Research Assistant, PhD Candidate, Network Science Institute, Northeastern University
Network Science | Statistical Physics | Deep Learning | Data Science
I am a PhD candidate at Northeastern University, Center for Complex Network Research, Network Science Institute.
My fields of interest include network science theory, physical networks, network embedding, brain networks, network symmetry, and Deep Learning. My projects include physical networks theory and real-world applications, done with my advisor Prof. Albert-Laszlo Barabasi. We are interested in developing new methods and new perspectives to study physical networks. In addition to my dissertation research, other areas that interest me include network modeling, network symmetry, and deep learning. One of the studies that I have conducted is analyzing the effect of the thresholding procedure in network constructions, which is common used in many network datasets. In another study, my collaborators and I designed a new convolutional neural network that is capable of learning potential symmetries in a dataset and extract the Lie algebra generators.