focused on developing advanced optimization and machine learning tools for design, planning, and operations of low-carbon resilient energy systems. Prior to MIT, I was a PhD student at NC State University, primarily working on hierarchical decision-making and optimization under uncertainty and their applications in production planning, supply chain, and capacity expansion problems. More about my current research here.
Research Interests
Resilient Infrastructure Planning and Optimization, Risk Analysis, Integration of Distributed Energy Resources, Large-scale Optimization, Transportation, Supply Chain.
Education
PhD in Industrial and Systems Engineering, North Carolina State University, 2017-2021
Msc in Industrial Engineering, Sharif University of Technology, 2014-2016
Bsc in Industrial Engineering, University of Tabriz, 2010-2014
Publications
Published or Accepted
Khorramfar, R., Santoni-Colvin, M., Amin, S., Norford, L.K., Botterud, A. and Mallapragada, D., 2023. Cost-effective Planning of Decarbonized Power-Gas Infrastructure to Meet the Challenges of Heating Electrification. (to appear in Cell Reports Sustainability) [arXiv]
Qiu, L., Khorramfar, R., Amin, S., Howland, M. Decarbonized Energy System Planning with High-Resolution Spatial Representation of Renewables Lowers Cost. Cell Reports Sustainability [link]
Khorramfar, R., Mallapragada, D., & Amin, S. (2024). Electric-gas infrastructure planning for deep decarbonization of energy systems. Applied Energy, 354, 122176. [link] [Preprint]
Khorramfar, R., Özaltın, O.Y., Kempf, K.G. and Uzsoy, R., 2022. Managing Product Transitions: A Bilevel Programming Approach. INFORMS Journal on Computing, 34(5), pp.2828-2844. [link]
Brenner, A., Khorramfar, R. and Amin, S., 2023, May. Learning Spatio-Temporal Aggregations for Large-Scale Capacity Expansion Problems. In Proceedings of the ACM/IEEE 14th International Conference on Cyber-Physical Systems (with CPS-IoT Week 2023) (pp. 68-77). [link]
Brenner, A., Khorramfar, R., Mallapragada, D. and Amin, S., 2022. Graph Representation Learning for Energy Demand Data: Application to Joint Energy System Planning under Emissions Constraints. AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges. [link]
Lado, E.N., Khorramfar, R., Amin, S. Optimal Storage Investment for Aggregators under Power Disruptions. (accepted) 2025 American Control Conference
Submitted/Draft
Khorramfar, R., Mallapragada, D., Amin, S. Power-Gas Infrastructure Planning under Supply and Demand Uncertainty via Distributionally Robust Optimization Model. [available upon request]
Brenner, A., Khorramfar, R., Amin, S. A Deep Generative Learning Approach for Two-stage Adaptive Robust Optimization. [arXiv]
Brenner, A., Khorramfar, R., Mallapragada, D. and Amin, S. Learning-assisted Stochastic Capacity Expansion Planning: A Bayesian Optimization Approach. [arXiv]
Khorramfar, R., Özaltın, O.Y., Uzsoy, R. and Kempf, K.G., Coordinating Resource Allocation during Product Transitions Using a Multifollower Bilevel Programming Model. [arXiv]
Under Preparation
High-resolution variable renewable energy siting under climate uncertainty
Private Incentives for Resilience Provision
Deep decarbonization pathways under commercial building electrification scenarios
Open-source Software
I developed JPoNG model which is an optimization model for joint planning of power and NG infrastructure with a resolved representation of spatial, temporal, and technological system operation. The model is implemented as open-source software in Python with Gurobi solver to systematically analyze energy transition problems.
I am planning to release the software format of the code in early 2024. The full formulation of the model is provided in this and this paper.