Xingjun (Daniel) Ma

I am an associate lecturer in the School of Computing and Information Systems, University of Melbourne, where I also obtained my PhD degree in 2019. I work closely with Prof. James Bailey. Prior to my PhD, I recieved my M.Eng. and B.Eng. from Tsinghua University and Jilin University successively. I have a broad interest in the theory and applications of machine learning and deep learning.

I am also very fortunate to have visited National Institute of Informatics, Japan invited by Prof. Michael E. Houle, and RIKEN, Japan by Dr. Bo Han, Dr. Gang Niu and Prof. Masashi Sugiyama.

Research Interests:

  • Machine Learning
    • Secure/Robust/Explainable machine learning
    • Adversarial machine learning
    • Weakly supervised learning
    • Reinforcement learning
  • Deep Learning and Security
    • Adversarial attack/defense
    • Backdoor attack/defense
    • Generative adverarial networks
    • Applications: object recognition, image inpainting, object detection, video recognition, automatic speech recognition
  • Artifical Intelligence
    • Medical AI
    • Virtual reality surgery

Professional Activities:

  • Journal Reviewer:
    • Pattern Recognition
    • IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
    • IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
    • IEEE Transactions on Knowledge and Data Engineering (TKDE)
    • IEEE Transactions on Industrial Informatics (IEEE T IND INFORM)
    • ACM Transactions on Multimedia Computing Communications and Applications (ACM TOMM)
    • Knowledge and Information Systems (KAIS)
    • Journal of Clinical Medicine (JCM)
    • IEEE Robotics and Automation Letters (RA-L)
  • Conference Reviewer:
    • IJCAI2020, ICML2020, ICLR2020, AAAI2020, KDD2019, NeurIPS2019.

For PhD applicants: funded positions are available for 2020 in our group on adversarial machine learning research. Please directly contact Prof. James Bailey.

For Unimelb master students, contact me if you are familar with: 1) web skills such as js, node.js and html; and 2) machine/deep learning knowledge/tools such as pytorch, tensorflow, keras. (send me your academic transcript)

Contact Me:

Email: $\alpha$.$\beta$ WHERE $\alpha$/$\beta$ is my first/lastname.
Office: Room 9.15, Doug McDonell Building (Building 168), University of Melbourne