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Xin Gao


Department of Mathematics and Statistics

York University, Toronto

Contact information:
email:xingao AT Tel:416-736-2100 ext 66097

Google Scholar Profile:


Our Artificial Intelligence and Machine Learning Lab’s Research


  • Congratulations to Gian Alix (research assistant of our lab) for receiving the Vector Scholarship of Artificial Intelligence from Vector Institute!

  • Our research team has developed a model selection method for colored Gaussian Graphical model using a penalized composite likelihood (Core contributor: Qiong Li). fg_graph1 parallel1 tuning1 realdata1


  • Our research team (core contributor: Gian Alix) has developed an online Type 2 Diabetes risk predictor. It  is an online tool that can be used to calculate a user’s risk of developing Type 2 Diabetes Mellitus. The risk prediction is based on the user’s input of medical lab information such as age, gender, body mass index, fasting blood sugar, triglycerides, and high-density lipoprotein levels. The calculator is modelled using a logistic regression model, and has been trained using the medical records of over ten thousand Canadian patients. This newly developed tool is intended to serve physicians and patients in predicting future diabetes risk and take early preventive measures.


  • Our research team is developing AI methods to analyze spectroscopic data on rocks from Mars to determine the mineral compositions of rock samples. (Core contributors: Menelaos Konstantinidis, Gian Alix, Guanlin Zhang,  Beth Lymer.)



  • A generalized linear mixed model (GLMM), combining the features of the commonly used generalized linear model (GLM) and a random effects model, was developed to predict future EAB (a type of beetle) spread patterns in Southern Ontario, Canada. (core contributors: Adam Zhong, Xingming Xu and Maria Xu.)


  • NSERC Discovery Acceleration Award, $120,000, 2018-2020. (The Discovery Accelerator Supplements (DAS) Program provides substantial and timely additional resources to accelerate progress and maximize the impact of established, superior research programs.)


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