Climate startups are critical to driving mitigation and adaptation innovation, yet they face distinctive ecosystem demands that generic support conditions alone cannot meet. Despite their growing importance, how climate startup ecosystems actually function and why they differ across cities remains poorly understood. Drawing on a multi-city comparative study of Korea, six key enablers (policy, finance, talent, market access, entrepreneurial culture, and support organizations) are examined to understand how they combine across urban contexts to shape ecosystem outcomes. No single enabler is sufficient, and effective ecosystems can emerge through distinct configurations shaped by each city’s industrial legacy, governance, and adaptive capacity. A climate-specific, configurational lens reveals the underlying logic of urban startup ecosystems offering practical implications for cities and policymakers navigating uneven regional development.

Esther Choi is Research Lead for Nature-Based Solutions and the Private Climate Sector at World Resources Institute. She leads research and engagement initiatives focused on scaling private-sector contributions to climate and nature action by aligning incentives, mobilizing capital and knowledge, and strengthening the enabling environments in which innovative solutions can emerge and scale.

Her current work centers on three intersecting areas: identifying ecosystem enablers for early-stage climate startups - particularly in emerging and developing economies; guiding companies and investors on nature-positive strategies; and advancing de-risking, blended finance, and institutional approaches that catalyze private investment for climate and nature action. Esther is also a Lead Author for the Finance Chapter of the Intergovernmental Panel on Climate Change (IPCC)’s Seventh Assessment Report.

From February to July 2026, Esther serves as a Visiting Professor at the Korea Advanced Institute of Science and Technology (KAIST), where her research and teaching focuses on sustainable finance, climate startup ecosystems, and the institutional conditions that enable climate innovation to scale.

With over a decade of experience spanning climate finance, sustainable development, and international governance, Esther has held research and policy roles at Stanford University's Sustainable Finance Initiative, the Green Climate Fund, the World Bank, and the Global Green Growth Institute. Her work has involved close collaboration with governments, investors, and multilateral institutions across Asia, Africa, and Latin America to design policies, financing mechanisms, and partnerships that support climate-aligned growth.

Esther holds a Ph.D. in Environmental Science, Policy, and Management from the University of California, Berkeley, and a Master’s degree in Environmental Management from the Yale School of the Environment. She is based in the San Francisco Bay Area.

Maintaining consistent levels of vigilance over time is critical for workers in high-risk work systems. The decline in performance that often occurs when monitoring and screening for occasional and unpredictable signals, known as vigilance decrement, endangers workers’ health and safety and threatens efficiency. Vigilance decrement is especially concerning in clinical environments, where shift work and long working hours impair clinicians’ ability to sustain attention over extended period. A clinical environment is a place where monitoring vigilance decrement is essential given its impact on clinicians’ job satisfaction productivity, patients’ safety, and healthcare quality. Despite its importance, efforts to monitor and predict vigilance decrement have largely relied on behavioral models that depend on retrospective or laboratory-based data, neither of which is feasible for continuously tracking individuals’ vigilance levels in real-time. To date, no human-technology interfaces that provide feedback to mitigate vigilance decrement in real-world operations exist. This talk presents recent work from the Human and Systems Lab on measuring, modeling, and mitigating vigilance decrement using artificial intelligence and features derived from neural and physiological sensors. The findings demonstrate the potential for integrating these approaches into assessment, training, and intervention tools to enhance human performance and ultimately support safer work environments.

Ji-Eun Kim is an Associate Professor in the Department of Industrial and Systems Engineering at the University of Washington (UW) and the Director of the Human and Systems Lab. Her research centers on designing adaptive interventions that accommodate diverse groups of users. To achieve this, she uses neurophysiological sensors to model and predict human performance. She holds a Ph.D. in Industrial Engineering from the Pennsylvania State University. She is a recipient of the 2023 National Science Foundation (NSF) CAREER Award and the 2020 UW Faculty Appreciation for Career Education & Training Award. Her advisees have received numerous honors, including the Human Factors and Ergonomics Society (HFES) Best Student Paper Award (Human Performance Modeling Technical Group), HFES Council of Technical Groups Student Presenter Award, and the Institute of Industrial and Systems Engineers (IISE) Doctoral Colloquium Dissertation Award. She serves as the Chair of the Human Performance Modeling Technical Group at the HFES.

What if a world simulation model could render not an imagined environment but a city that actually exists? Prior generative world models synthesize visually plausible yet artificial environments by imagining all content. We present Seoul World Model (SWM), a cityscale world model grounded in the real city of Seoul. SWM anchors autoregressive video generation through retrieval-augmented conditioning on nearby street-view images.

However, this design introduces several challenges, including temporal misalignment between retrieved references and the dynamic target scene, limited trajectory diversity and data sparsity from vehicle-mounted captures at sparse intervals. We address these challenges through cross-temporal pairing, a large-scale synthetic dataset enabling diverse camera trajectories, and a view interpolation pipeline that synthesizes coherent training videos from sparse street-view images. We further introduce a Virtual Lookahead Sink to stabilize long-horizon generation by continuously re-grounding each chunk to a retrieved image at a future location.

We evaluate SWM against recent video world models across three cities: Seoul, Busan, and Ann Arbor. SWM outperforms existing methods in generating spatially faithful, temporally consistent, long-horizon videos grounded in actual urban environments over trajectories reaching hundreds of meters, while supporting diverse camera movements and text-prompted scenario variations.

Seungryong Kim is an Associate Professor at Kim Jaechul Graduate School of AI, KAIST, Seoul, Korea. Before joining KAIST, he was an assistant professor at Korea University, a postdoctoral researcher in the School of Computer and Communication Science at École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, and a postdoctoral researcher in the School of Electrical and Electronic Engineering at Yonsei University, Seoul, Korea. 

He was a research intern at Microsoft Research Asia (MSRA), Beijing, China. He received the B.S. and Ph.D. degrees in the School of Electrical and Electronic Engineering at Yonsei University, Seoul, Korea, in 2012 and 2018, respectively. His research interests include Computer Vision, Computational Photography, Machine Learning, and Deep Learning, particularly representation learning, visual scene reconstruction and understanding.

World Model is an ambiguous term. I will discuss how people define the World Model differently, what they are building, and how I define it myself. And then I will discuss its significance, especially in the context of Robotics.

Minjoon Seo is Co-Founder & CEO at Config and an Associate Professor at KAIST AI. He works on the data infrastructure and technology for robotics. His research background is in foundation models, especially language models and vision-language models. He received the NAACL 2025 Best Paper Award and is recognized as a Forbes Korea Y30s Rising AI Leader