This seminar explores how agentic AI can be integrated into academic research as a disciplined, verifiable, and human-led method rather than a generic productivity tool. It provides practical frameworks for responsible use, workflow verification, and multi-agent research design across literature work, analysis, writing, and grant development.
This seminar explores how frontier AI systems can be organized into specialized research 'teams' or multiple-agent workflows that support orchestrated literature review, analysis, synthesis, writing, and verification all under human control. It combines conceptual depth with practical demonstrations to help researchers...
This workshop explores how autonomous AI agents can support scientific research in R, from conceptual distinctions between chat systems and agentic workflows to practical deployment with open-source tools (OpenCode). It is designed for researchers who want to use AI critically, securely, and effectively in data-intensi...
This seminar explores how to treat artificial intelligence as a configurable research method rather than a chatbot or generic software tool. It provides a rigorous framework for validating AI-enabled workflows, designing defensible studies, and making scientific claims that remain consistent with the available evidence...
This workshop explores how AI can be used responsibly across the research lifecycle, from literature discovery and question development to computational analysis, writing, and publication and gives a practical framework for deciding what AI can assist with and what must remain human-led.
It is argued that agents lower the cost of maintaining tests, commit histories, repository structure, instructions, and decision records while making their benefits immediate while making their benefits immediate.
This seminar explores the conceptual foundations of artificial intelligence and offers practical guidance on using and evaluating AI tools responsibly in academic research.
Daisung Jang· 0 citations
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