Anthony Degleris

  • Program Year: 4
  • Academic Institution: Stanford University
  • Field of Study: Electrical Engineering
  • Academic Advisor: Abbas El Gamal
  • Practicum(s):
    Argonne National Laboratory (2022)
  • Degree(s):
    B.S. Electrical Engineering, Stanford University, 2020
  • Personal URL:

Summary of Research

As an Electrical Engineering PhD student at Stanford, I plan to study optimization and power systems, with a special emphasis on energy storage and renewable integration. As the power grid becomes more complex and diverse, developing algorithms for operating the grid will be an essential part of meeting the world's growing electricity demand and building a grid that can withstand natural disasters. My research will focus on using tools from optimization and control to develop better algorithms for managing the grid.


[1] A Degleris, N Gillis (2020). A Provably Correct and Robust Algorithm for Convolutive Nonnegative Matrix Factorization. IEEE Transactions on Signal Processing.

[2]* A Williams, A Degleris, Y Wang, S Linderman (2020). Point process models for sequence detection in high-dimensional neural spike trains. Advances in Neural Information Processing Systems.

[3]* A Degleris, L Fuentes Valenzuela, A El Gamal, R Rajagopal (2021). Emissions-aware electricity network expansion planning via implicit differentiation. NeurIPS 2021 Workshop: Tackling Climate Change with Machine Learning.

[4] Y Wang, A Degleris, A Williams, S Linderman (2023). Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture Models. Journal for the American Statistical Association.

[5] LF Valenzuela, A Degleris, A El Gamal, M Pavone, R Rajagopal (2023). Dynamic locational marginal emissions via implicit differentiation. IEEE Transactions on Power Systems.

[6]* E Balogun, S Martin, A Degleris, R Rajagopal (2023). Equitable dynamic electricity pricing via implicitly constrained dual and subgradient methods. IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids.

[7] [in review] LF Valenzuela, A Degleris, A El Gamal, M Pavone, R Rajagopal. Efficiently Computing Locational Marginal Emissions Rates using Dual Decomposition. IEEE SmartGridComm 2024.

[8] [in review] A Degleris, A El Gamal, R Rajagopal. Gradient Methods for Scalable Multi-value Electricity Network Expansion Planning. INFORMS Operations Research.

*Oral Presentation / Best Paper


Stanford Graduate Fellow (2020).
Phi Beta Kappa (2020).
Sigma Xi (2020).
Frederick Emmons Terman Engineering Scholastic Award (2020).
Tau Beta Pi (2018).