Event Overview:

Many pressing questions in science are larger than the resource researchers have to solve them. These questions require the ability to consult scattered information, much of which are not organized in a structured form. Enters Ce Zhang, PaleoDeepDive creator, and Stanford University postdoctoral researcher. Zhang developed the program PaleoDeepDive, a machine-reading system for paleontologists. PaleoDeepDrive was featured in Nature magazine and is widely used in fields ranging from paleobiology, genomics, and event in anti-human trafficking.

Zhang research focuses on building a data management system for machine learning workloads with the goal to simplify the complex process of building KBC systems. He will explain PaleoDeepDive's framework, and its ultimate goal to allow scientists to build a KBC system without worrying about any algorithmic, performance, or scalability issues. Zhang will discuss the key challenge in building a high-quality KBC system, dealing with information that is both diverse in type and size, and underlying techniques to boost the range of machine learning workloads. 

About Ce Zhang:

Ce is a postdoctoral researcher in Computer Science at Stanford University. He is working with Christopher Ré on data management and database systems. With the indispensable help of many collaborators, his doctoral work produced the system PaleoDeepDive, a trained data system for automatic knowledge-base construction. As part of his doctoral thesis, he led the research efforts that won the 2014 SIGMOD Best Paper Award and was invited to the “Best of VLDB 2015” special issue; PaleoDeepDive, a machine-reading system for paleontologists, was featured in Nature magazine, and he also led the Stanford team that produced the top-performing machine-reading system for TAC-KBP 2014 slot-filling evaluations using DeepDive. Ce obtained his doctorate. from the University of Wisconsin-Madison, advised by Christopher Ré, and his Bachelor of Science degree from Peking University, advised by Bin Cui.