Samuel Talkington
Ph.D. Student, Georgia Institute of Technology
I am a Ph.D. student in the School of Electrical and Computer Engineering at the Georgia Institute of Technology, and I am fortunate to be advised by Daniel K. Molzahn.
My research develops efficient algorithms for societal-scale problems in electric power networks. At the interface of statistics and optimization, I exploit domain structure—such as power flow physics, spectral graph properties, and communication protocols—to improve scalability and reliability. My broader vision is to treat randomness as a resource to transform how the grid is operated, drawing on tools such as randomized numerical linear algebra.
I am privileged for my work to be supported by the NSF Graduate Research Fellowship (GRF), the NSF AI Institute for Advances in Optimization (AI4OPT), the Georgia Tech Supporting Teaching ExpERience (STEER) fellowship, Sandia National Laboratories, the Georgia Tech Strategic Energy Institute (SEI), the Energy Policy and Innovation Center (EPIcenter) at Georgia Tech, and the Georgia Tech Steve W. Chaddick and Szalm Fellowships in ECE.
On the academic job market
I'm seeking a tenure-track faculty position in electrical engineering, computer science, operations research, or a related field.
Blog
Linearizing the Power Flow EquationsUpcoming events: 2025 INFORMS Annual Meeting, 2025 NAPS Conference, and 2026 ICERM RandNLA Workshop
Harvard PAI Seminar: "Randomness as a Resource: Scalable Algorithms for Grid Decision-Making"
Generating Random Graphs and Laplacians in Julia
all posts
Recent Research
Efficient Network Reconfiguration by Randomized Switching
Samuel Talkington, Dmitrii M. Ostrovskii, and Daniel K. Molzahn
arXiv / code / bibTeX
Admittance Matrix Concentration Inequalities for Understanding Uncertain Power Networks
Samuel Talkington, Cameron Khanpour, Rahul K. Gupta, Sergio A. Dorado-Rojas, Daniel Turizo, Hyeongon Park, Dmitrii M. Ostrovskii, and Daniel K. Molzahn
arXiv / bibTeX
Differentiating Through Power Flow Solutions for Admittance and Topology Control
Samuel Talkington, Daniel Turizo, Sergio A. Dorado-Rojas, Rahul K. Gupta, and Daniel K. Molzahn
arXiv / bibTeX
Error Bounds for Radial Network Topology Learning from Quantized Measurements
Samuel Talkington, Aditya Rangarajan, Pedro A. de Alcântara, Line Roald, Daniel K. Molzahn, and Daniel R. Fuhrmann
arXiv / bibTeX
VArsity: Can Large Language Models Keep Power Engineering Students in Phase?
Samuel Talkington and Daniel K. Molzahn
57th North American Power Symposium (NAPS 2025) / bibTeX
Covert Distribution Load Tripping Attacks
Betelihem Kebede Ashebo, Samuel Talkington, Saman Zonouz, and Daniel K. Molzahn
57th North American Power Symposium (NAPS 2025) / code / bibTeX
Strategic Electric Distribution Network Sensing via Spectral Bandits
Samuel Talkington, Rahul Gupta, Richard Asiamah, Paprapee Buason, and Daniel K. Molzahn
IEEE CDC 2024 / arXiv / slides / bibTeX
Locational marginal burden: Quantifying the equity of optimal power flow solutions
Samuel Talkington∗, Amanda West∗, and Rabab Haider (∗ equal contribution)
ACM e-Energy 2024 / arXiv / video / slides / bibTeX
all papers
Teaching
Your Institution Here
Computational Power System Analysis (Fall 202X)
Georgia Tech
ECE 4320: Power System Analysis (Spring 2025)
ECE 2020: Digital System Design (Fall 2024)