Wenting Li is a Postdoc Research Associate at Los Alamos National Laboratory (LANL). She obtained the Ph.D. degree in Electrical Computer and System Engineering (ECSE) and Master degree in Applied Mathematics at the Rensselaer Polytechnics Institute (RPI) in Dec. 2019. Her supervisor is Prof. Meng Wang. Her doctoral thesis is about developing machine/deep learning based algorithms to enhancing power systems monitoring and protection. Before she came to RPI in 2015, she worked as a research assistant in the Wind Farm Research Center (WFRC) at Shanghai Jiao Tong University (SJTU) under the supervison of Prof. Xu Cai. In 2013, she received her B.S. degree in Elcetrical Engineering and Automation from Harbin Institute of Technology (HIT).
- 03/11/2021: Talk on a latest paper on neural networks verification for the optimization machine learning (OPTML) reading group.
- 02/24/2021: A lighting talk for the DisrupTech: Robust fault location through graph-based learning at low label rates
- 08/03/2020: Big Data Analytics Sessions during the 2020 PES general meeting : Identifying Overlapping Successive Events Using a Shallow Convolutional Neural Network here
- 07/14/2020: Los Alamos National Laboratory Postdoc Seminar: Physics-informed Neural Networks for High Impedance Fault Detection.
- 11/01/2019: CURENT Power and Energy Seminar: Real-time and Agile Data-driven Approaches Enabling Power Grids to be Smart. here
- Physics-informed machine learning
- Graph learning and graph neural networks
- Robust optimization and verified neural networks
- Feature extraction from high-dimensional data
- Deep learning models Design (CNN, RNN, LSTM, Autoencoder)