Steering Committee/Affiliated faculty

headshot of Neha Patankar

Neha Patankar

Assistant Professor

Systems Science and Industrial Engineering


Neha Patankar's expertise is in multi-attribute computational modeling and decision-making for energy transition, focusing on the rapidly evolving electricity sector. Her research supports energy policy decisions under deep techno-economic uncertainty, reveals system-wide technology and resource tradeoffs, and evaluates pathways for economy-wide decarbonization. Before joining Binghamton University, she earned a PhD in Operations Research from North Carolina State University and served as an associate research scholar at Princeton University. During this time, she worked on the REPEAT and Open Energy Outlook projects highlighted by several U.S. news outlets. These projects provide a regular, timely, and independent environmental and economic evaluation of federal energy and climate policies.

Google Scholar


  • Improving the representation of energy efficiency in an energy system optimization model. Patankar, N., Fell, H. G., de Queiroz, A. R., Curtis, J., & DeCarolis, J. F. (2022). Applied Energy.
  • Using robust optimization to inform US deep decarbonization planning. Patankar, N., Eshraghi, H., de Queiroz, A. R., & DeCarolis, J. F. (2022). Energy Strategy Reviews.
  • Least cost energy system pathways towards 100% renewable energy in Ireland by 2050. Yue, X., Patankar, N., Decarolis, J., Chiodi, A., Rogan, F., Deane, J. P., & O’Gallachoir, B. (2020). Energy.
  • Modeling the operational flexibility of natural gas combined cycle power plants coupled with flexible carbon capture and storage via solvent storage and flexible regeneration. Cheng, F., Patankar, N., Chakrabarti, S., & Jenkins, J. D. (2022). International Journal of Greenhouse Gas Control.


  • PhD, North Carolina State University
  • BS, Pune University, India

Research Interests

  • Optimization and modeling
  • Decision-making in complex systems
  • Robust energy transition
  • Power system planning
  • Uncertainty quantification