Jiakang Huang

I am Jiakang (Daniel) Huang, a Bachelor of Science student in Computer Science at the University of British Columbia. I am interested in the systems and infrastructure that make sustainable, self-evolving AI agents possible. My current work spans compiler optimization for efficient ML workloads (graph fusion in PyTorch Inductor), modular model composition via full fine-tuned model merging, and query optimization in distributed and vector data systems.

I am currently working with Henry Chan, a Principal Software Engineer at Huawei Canada's Field Lab, where I contribute to the development of GaussDB's shared-nothing distributed database system and its vector database branch. In parallel, I am conducting research at the University of Toronto on graph fusion strategies in PyTorch Inductor. During my third year, I also served as an undergraduate teaching assistant for CPSC 213, supporting students in computer systems and low-level programming.

My long-term goal is to build sustainable, self-evolving AI agents that can continuously learn, adapt, and improve. My research path reflects this vision: my first paper focuses on natural language processing, aiming to help AI better understand human language; my second paper centers on AI infrastructure, targeting faster and more efficient systems for training and serving models; and my planned third paper will explore AI memory systems, enabling agents to develop stronger long-term memory.

Beyond academics and research, I co-founded iMark, an AI bookmark assistant built on retrieval-augmented generation, memory-based personalization, and end-to-end product design. Outside of work, I enjoy basketball and CS:GO, where I once ranked in the top 5% globally.