About Me

I am a current Ph.D. in the University of York supervised by Dr Daniel Kudenko and Dr Sam Devlin. My research interests are about "Game Analytics", more precisely, "Game Data Mining". The purpose of "Game Data Mining" is generally about building predictive models based on players' in-game behaviours in order to predict their future decisions. For example, it is common seen that players are leaving from a game for some reasons, but it is usually very hard to find out what game designs are correlated with their decisions. With "Game Data Mining", a predictive model could be built to link players' behaviours to their activities, therefore, their disengaging decisions can be predicted in advance. By the way, sometimes I also produce games for research.

Contact Details

Hanting Xie
Room B81, Foss Studios, 32 Lawrence Street
York, Yorkshire YO10 5FN United Kingdom

Phone: +44(0)7472454233
Email: xiehanting@gmail.com


University of York

Ph.D. in Game Data Mining Since 2013, 10

I started my Ph.D. in 2013 to do research in the area of game analytics supervised by Dr. Daniel Kudenko. Since then, I was mainly working on predicting players' disengagement and first purchases in two commercial games "I Am PlayR" and "Lyroke" which are both developed by "We R Interactive" (Recently purchased by Inspired Gaming). At the same time, I am developing a clone of Asteroids for my later research which is about data mining on game interface design.

University of Bristol

MS.c. in Computer Science 2011, 10 - 2012, 10

I was specialised in Machine Learning, Data Mining and High Performance Computing during my Master's degree. My final thesis is named "Efficient Matching of "If-Then" Rules".

Thesis Abstract:
The LCS (Learning Classifier Systems) are systems which have been broadly used on different machine learning problems. This system is a rule ("If-Then Rules") based system. Compared with other machine learning methods, LCS has a very slow run-time. And the most time consumption part inside of it is "If-Then Rules Matching". In this study, the problem would be optimized using different techniques, including: "HashMatch", "Recursion Optimization", "Object Pool", "Parallel TTreeN" and "Input Tree + TTreeN". Among those, the "Input Tree + TTreeN" method could even get a stable acceleration of more than 10 times faster than the naive matching and it is developed originally from this thesis. The idea of it is to take the advantage of trees to prune the duplicated inputs before performing the matching. Also, it transfers the traditional loop comparison into the comparisons between the shapes of the trees. Benefits from those two features, this approach offers a competitive improvement on its efficiency.

Macau University of Science and Technology

BS.c. in Computer Science 2011, 10 - 2012, 10

I was specialised in Computer Technology and Application during my Bachelor's degree. My final thesis is named "DNA Sequence Alignment Using VHDL".

Thesis Abstract:
"DNA Alignment" is a useful technique in the area of medicine and biology.The main idea of this research is to design and implement a hardware version of "Smith-Waterman Alignment" on FPGAs using VHDL. A hardware version has its certain value over the software design: firstly, hardware design has lower power consumption than a software design; Secondly, the PCs are designed as general purpose machines, when a simple calculation is needed, several clocks will also be cost for it, but hardware is designed to be dedicated into one problems, it’ll have higher efficient than PCs.



Data Scientist January 2017 – Present

  • Work on the prediction of players’ future decisions (e.g., churn, purchasing) based on players’ past behaviours across game products of all customers (game studios or companies).
  • Train off-line models for prediction with both classical machine learning algorithms and deep learning methods.
  • Train on-line models for recommendation systems that can be integrated into customers’ games.


Game Data Scientist Internship June 2015 – Sep 2015

  • Integrated Google Analytics (GA) into the multi-platform mobile game ‘Race Team Manager (RTM)’.
  • Developed data visualisation tool for the game ‘RTM’ including automatically extracting data from GA server and generate trending charts by one click.
  • Integrated new pricing system in the game ‘RTM’ inspired by data analysis.
  • Developed GA ‘easy to integration’ unity wrapper for a base prototype of new games.

EA Sports

Part-time Data Editor Nov 2014 – Sep 2015

  • Worked as data editor to maintain Chinese Super League dataset for the FIFA series.

National Institute of Informatics, Japan

Research Internship Oct 2012 – Apr 2013

  • Applied ‘GED fast terminate techniques’ (Generalized Expansion Dimension, introduced by Prof. Michael Houle) to KD-Tree (Integrated in ANN system)
  • Solved a multimedia cluster search problem posed by collaborators of Prof. Houle at INRIA/IRISA in Rennes, France.



PhD Research Jun 2015 – Present

Worked together with Dr. Pushmeet Kohli from Microsoft, Dr. Daniel Kudenko and Dr. Sam Devlin from University of York, this research is to compare several popular algorithms towards finding the best interface designs for individual players in a self-developed clone of ‘Asteroids’. I implemented the clone game with Unity and every algorithm for comparisons. The game is available on my personal website and the rest of research is still undergoing.


PhD Research Jan 2013 – Present

This research is conducted with Dr. Daniel Kudenko and Dr. Sam Devlin as the core part of my PhD’s research. Trends of player behaviours (e.g., retention and purchases) are important issues in game industry. However, most current methods are only available for specific games without generality. The main contribution of this research is to use frequency of game events as data representations to predict player behaviour trends. It provides better generality because no knowledge of any event but their frequency is needed. Two publications have been published.


Research Papers


Azure Machine Learning Award


Programming Skills

  • Advanced: Python, C, C#, Java
  • Intermediate: Matlab, C++

Game Engine

  • Intermediate: Unity 5
  • Starter: Unreal Engine 4