People / Students / Class of 2027
Jacqueline Liu graduated from the University of California, Los Angeles with a Bachelor of Science in Applied Mathematics, with a specialization in computing. Her academic interests center on multimodal AI agents and reinforcement learning. At UCLA, she pursued research in transformer-based scene graph generation and video action recognition, gaining experience in multimodal learning and visual understanding. She also completed a first-author conference paper on multiplayer combinatorial bandits under information asymmetry, exploring how agents can coordinate and learn effectively when they do not share the same information. Alongside her research, Jacqueline worked on applied industry projects with organizations including HARMAN International and GE Aerospace, where she developed a stronger understanding of how machine learning models move from research ideas into real-world systems that improve workflows and generate measurable business value.
Jacqueline previously worked as a Data Engineering Intern at HSBC, where she was responsible for building multi-agent RAG systems for enterprise knowledge and workflow automation. Her work involved Python-based data pipelines, retrieval systems, and agent orchestration using tools such as LangChain and LangGraph, with a focus on improving information access, contextual reasoning, and deployment efficiency across business teams. To stay close to the fast-moving AI product landscape, she also worked as an AI Product Strategy Intern at TikTok, where she analyzed frontier AI model and product developments from companies such as OpenAI, Google, and Anthropic. Through competitive research, product teardown, and technical strategy analysis, she studied how model capabilities, infrastructure choices, user experience, and go-to-market decisions shape whether AI products can successfully reach and retain users.
At MLDS, Jacqueline looks forward to studying machine learning more systematically while continuing to gain industry experience and exploring practical applications of AI. She hopes to contribute to the growing field of AI agents by building systems that can reason, collaborate, and create value in real-world settings.
