ACM e-Energy Paper on Vulnerabilities of Load Forecasting Algorithms

Our work Exploiting Vulnerabilities of Load Forecasting Through Adversarial Attacks was accepted at this year’s ACM e-Energy conference. We looked into the overlooked security issues on load forecasting, which is an essential step in power system operations. With only small attack efforts, severe damages could be made upon normal system operations.

Code is available here.

Grid Science Winter School

I attended the 3rd Grid Science Winter School & Conference held in Santa Fe by Los Alamos National Laboratory. It was a super interesting venue! Great to hear from a lot of inspiring works, gather with old friends during my summer internship, and showed our work on Optimal Control via Neural Networks as a contributed student talk. Also showed one poster on our ongoing work Data-Driven Vulnerabilities of Power Systems Algorithms.

ICLR Paper on Optimal Control via Neural Networks

Our work on control via neural network (preprint on Arxiv available): Optimal Control Via Neural Networks: A Convex Approach was accepted to ICLR 2019. Very exciting theoretical results on showing a class of neural networks being input-convex, which are able to achieve excellent learning plus control performances on tasks such as Mujoco locomotion and building energy management.

Also nominated as an oral paper for the conference, along with a travel award. Featured in media coverage such as Sohu, 机器之心.

SmartGridComm 2018

I attended the SmartGridComm this year held in Aalborg, Denmark on Oct. 29-31, 2018, and presented our work Is Machine Learning in Power Systems Vulnerable? We are interested in the general security issues of applied ML in power networks and smart grids, and call for more rigorous analysis of algorithmic security threatens. Take a look at our Arxiv preprint!