Dr. Tao Liu is a tenure-track Assistant Professor in the Department of Mathematics and Computer Science at Lawrence Technological University. He received his Ph.D. in Electrical and Computer Engineering from Florida International University in 2020 under the supervision of Dr. Wujie Wen. He also earned an M.S. in Computer Engineering from Florida International University in 2012 and a B.S. in Computer Science from Southeast University in 2007.

Prior to joining Lawrence Technological University, Dr. Liu was a Research Associate in the Department of Electrical and Computer Engineering at Lehigh University. He also brings more than six years of full-time industry experience in software development and data analytics.

Dr. Liu’s research focuses on secure, efficient, and trustworthy artificial intelligence systems. His work spans adversarial machine learning, AI security, model compression, hardware-aware deep learning, embedded and edge AI, and privacy-preserving inference. He is also interested in neuromorphic computing and emerging architectures for reliable and efficient intelligent systems. His goal is to develop AI systems that are not only high-performance, but also robust, secure, and practical for deployment in real-world and mission-critical environments.

» Education

• Ph.D. in Electrical and Computer Engineering, Florida International University, 2020
• M.S. in Computer Engineering, Florida International University, 2012
• B.S. in Computer Science, Southeast University, 2007

» Professional Experience

• Assistant Professor, Lawrence Technological University
• Research Associate, Lehigh University
• More than six years of industry experience in software development and data analytics 

» Courses and Advising

• MCS 4613 Computer Networks
• MCS 4663 Operating Systems
• MCS 4833/4843 Senior Project: AI and Cybersecurity
• MCS 5813 Introduction to Computer Security
• MCS 5993 Adversarial Machine Learning
• MCS 7013/7033 Collaborative Research Project: AI and Cybersecurity
• MCS 7113/7133 Master’s Thesis

» Research Interests

• Secure and trustworthy artificial intelligence
• Efficient AI systems, model compression, and hardware acceleration
• Embedded/edge AI and neuromorphic computing
• Privacy-preserving and networked intelligent systems

» Selected Publications 

• J. Liu, Z. Liu, X. Huang, R. Zhu, Q. Zheng, Z. Hao, T. Liu, J. Tao, and Y. Fan, “Auto-isp: An efficient real-time automatic hyperparameter optimization framework for ISP hardware system,” in Proc. 61st ACM/IEEE Design Automation Conf. (DAC), 2024, pp. 1–6.
• R. Ran, N. Xu, T. Liu, W. Wang, G. Quan, and W. Wen, “Penguin: Parallel-packed homomorphic encryption for fast graph convolutional network inference,” in Advances in Neural Information Processing Systems (NeurIPS), vol. 36, 2023, pp. 19104–19115.
• T. Liu, Z. Liu, Q. Liu, W. Wen, W. Xu, and M. Li, “Stegnet: Turn deep neural network into a stegomalware,” in Proc. 36th Annual Computer Security Applications Conf. (ACSAC), 2020.
• T. Liu, W. Wen, L. Jiang, Y. Wang, C. Yang, and G. Quan, “A fault-tolerant neural network architecture,” in Proc. 56th ACM/IEEE Design Automation Conf. (DAC), 2019, pp. 1–6.
• Z. Liu, Q. Liu, T. Liu, N. Xu, X. Lin, Y. Wang, and W. Wen, “Feature distillation: DNN-oriented JPEG compression against adversarial examples,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), 2019.
• Z. Liu, T. Liu, W. Wen, L. Jiang, J. Xu, Y. Wang, and G. Quan, “DeepN-JPEG: A deep neural network favorable JPEG-based image compression framework,” in Proc. 55th ACM/IEEE Design Automation Conf. (DAC), 2018, pp. 1–6.

» Honors & Awards

• Best Paper Nomination, ICCAD 2018
• Best Paper Nomination, ASP-DAC 2018
• DAC 2017 Richard Newton Young Fellow Award