Sijia Liu

Biography

Sijia Liu received the Ph.D. degree (with All-University Doctoral Prize) in Electrical and Computer Engineering from Syracuse University, NY, USA, in 2016. He was a Postdoctoral Research Fellow at the University of Michigan, Ann Arbor, in 2016-2017, and a Research Staff Member at the MIT-IBM Watson AI Lab in 2018-2020. Since 2021, he has served as an Affiliated Professor at the MIT-IBM Watson AI Lab, ... IBM Research. Dr. Liu's research spans the areas of machine learning, optimization, computer vision, security, signal processing, and data science, with a focus on developing learning algorithms and theory for trustworthy artificial intelligence (AI). These research themes provide a solid foundation for reaching his long-term research objective: Making AI systems safe and scalable. He received the Best Student Paper Award at the 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’16), and the Best Paper Runner-Up Award at the 38th Conference on Uncertainty in Artificial Intelligence (UAI’22). He has published over 70 papers at top-tier machine learning and computer vision conferences, such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, AISTATS, and AAAI. He has also organized a series of Trustworthy and Scalable Machine Learning workshops and tutorials in ICML, NeurIPS, KDD, CVPR, and ICASSP.