The Artificial Intelligence group focuses on intelligent computing. Our research objective is to make the computer systems more intelligent, with the ability to hear and understand human speech and languages, to learn from examples, to extract and recognize important features from complex data, to represent and reason about knowledge in various new and advanced programming languages, and to plan courses of actions.  The enabling technology areas that we study are machine learning and data mining, speech recognition, logic programming and Bayesian networks, natural language processing and information retrieval, and computer music and audio engineering. We continue to disseminate our research results in high-profile international conferences and journals, and apply our research to application areas such as programming language design, Chinese medicine, biometrics, speech and handwriting recognition and computer security, computer music as well as data mining and its applications on the Web and in bioinformatics.

Focused research areas and projects

Seminar Schedule


UG Courses:

COMP 221:  Fundamentals of Artificial Intelligence

COMP 327:  Introduction to Pattern Recognition

COMP 300Y: Introduction to Machine Learning

COMP 342: Introduction to Computer Music


PG Courses:

COMP 521: Artificial Intelligence

COMP 522: Machine Learning

COMP 526: Natural Language Processing

COMP 527: Pattern Recognition

COMP 537: Knowledge Discovery in Databases

COMP 538: Reasoning and Decision under Uncertainty

COMP 621: Advanced Topics in Artificial Intelligence

Focused Research Areas

l            In knowledge representation, we are working on Bayesian networks, answer-set logic programming, and case-based reasoning

l            In machine learning and data mining, we are working on Web mining to uncover Web users’ preferences and access patterns, clustering and analysis of sequential data, semi-supervised learning, embedding of high-dimensional data, transductive learning for IDS alarm processing, sensor fusion for intrusion detection.

l            In planning, we are studying how to learn to make better plans from past plans, with application to customer relationship management and robotic programming.

l            In speech and natural language understanding, we apply statistical methods to enable more effective techniques to understand human speech and translate between different natural languages

l            In computer music and audio engineering, we are working on Matching Synthesis of Western and Chinese Musical Instruments Musical Instrument Recognition.

Research Projects

HORNER, Andrew
  • Wavetable Matching with Modified Spectral Snapshots, Sept 2002 - Aug 2005

  • Wavetable Matching Using Combinatorial Basis Spectra Selection, Sept 2001 - Aug 2004

  • Low Peak Amplitudes for Wavetable Synthesis, Aug 1999 - July 2001

KWOK, James
  • Machine learning for content-based and collaborative recommender systems
  • Information geometry of kernel methods with applications to data mining
  • Support vector machine with Bayesian learning for the automatic categorization of text and hypertext documents
LIN, Fangzhen
  • Learning and its role in reasoning. Specifically, I'm interested in provably correct learning methods to make reasoning more efficient.
  • Planning and learning. We are using Planner-R to experiment how learning can help planning. This is an ongoing RGC CERG project.
  • ASSAT - computing answer sets by using SAT solvers.
MAK, Brian
  • Discriminative Training of Non-parametric Auditory Filters for Automatic Speech Recognition, 2002-2004
  • Asynchronous Multi-Band Continuous Speech Recognition using HMM Composition, 2002-2003
WU, Dekai
  • Statistical Natural Language Processing
  • Machine Translation.
YANG, Qiang
  • Web mining for Web user access-pattern prediction, with application to Web caching, prefetching and e-learning 2001-- 2004

  • Data Mining for case bases and plans, with application to customer relationship management 2002—2004

YEUNG, Dit-Yan
  • Semi-supervised learning

  • Embedding/manifold/spectral methods

  • Kernel methods

ZHANG, Nevin
  • Efficient Algorithms for Near-Discernible Partially Observable Markov Decision Processes, September 2001 - August 2004

  • Toward a Statistical Foundation for Traditional Chinese Medicine (TCM) Diagnostics, January 2000 - present

Affiliated Centers and Joint Projects

Cyberspace Center

Human Language Technology Center (HLTC)

Virtual English Language Adviser Project (VELA)




HORNER, Andrew

KWOK, James

LIN, Fangzhen

MAK, Brian

WU, Dekai

YANG, Qiang

YEUNG, Dit-Yan

ZHANG, Nevin

PG Students

CHAI, Xiaoyong





CHU, Calvin

LAU, Ricky


WANG, Gang



WONG, Francis H.C.

WUN, Simon C.W

XIONG, Yimin

YIN, Jie 

ZHAO, Jicheng

ZHAO, Yuting

ZHUANG, Cammy Y.Z.


CHAN, Raymond

GUO, Haipeng

YANG, Audrey

YUAN, Shihong

ZHANG, Zhihua




    Artificial Intelligence Laboratory Location:  room 4215, Tel: 23588834