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

In recent years, artificial intelligence has begun to transform the life sciences—not by replacing scientific intuition, but by accelerating discovery through scalable pattern recognition, hypothesis generation, and system-level insight. In this seminar, I will explore how AI methods originally honed in semiconductor manufacturing and e‑commerce are now being adapted for high-impact applications in biotechnology, diagnostics, and precision medicine. Using Silicon Valley as a case study, I will share how domain-guided AI can shorten feedback loops between data collection, interpretation, and therapeutic development.

The talk will include practical lessons from my journey as co-founder of Erudio Bio, where we integrate semiconductor-based multiplexing with multi-omic biomarker profiling to create a high-resolution, AI-native platform for disease detection. I will also discuss how foundational experiences at Amazon and Gauss Labs shaped my understanding of industrial-scale data systems and informed our approach to building interpretable, data-efficient AI for biological complexity. These stories demonstrate that the future of biotech will depend not only on data volume, but on the quality, context, and cross-domain translation of that data.

Finally, I will reflect on broader efforts to bridge AI and bioscience across institutional boundaries—from academic seminars at Stanford University, Seoul National University (SNU), Korea Advanced Institute of Science and Technology (KAIST), and Pohang University of Science and Technology (POSTECH) to public-private engagements in Korea, France, Austria, and the U.S. Through this global lens, we’ll consider what it means to pursue “explainable AI” in biology—not just to improve predictions, but to generate trust, scientific insight, and real-world impact. The seminar invites scientists, engineers, and policymakers to rethink how AI can augment—not abstract—our understanding of life.