Zhenming Liu is an assistant professor in the Computer Science Department at the College of William & Mary. He received his PhD in the theory of computation from Harvard University in 2012. He was a postdoctoral research associate at Princeton University in 2012 and 2014, and an alpha modeler at Two Sigma Investments from 2014 through 2016.
Dr Liu received the Best Student Paper Award at ECML/PKDD 2010 and the Best Paper Award Runner-up at IEEE INFOCOM 2015. Currently, Dr Liu is using tools from applied probability, theoretical computer science, and optimisation to build scalable systems for analysing massive networked datasets.
His lab focuses on building algorithmic foundations for large-scale end-to-end machine learning solutions. His research programme consists of two thrusts:
1. Computational learning theory for graphs. Graphs, the generic objects capturing the relationship/interaction between entities, are ubiquitous in data modelling and analysis. For instance, analysing the interactions in a social network can help researchers predict the circulation of (fake) news and its impacts. His lab is designing a suite of computationally efficient, statistically sound, learning algorithms for graphs, optimised for applications in social network analysis, topical models, and financial news analysis.
2. Large-scale learning system design and delivery. Two barriers hinder the delivery of artificial intelligence to the public. One is the lack of computing infrastructure support for ‘applications in the wild’ (in terms of scalability and throughput) and the other is the computational weakness of today's front-end devices. His lab is designing low-cost systems that can train on peta-scale data, and systems that can deliver high-throughput machine learning services.
In addition, to bridge the gap between theory and practice in machine learning Zhenming is collaborating with industry. Some of his current and past efforts include collaborations with AT&T on customer care related analysis, collaborations with Activision Blizzard to analyse players’ in-game behaviour and developing financial models for equity markets.