Lele Wang

Lele Wang is an Assistant Professor in the Department of Electrical and Computer Engineering (ECE) at the University of British Columbia (UBC), Vancouver. He is affiliated with the Mathematics of Information, Learning and Data (MILD) research cluster and the Institute of Applied Mathematics. His research focuses on information-theoretic approaches to data science and machine learning, with contributions to universal compression, coding theory, statistical inference on graphs, and noisy computing. Before UBC, he held postdoctoral positions at Stanford University, Tel Aviv University, and as an NSF Center for Science of Information fellow.

Education: PhD in Electrical Engineering from the University of California San Diego (2015), B.E. in Academic Talent Program from Tsinghua University (2009).

Research Interests: Developing tools in information theory, coding theory, high-dimensional statistics, combinatorics, random graphs, and deep generative models for data-driven challenges. Notable works include universal graph compression for stochastic block models and coding-theoretic approaches to distributed machine learning.

Awards: Recipient of the 2017 IEEE Information Theory Society Thomas M. Cover Dissertation Award and NSF CSoI Postdoctoral Fellowship. His students have won multiple awards including GSI Awards and NSERC grants.

Mentoring: Prioritizes student development in critical and creative thinking, offering courses in real analysis, probability theory, and theoretical frameworks. Hosts round-robin group meetings inspired by Thomas Cover to foster concise communication and problem-solving skills.