My ultimate research vision is to develop an AI model that can emulate human reasoning and thinking, which requires building a differentiable Neural-Symbolic AI. This approach involves enabling neural models to interact with external symbolic modules, such as knowledge graphs, logical engines, math calculators, and physical/chemical simulators. This will facilitate end-to-end training of such a Neural-Symbolic AI system without annotated intermediate programs. During this talk, I will introduce my two research endeavors focused on building differentiable neural symbolic AI using knowledge graphs. Firstly, I will discuss how Symbolic Reasoning can help Neural Language Models. I designed OREO-LM, which incorporates knowledge graph relational reasoning into a Large Language Model, significantly improving multi-hop question answering using a single model. Secondly, I will discuss how Neural Embedding can help Symbolic Logic Reasoning. I solve complex first-order logic queries in neural embedding space, using fuzzy logic operators to create a learning-free model that fulfills all logic axioms. Finally, I will discuss my future research plans on applying differentiable neural-symbolic AI to improve program synthesis, architecture design, and scientific discovery.  Bio:  Ziniu Hu is a fifth-year PhD student in computer science at UCLA. His research focuses on integrating symbolic knowledge reasoning with neural models. Under the guidance of Professors Yizhou Sun and Kai-Wei Chang, he has developed several models that have successfully solved complex question-answering and graph mining problems. His research has received support from Baidu Ph.D. Fellowship and Amazon Ph.D. Fellowship. He also contributed to the research community as the research-track workflow co-chair for KDD'23 and was awarded the top reviewer at NeurIPS'22. His research has been deployed on various industrial applications, including Tiktok unbiased Recommendation, Google YouTube Shorts recommendation, Microsoft Graph anomaly detection, and Facebook hate speech detection service. His research has received several awards, including the best paper award at WWW'19, the best student paper award at DLG-KDD'20 workshop, and the best paper award at SoCal-NLP'22.