Events Calendar

CS Distinguished Lecture: Eric Xing (CMU) - Big Data, Big Model, and Big Learning

Speaker: Eric Xing, Carnegie Mellon University

Talk Title: Big Data, Big Model, and Big Learning

Series: CS Distinguished Lectures

Abstract: In many modern applications built on massive data, such as societal-scale event detection, social security and privacy, web commerce and marketing, and personalized medicine, one needs to handle extremely large-scale data and models that threaten to exceed the limit of current infrastructures and algorithms. Due to the extremely large volume, high dimensionality, and massive task complexity associated with this applications, many modern advancements in computational and statistical learning have been rendered un-leverageable due to their poor scalability on ultra-dimensional models and inability to extract values from massive data; practitioners are forced to turn to naive alternatives such as KNN or K-means cluster for complex problems purely due to their simplicity and scalability, but not for their model validity and correctness. In this talk, I will present some thoughts and work on big learning problems in web-scale social data mining, computational biology, and computer vision. I will discuss some insights and promising directions toward large data size, large feature dimension, and large concept space, including parallelizable and online Monte Carlo for infinite dynamic topic models, fast 1st-order convex optimization algorithms for learning ultra high-dimensional sparse structured input/output models, and output coding techniques for massive multi-task and transfer learning, and I will discuss the design and issues of low level computer architecture and operating systems supporting large learning, applied to a wide range of problems.

Biography: Dr. Eric Xing is an associate professor in 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 solving problems involving automated learning, reasoning, and decision-making in high-dimensional, multimodal, and dynamic possible worlds in social and biological systems. Professor Xing received a Ph.D. in Molecular Biology from Rutgers University, and another Ph.D. in Computer Science from UC Berkeley. His current work involves, 1) foundations of statistical learning, including theory and algorithms for estimating time/space varying-coefficient models, sparse structured input/output models, and nonparametric Bayesian models; 2) computational and statistical analysis of gene regulation, genetic variation, and disease associations; and 3) large-scale information & intelligent system in social networks, computer vision, and natural language processing. Professor Xing has published over 180 peer-reviewed papers, and is an associate editor of the Annals of Applied Statistics (AOAS), the Journal of American Statistical Association (JASA), the IEEE Transaction of Pattern Analysis and Machine Intelligence (PAMI), the PLoS Journal of Computational Biology, and an Action Editor of the Machine Learning Journal (MLJ), the Journal of Machine Learning Research (JMLR). He is a member of the DARPA Information Science and Technology (ISAT) Advisory Group, a recipient of the NSF Career Award, the Sloan Fellowship, the United States Air Force Young Investigator Award, the IBM Open Collaborative Research Award, and best paper awards in a number of premier conferences including UAI, ACL, SDM, and ISMB.

Host: Gaurav Sukhatme, Michael Waterman

Location: Seaver Science Library (SSL) - 150

Audiences: Everyone Is Invited

Contact: Assistant to CS chair