Maya Mutic

Currently: Data Scientist, electronRx; Formerly: Research Assistant

Maya is currently a data scientist at electronRx and worked as an Research Assistant in the ZERO Lab from 2023-2024.

Her research involves combining machine learning and optimization methods for the GenX capacity expansion problem. Prior to joining the lab, she was a master’s student in the department of Operations Research and Financial Engineering at Princeton, and obtained her BS in Applied and Computational Math from Caltech in 2021.

Publications

GenX.jl: a Configurable Power System Capacity Expansion Model for Studying Low-Carbon Energy Futures

Software, 2021

Aneesha Manocha, Filippo Pecci, Gabriel Mantegna, Jacob Schwartz, Jesse Jenkins, Luca Bonaldo, Maya Mutic, Neha Patankar, Qian Luo, Qingyu Xu, Sambuddha Chakrabarti, Wilson Ricks

github.com/GenXProject/GenX.jl

GenX is a highly-configurable, open source electricity resource capacity expansion model that incorporates several state-of-the-art practices in electricity system planning to offer improved decision support for a changing electricity landscape. GenX is a constrained linear or mixed integer linear optimization model that determines the portfolio of electricity generation, storage, transmission, and demand-side resource investments and operational decisions to meet electricity demand in one or more future planning years at lowest cost, while subject to a variety of power system operational constraints, resource availability limits, and other imposed environmental, market design, and policy constraints. GenX features a modular and transparent code structure developed in Julia + JuMP. The model is designed to be highly flexible and configurable for use in a variety of applications from academic research and technology evaluation to public policy and regulatory analysis and resource planning.