Lele Wang

Assistant Professor

Research Interests

Coding theory
Communication theory
Graph theory
information theory
Mathematical data science
Practical low-complexity algorithms for communication and compression
Theoretical limits on information flow over networks

Relevant Degree Programs


Graduate Student Supervision

Master's Student Supervision

Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.

Universal graph compression: stochastic block models (2021)

Motivated by the prevalent data science applications of processing and mining large-scale graph data such as social networks, web graphs, and biological networks, as well as the high I/O and communication costs of storing and transmitting such data, this thesis investigates lossless compression of data appearing in the form of a labeled graph. In particular, we consider a widely used random graph model, stochastic block model (SBM), which captures the clustering effects in social networks. An information-theoretic universal compression framework is applied, in which one aims to design a single compressor that achieves the asymptotically optimal compression rate, for every SBM distribution, without knowing the parameters of the SBM that generates the data. Such a graph compressor is proposed in this thesis, which universally achieves the optimal compression rate for a wide class of SBMs with edge probabilities ranging from $O(1)$ to $\Omega(1/n^{2-\e})$ for any $0
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