Eric Xing is an assistant professor in the Machine Learning Department, the Language Technology Institute, and the Computer Science Department within the School of Computer Science at Carnegie Mellon University. His principal research interests lie in the development of machine learning and statistical methodology; especially for building quantitative models and predictive understandings of the evolutionary mechanism, regulatory circuitry, and developmental processes of biological systems; and for a wide spectrum of problems in AI involving automated learning, reasoning, and decision-making in open, evolving possible worlds.  Professor Xing received his B.S. in Physics from Tsinghua University, his first Ph.D. in Molecular Biology and Biochemistry from Rutgers University under C.S. Yang, and then his second Ph.D. in Computer Science from UC Berkeley under Michael Jordan, Richard Karp and Stuart Russell. He has been a member of the faculty at Carnegie Mellon University since 2004, and his current work involves, 1) graphical models, Bayesian approaches, inference algorithms, and learning theories for analyzing and mining high-dimensional, longitudinal, and relational data; 2) computational and comparative genomic analysis of biological sequences, systems biology investigation of gene regulation, and statistical analysis of genetic variation, demography and linkage (to diseases); and 3) application of statistical learning in text/image mining, vision, and machine translation. 

报告时间：
May 31(Thursday): 3:30pm- 5:00pm
June 1(Friday): 2:00pm- 3:30pm
June 4(Monday): 2:00pm- 3:30pm
June 7(Thursday):3:30pm- 5:00pm 

内容简介：  

The past decade has seen a growing trend of applying probability theory to machine intelligence systems that deal with complex real-world data with rich semantic structure and temporal and/or spatial dynamics. Probabilistic graphical model is a formalism that exploits the conjoined talents of graph theory and probability theory to build complex models out of simpler pieces. It offers a powerful language to elegantly define expressive distributions under complex scenarios in high-dimensional space, and provide a systematic computational framework for probabilistic inference. These virtues have particular relevance in a wide range of application in scientific and engineering problems such as computational biology, robotics, information science, finance, machine learning, etc. In this lecture series, I will discuss the basic mathematical underpinnings of graphical models---including representation syntax, inference algorithms, and learning strategies. Finally I will illustrate and discuss applications of graphical models in informational retrieval and natural language process.