This is Feiyang Tang’s home on the web!
Grew up and raised in a mid-sized city of Anhui Province, Eastern China, while half of my family live overseas in Australia, New Zealand, and the EU, travelling everywhere and getting to touch all different kinds of cultures has always been my dream. This motivated me to move to NZ in 2014 when I was 16 years old.
I am currently a Ph.D. candidate of machine learning in Norwegian Computing Center (NR) and NTNU - Norwegian University of Science and Technology. I obtained MSc in Artificial Intelligence from KU Leuven, BE and BSc Honours in computer science from The University of Auckland, NZ.
My research interests include machine learning, data mining and software security. I work in NR’s Information Security research group which does research and industrial development on information security and related topics.
While study always takes most of my time, I was also a huge coffee lover and a part-time barista back in Auckland. Travelling always plays a huge part in my life and I look forward to leaving my footprint on every corners of this beautiful planet (23 countries so far).
PhD in Machine Learning, 2023
Norwegian Computing Center & NTNU, Norway
MSc in Artificial Intelligence, 2020
KU Leuven, Belgium
BSc Honours in Computer Science, 2019
The University of Auckland, New Zealand
BInfSc in Software Engineering, 2018
Massey University, New Zealand
Build and evaluate some online spam filter algorithms that can deal with infinite examples and features.
Explored advanced techniques for constructing features that better describe objects of interest and perform a few tasks using these features.
Enhanced model align image and text on a fragment level for artworks.
Most studies on pattern mining consider itemsets that have a high frequency of occurrence as useful, often determined by the support of the itemsets. However, current research has shown that we need to move beyond a pure “support-confidence” framework for pattern mining. In our research we will concentrate on detecting self-sufficient itemsets from data streams. These patterns have a frequency that is significantly different from the frequency of their subsets and supersets. We present a comprehensive framework for mining self-sufficient itemsets from data streams along with a drift detector. This supports mining self-sufficient itemsets in an online environment and provides the ability to adapt to changes in the stream. Our experimental evaluations show that our framework can mine self-sufficient itemsets faster in an online environment and with better precision and recall.