Tong Huang is an Assistant Professor in the Department of ECE at San Diego State University. Before joining SDSU, he was a postdoctoral associate in the Laboratory for Information and Decision Systems (LIDS) at the Massachusetts Institute of Technology (MIT). He received his Ph.D. degree from Texas A&M University in 2021. His industry experience includes an internship with ISO-New England in 2018 and an internship with Mitsubishi Electric Research Laboratories in 2019. As the first author, he received two Best Paper Awards at the 2020 IEEE PES General Meeting and the 54-th Hawaii International Conference on System Sciences. His research focuses on cyber-physical resilience enhancement of power electronics-dominated electricity infrastructure via both data-driven and model-based approaches. His CV can be found here.

Announcements

[Jun. 2024] Our paper titled “The Role of Electric Grid Research in Addressing Climate Change” has been accepted in Nature Climate Change. The preprint is here.

[Oct. 2023] Our paper titled “Detection of Cyber Attacks in Grid-Tied PV Systems Using Dynamic Watermarking” is accepted by IEEE Transactions on Industry Applications. See here for the full paper.

[Sep. 2023] Our paper titled “Robust Dynamic Watermarking for Cyber-Physical Security of Inverter-Based Resources in Power Distribution Systems” is accepted by IEEE Transactions on Industrial Electronics. See here for the full paper.

[Aug. 2023] As the lead PI, Dr. Huang received the NSF ASCENT Award ($1.5M). The project aims to boost cyber and physical resilience of networked IBRs. Thanks, NSF!

[Aug. 2023] As a keynote speaker, Dr. Huang gave a talk at SPP 2023 Tech Expo in Kansas City, MO. [Agenda]

[Jul. 2023] As the first author, I received the prestigious IEEE Power and Energy Society (PES) Technical Committee Prize Paper Award from the IEEE PES Analytic Methods for Power Systems Committee. See here for the prize paper. [News]

[Apr. 2023] Our paper titled “Cyber-resilient Automatic Generation Control for Systems of Microgrids” has been accepted by IEEE Transactions on Smart Grid.

[Dec. 2022] Our paper titled “Accelerating the Electric Grid Carbon Neutral Transition through Domain-tailored Artificial Intelligence” has been accepted by Pattern (Cell Press).

[Nov. 2022] Our paper titled “On an Information and Control Architecture for Future Electric Energy Systems” has been accepted by Proceedings of the IEEE. [Arxiv]

[Nov. 2022] Dr. Huang was invited to give a talk at the MAE Seminar Series organized by UCSD.

[Jun. 2022] Our paper titled “Massively Digitized Power Grid: Opportunities and Challenges from Use-inspired AI” has been accepted by Proceedings of the IEEE. [Arxiv]

[Sep. 2021] Our paper titled “A Neural Lyapunov Approach to Transient Stability Assessment of Power Electronics-interfaced Networked Microgrids” has been accepted by IEEE Transactions on Smart Grid. [Arxiv]

[Sep. 2021] Our paper titled “Enabling Secure Peer-to-peer Energy Transaction through Dynamic Watermarking in Future Distribution Grids” has been published in IEEE Electrification Magazine. [Link]

[Aug. 2021] Our paper titled “Distributed Learning-based Stability Assessment for Large Scale Networks of Dissipative Systems” has been accepted by 2021 60th Conference on Decision and Control (CDC2021).

[Aug. 2021] I obtain my Ph.D. degree!

[May 2021] I’ve successfully defended my thesis entitled “Physical and Cyber Anomaly Management in Massively Digitized Power Systems.”

[Jan. 2021] We received the Best Paper Award, (top 0.76% of 1448 papers submitted) in the 54-th Hawaii International Conference on System Sciences (HICSS 54) for our paper titled “A Neural Lyapunov Approach to Transient Stability Assessment in Interconnected Microgrids.” [Link]

[Jun. 2020] We received the Best Paper Award, (top 5% of 1600 papers submitted) in IEEE Power and Energy Society (PES) General Meeting 2020 for our paper titled “A Holistic Framework for Parameter Coordination of Interconnected Microgrids Against Disaster.” [News][Link]

[Mar. 2020] Our paper titled “A Synchrophasor Data-Driven Method for Forced Oscillation Localization Under Resonance Condition” has been accepted by IEEE Transactions on Power Systems. [Link]