James Y. Chen

MIT EECS

Cambridge, MA

[first name]yc AT mit DOT edu

Welcome to my website!

I’m James, a third-year PhD student in MIT EECS advised by Priya Donti. My research explores how to overcome the scarcity of real-world grid data as we seek to translate the successes of modern machine learning to power grid applications. Questions I think about include:

How can we leverage our understanding of grid physics to make the most of the data we do have?
How can we develop models that effectively use imperfect, multimodal data?
How can grid operators and stakeholders make more data available without compromising security or privacy?

I received my SM in EECS from MIT in May 2026, where I was fortunate to be co-advised by Ali Jadbabaie. I graduated from Caltech in June 2024 with a Bachelor of Science in Electrical Engineering. I worked on enforcing real-world constraints in learning-augmented online optimization with Adam Wierman, as well as non-asymptotic information theory with Victoria Kostina.

selected publications

Communication-Limited Multi-Agent Grid Congestion Management via Differentiable Optimization

James Y Chen, Stéphane Drobot, Lucas Saludjian, and 4 more authors

In Proceedings of the 17th ACM International Conference on Future and Sustainable Energy Systems (ACM e-Energy), Banff, Canada, 2026

Abs HTML

While there have been many recent advances in multi-agent grid control, they often rely on more communication infrastructure than is available in existing real-world transmission systems. To address this, we present a novel framework for multi-agent grid congestion management inspired by the communication architecture used by RTE, the French transmission system operator. Our framework considers communication-limited architectures in which a central coordinator is only able to periodically provide information to an ensemble of local optimization-based controllers; these local controllers do not communicate, but are nonetheless dynamically coupled. The goal of the coordinator is to provide signals that limit adverse interactions between local controllers, and to ensure that the system as a whole minimizes operational costs and violations of thermal limits. To do so, we leverage the structure of the congestion management problem to expose a parameterized family of locally-verifiable constraints for each controller that jointly imply global constraint satisfaction. The central coordinator then assigns constraint values from this family to the local controllers, with the goal of minimizing the resultant cost over the joint closed-loop trajectory under forecasted scenarios of future disturbances. We frame this as a bi-level problem, and use recent advances in differentiable optimization to find an approximate solution. We demonstrate our method on an IEEE 118-node system partitioned into three control areas. Our results demonstrate that our approach significantly lowers cost compared to RTE’s existing baseline. Furthermore, despite the significant communication constraints, our framework achieves costs comparable to the optimal cost under perfect information of future disturbances and with no restrictions on communication.