Ting Wang

College of Computing
Georgia Institute of Technology
Atlanta, GA 30332
twang@cc.(spam

gatech. stops

edu here)

OVERVIEW

Section 1: INTRODUCTION (short bio)

Currently Ting is a Ph.D. student in the College of Computer Science, working with Prof. Ling Liu. His research interests include data privacy & security, data mining and unstructured data (e.g., graph). He holds a M.S. degree from the University of British Columbia and a B.S. from Zhejiang University, both in Computer Science.

Section 2: our approach (PUBLICAtions)

  • [B1] Ting Wang and Ling Liu. “From Data Privacy To Location Privacy”. Machine Learning in Cyber Trust: Reliability, Security, Privacy. Editors: Jeffery Tsai and Philip Yu. Publisher: Springer-Verlag, 2008.
  • [C1] Ting Wang, Ling Liu, Chang-shing Perng, et al. “A Temporal Data-Mining Approach for Discovering End-to-End Transaction Flows”. 2008 IEEE International Conference on Web Services (ICWS’08). (pdf)
  • [C2] Ting Wang and Rachel Pottinger. “SeMap: A Generic Mapping Construction System”. The 11th International Conference on Extending Database Technology (EDBT’08). (pdf)
  • [C3] Ting Wang and Ling Liu. “Butterfly: Protecting Output Privacy in Stream Mining”. The 24th IEEE International Conference on Data Engineering (ICDE’08). (pdf)
  • [C4] Bhuvan Bamba, Ling Liu, Peter Pesti and Ting Wang. “Supporting Anonymous Location Queries in Mobile Environments with PrivacyGrid”. The 17th International World Wide Web Conference (WWW’08). (pdf)
  • [C5] Ting Wang, Shuang Hao, Ping Wang and Gang Peng. “Efficient and Density-Aware Routing in Wireless Sensor Networks”. The 15th IEEE International Conference on Communication and Networks (ICCCN’06). (pdf)
  • [C6] Ting Wang. “TWStream: Finding Correlated Streams under Time Warping”. The 8th Asia Pacific Web Conference (APWeb’06). (pdf)
  • [C7] Ting Wang. “Generalized Projected Clustering in High-Dimensional Streams”. The 8th Asia Pacific Web Conference (APWeb’06). (pdf)
  • [T1] Ting Wang and Ling Liu “Output Privacy Protection in Stream Mining”. Technical Report. (pdf)
  • [T2] Ting Wang. “Fast Monte Carlo Smoothers”. Technical Report. (pdf)

Section 3: EXPERIMENTS (RESEARCH PROJECTS)

Data Stream Processing

Data stream brings new challenges/opportunities for data processing/mining research, due to its strict requirement on the space/time complexity of the algorithms, its unique property of transience and its broad applications. I have been working on the following problems: (1) correlations within stream, (2) locality in query processing and (3) privacy-preserving processing.

 

Ad-Hoc & Sensor Networks
Wireless Sensor Network (WSN) represents a new way of sensing, gathering and processing information. Featuring unstable communication, energy-constrained nodes and frequent topology changes, WSN asks for combining elegant theory and effective engineering approaches. I have been working on the following problems: (1) adaptive energy-efficient routing, and (2) content Indexing in ad-hoc networks.

 

Fast Learning Methods

Statistical learning methods can, effectively learn the underlying models from data, which however could hardly be applied to real applications due to its computation complexity. I have worked on fast approximate versions of learning methods that can be applied to real tasks.

Section 4: related work (Learning & teaching)

Course Work

Before Georgia Tech  
CSE 506 Operating System CSE 549 Computational Biology
CSE 698 Practicum in Teaching  ESL 598  Advanced Oral Aural
CPSC 500 Algorithm Design and Analysis CPSC 532 Advanced Machine Learning
CPSC 515 Image Understanding CPSC 542 Non-linear Optimization
CPSC 540 Machine Learning   CPSC 534 Meta-data Management
At Georgia Tech  
CS 6290 Computer Architecture CS 7001 Introduction to Graduate Study
CS 8906 Special Problem Study  

 

Teaching

2005.1 – 2005.4 Introduction to Database System
2005.5 – 2005.6 Software Engineering
2005.6 – 2005.7 Introduction to Computation

 

Reading List

Section 5: conclusion (life's other aspects)

Album
To the time-space I have been in, and the people I have been with.

 

Links to Friends

To the wonderful experience of growing up with them together.