Junzi Zhang is a fourth year Ph.D. candidate in Stanford Institute for Computational & Mathematical Engineering (ICME), currently under supervision of Prof. Stephen Boyd. His research is focused on the design and analysis of optimization algorithms and softwares, as well as the applications in the fields of machine learning, causal inference and decision making (especially reinforcement learning), and is sponsored by Stanford Graduate Fellowship. In particular, in collaboration with Prof. Stephen Boyd and Dr. Brendan O’Donoghue, he proposed a stabilized type-I Anderson acceleration method, which has been partly implemented into SCS (Splitting Conic Solver) version 2, one of the most widely used open source convex optimization solver in the world, with notable performance improvement. In addition, he has also served as a reviewer in several top journal and conferences, and has been awarded as Top 200 Best Reviewers (200/3000) in NeurIPS 2018 and Top 5% Reviewers in ICML 2019. Before coming to Stanford, he graduated from School of Mathematical Sciences (SMS) in Peking University (PKU) as Outstanding Graduates in Beijing and Honorary Graduate in the Program of Top Talent Training for Applied Mathematics. He received Chinese National Scholarship for three consecutive years (maximum possible), and has earned first prize in PKU Challenge Cup, PKU Innovation Award and Group Silver Medal in S.-T. Yau College Student Mathematics Contests. He has also been the co-president of Association of Chinese Students and Scholars at Stanford (ACSSS), and had contributed to enhance the cooperation between the Chinese community and the Stanford school authority, as well as boosting the influence of the Chinese culture in the community of international students.