Sunghee Yun (He/Him)

Co-founder & CTO @ Erudio Bio, Inc., Chief Business Development Officer @ WeStory.ai, Advisor & Evangelist @ CryptoLab, Inc., KFAS-Salzburg Global Leadership Initiative Fellow @ Salzburg Global Seminar, Adjunct Professor @ Sogang University, Advisory Professor @ DGIST, Global Advisory Board Member @ Innovative Future Brain-Inspired Intelligence System Semiconductor of Sogang University, Network Expert @ Gerson Lehrman Group

Abstract

As artificial intelligence enters a new phase of maturity, we are witnessing the emergence of agentic AI systems—models that not only interpret data but autonomously reason, decide, and act. In this talk, I will trace the trajectory of this evolution: from the early promise of deep learning and statistical inference to today’s frontier of large language models (LLMs) and multi-agent AI architectures. Drawing on perspectives from both Silicon Valley and the Korean R&D landscape, I aim to unpack how these technologies can be harnessed across disciplines—not as black-box tools, but as interpretable, iterative collaborators.

This seminar builds on a sequence of AI lectures I’ve delivered at academic institutions and research centers including Stanford University, Seoul National University (SNU), Korea Advanced Institute of Science & Technology (KAIST), Pohang University of Science and Technology (POSTECH), Daegu Gyeongbuk Institute of Science & Technology (DGIST), Sogang University, Lyon Research Center of CryptoLab, Inc., and Salzburg Global Seminar. Each of these engagements offered a different lens—ranging from mathematical optimization and data infrastructure to bio-AI convergence and public-sector applications. At the heart of these discussions is a shared challenge: how to build systems that extend human intuition while remaining grounded in domain understanding and data fidelity. Through concrete case studies—including my startup Erudio Bio—I will demonstrate how agentic AI frameworks are being applied to biotech, diagnostics, and semiconductor processes.

For an audience rooted in statistics and data science, this lecture will also explore where traditional inference meets modern AI—what we gain, what we risk, and how theory can guide practice in this new paradigm. Whether in multi-modal learning, scientific discovery, or personalized medicine, I will argue that the most impactful AI systems are not the most complex—but the most context-aware. By bridging human insight with computational autonomy, we unlock not only smarter systems, but more meaningful questions.