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Hi there, I'm Ming Kai Li, a Master of Science candidate in Computer Science and Engineering at UCSD with a strong focus on LLM systems and Computer Vision. I enjoy turning research into practical AI systems, from LLM pipelines to knowledge graphs and clinical decision support.\n\nI have worked as an AI Engineer at Citrus Oncology and as a research intern in Academia Sinica and NTU's Vision & Learning Lab, combining theory and hands-on deployment to help doctors and researchers make better decisions. I'm excited to apply my skills to challenging problems in data-driven software and AI.…Hi there, I'm Ming Kai Li, a Master of Science candidate in Computer Science and Engineering at UCSD with a strong focus on LLM systems and Computer Vision. I enjoy turning research into practical AI systems, from LLM pipelines to knowledge graphs and clinical decision support.\n\nI have worked as an AI Engineer at Citrus Oncology and as a research intern in Academia Sinica and NTU's Vision & Learning Lab, combining theory and hands-on deployment to help doctors and researchers make better decisions. I'm excited to apply my skills to challenging problems in data-driven software and AI.

Ming-Kai Liu

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Hi there, I'm Ming Kai Li, a Master of Science candidate in Computer Science and Engineering at UCSD with a strong focus on LLM systems and Computer Vision. I enjoy turning research into practical AI systems, from LLM pipelines to knowledge graphs and clinical decision support.\n\nI have worked as an AI Engineer at Citrus Oncology and as a research intern in Academia Sinica and NTU's Vision & Learning Lab, combining theory and hands-on deployment to help doctors and researchers make better decisions. I'm excited to apply my skills to challenging problems in data-driven software and AI.…Hi there, I'm Ming Kai Li, a Master of Science candidate in Computer Science and Engineering at UCSD with a strong focus on LLM systems and Computer Vision. I enjoy turning research into practical AI systems, from LLM pipelines to knowledge graphs and clinical decision support.\n\nI have worked as an AI Engineer at Citrus Oncology and as a research intern in Academia Sinica and NTU's Vision & Learning Lab, combining theory and hands-on deployment to help doctors and researchers make better decisions. I'm excited to apply my skills to challenging problems in data-driven software and AI.

Available to hire

Hi there, I’m Ming Kai Li, a Master of Science candidate in Computer Science and Engineering at UCSD with a strong focus on LLM systems and Computer Vision. I enjoy turning research into practical AI systems, from LLM pipelines to knowledge graphs and clinical decision support.\n\nI have worked as an AI Engineer at Citrus Oncology and as a research intern in Academia Sinica and NTU’s Vision & Learning Lab, combining theory and hands-on deployment to help doctors and researchers make better decisions. I’m excited to apply my skills to challenging problems in data-driven software and AI.

Skills

Experience Level

Expert

Expert

Expert

Expert

Language

Chinese

Fluent

English

Fluent

Work Experience

AI Engineer at Citrus Oncology

September 1, 2025 - October 24, 2025

Engineered an end-to-end LLM pipeline to automatically extract key clinical data from unformatted image/text-based PDF Electronic Health Records (EHRs), creating a knowledge base to support a clinical chatbot. Designed and implemented a Retrieval-Augmented Generation (RAG) system to automate clinical follow-up report generation, grounding differential diagnoses and clinical reasoning in SOAP-formatted notes. Built a real-time conversational AI assistant that updates clinical reports as new information becomes available, improving physician workflow.

Research Intern at Institute of Information Science, Academia Sinica

February 1, 2024 - October 24, 2025

Extracted knowledge graphs from Cyber Threat Intelligence (CTI) reports to identify malware attack patterns. Enhanced entity extraction robustness by combining Large Language Models (LLMs) with Semantic Role Labeling (SRL), improving recall by 10% through more accurate detection of entities and their semantic relationships. Constructed a knowledge graph database and applied graph matching algorithms to identify potential Mitre ATT&CK TTPs, achieving a 44% recall rate.

Undergraduate Researcher at Vision & Learning Laboratory, NTU

December 1, 2023 - October 24, 2025

Explored how diffusion model internal representations improve vision tasks like semantic segmentation. Researched the unlearning of diffusion models, specifically utilizing Textual Inversion to bypass the safety mechanism of Text-to-Image diffusion models.