Research Interests

Bringing Generative Language Models to Resource-Limited Devices. Empowering users to harness the capabilities of generative language models on devices with limited computational, memory, or energy resources. This includes developing scalable, collaborative and adaptive strategies for managing workload and data across multiple devices, addressing challenges like bandwidth and latency, and maintaining high model performance despite resource constraints.

Scalable Multi-Agent Knowledge-Intensive Document Analysis. Harnessing multiple generative language model-based agents to perform large-scale, high-fidelity analysis of complex, knowledge-rich documents such as patents, insurance policies, and academic research papers. This research enables advanced applications, including intelligent document processing, automated content expansion, synthesis, and contextual tagging. By orchestrating multiple agents in parallel, the system achieves deep contextual understanding and high analytical precision while maintaining scalability and cost-efficiency.

Education

Ph.D., Electrical Engineering, National Taiwan University, Taiwan
Thesis: "A Cost-Effective System for Real-Time Big Data Processing"
Advisor: Wanjiun Liao, PhD

M.S., Computer Science and Information Engineering, National Taiwan University, Taiwan
Thesis: "SWARM-Secure Wireless Ad-hoc network Reliance Management"
Advisor: Feipei Lai, PhD

B.S., Mathematical Sciences, National Chengchi University, Taiwan

Professional Experience

Assistant Professor, Soochow University, Taiwan

Visiting Scientist, IBM T. J. Watson Research Center, NY, USA
Advisor: Chung-Sheng Li, PhD

Senior Data Scientist, InQuartik, Taiwan

Engineer, Institute for Information Industry, Taiwan

Contact Information

No. 56, Sec. 1, Guiyang St., Zhongzheng Dist., Taipei City 100006, Taiwan

linjiun.tsai@gmail.com or linjiun.tsai@scu.edu.tw

Tel +886-2-2311-1531 ext. 3815

Fax +886-2-2375-6878