Big Data, Lifelong Machine Learning and Transfer Learning

Speaker:        Prof. Qiang YANG
                Professor of Department of Computer Science and Engg.
                Hong Kong University of Science & Technology
                Head of the Huawei Noah's Ark Research Lab

Title:          "Big Data, Lifelong Machine Learning and Transfer
                 Learning"

Date:           Thursday, 12 December 2013

Time:           2:00pm - 3:30pm

Venue:          Leung Yat Sing Lecture Theater
                (LT-F, near lift nos. 25/26), HKUST

Abstract:

A major challenge in today's world is the Big Data problem, which
manifests itself in Web and Mobile domains as rapidly changing and
heterogeneous data streams. A data-mining system must be able to cope with
the influx of changing data in a continual manner. This calls for Lifelong
Machine Learning, which in contrast to the traditional one-shot learning,
should be able to identify the learning tasks at hand and adapt to the
learning problems in a sustainable manner. A foundation for lifelong
machine learning is transfer learning, whereby knowledge gained in a
related but different domain may be transferred to benefit learning for a
current task. To make effective transfer learning, it is important to
maintain a continual and sustainable channel in the life time of a user in
which the data are annotated. In this talk, I outline the lifelong machine
learning situations, give several examples of transfer learning and
applications for lifelong machine learning, and discuss cases of
successful extraction of data annotations to meet the Big Data challenge.

*****************
Biography:

Qiang Yang is the head of Huawei Noah's Ark Research Lab and a professor
in the Department of Computer Science and Engineering, Hong Kong
University of Science and Technology. His research interests are data
mining and artificial intelligence including machine learning, planning
and activity recognition. In the past 18 months, he has been leading an
effort at Huawei's Noah's Ark lab in addressing the Big Data challenge and
exploring new opportunities in the telecommunications industry. He
specializes in transfer learning, a machine learning paradigm that allows
knowledge to be extracted and transferred in a variety of domains and
representations.  He is a fellow of AAAI, IEEE, IAPR and AAAS. He received
his PhD from Computer Science Department of the University of Maryland,
College Park in 1989. He had been an assistant/associate professor at the
University of Waterloo between 1989 and 1995, and a professor and NSERC
Industrial Research Chair at Simon Fraser University in Canada from 1995
to 2001. He has been a keynote speaker in many top conferences in AI and
data mining, and has been a champion in ACM KDDCUP competitions. He was
elected as a vice chair of ACM SIGART in July 2010 and is the founding
Editor in Chief of the ACM Transactions on Intelligent Systems and
Technology (ACM TIST) as well as several other top international journals.
He has served as a PC co-chair and general co-chair of prestigious
international conferences including ACM KDD 2010 and 2012, ACM RecSys
2013, ACM IUI 2010, etc. He currently serves as an IJCAI trustee and will
be the PC chair for IJCAI 2015.