Sijia Liu

Research Staff Member

Prior to joining the MIT-IBM Watson AI Lab, Sijia Liu was a Research Fellow at the University of Michigan, Ann Arbor. He received the Ph.D. degree (with All University Doctoral Prize) in Electrical and Computer Engineering from Syracuse University, NY, USA, in 2016. His recent research interests include deep learning, adversarial machine learning, gradient-free optimization, nonconvex optimization, and graph data analytics. He received the Best Student Paper Finalist Award at Asilomar Conference on Signals, Systems, and Computers (Asilomar’13). He received the Best Student Paper Award at the 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’17). He served as a general co-chair of the Symposium ‘Signal Processing for Adversarial Machine Learning’ at GlobalSIP, 2018. He co-chaired the workshop ‘Adversarial Learning Methods for Machine Learning and Data Mining’ at KDD, 2019.

Selected Publications

Selected Publications

Areas of Interest

  • Optimization for Deep Learning
  • Adversarial robustness
  • Security for AI
  • Network data analytics


  • Optimization
  • Deep learning
  • Security
  • Graph
  • Single processing