New York City – September 21, 2026 – As the United States faces increasingly complex public challenges, ranging from intensifying extreme weather events to the growing healthcare needs of an aging population, the demand for scalable and interpretable artificial intelligence systems has become increasingly important. Advanced predictive technologies capable of identifying risks, supporting early intervention, and improving resource allocation are playing an increasingly significant role across public-interest sectors. Chi-Han Lee, an AI researcher specializing in machine learning and predictive analytics, is developing transferable AI modeling frameworks that address critical challenges in environmental resilience and public health through data-driven solutions.
Modern predictive systems often face challenges in capturing complex relationships within large-scale datasets, particularly when dealing with dynamic environments involving spatial variations, long-term trends, and uncertain risk factors. Traditional statistical approaches may struggle to simultaneously interpret complex patterns and provide scalable solutions across different application scenarios. To address these limitations, Lee has focused his research on developing machine learning frameworks that combine advanced algorithms with interpretable modeling approaches, enabling more effective prediction and decision support in socially significant domains.
One of Lee’s major research contributions is a spatial-temporal hybrid deep-learning architecture based on Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM) for environmental forecasting. By integrating CNN-based spatial feature extraction with LSTM-based temporal pattern modeling, the framework is capable of identifying complex patterns within large-scale temperature datasets and improving long-range forecasting performance. The model was trained and evaluated using 2.9 million historical temperature records across 321 cities worldwide and achieved an R² score exceeding 0.90, demonstrating strong predictive capability compared with conventional statistical forecasting approaches.
The significance of this research extends beyond algorithmic performance. Accurate environmental forecasting plays an increasingly important role in supporting climate adaptation strategies, agricultural planning, energy management, and disaster preparedness. Lee’s deep-learning architecture provides a scalable and adaptable forecasting framework that can support broader environmental resilience efforts by improving the accuracy and timeliness of climate-related predictions. Rather than being limited to a single geographic region or application, the architecture demonstrates the potential for adaptation across different regional datasets and climate-sensitive decision-making scenarios.
In agricultural applications, improved forecasting capabilities can support more informed planting decisions, irrigation planning, and risk management strategies u nder increasingly variable climate conditions. In the energy sector, enhanced prediction of temperature and weather-related patterns can contribute to more efficient planning for renewable energy integration and infrastructure resilience. For emergency preparedness, scalable algorithmic solutions developed from Lee’s research can support broader federal and state-level disaster resilience systems by providing more accurate forecasting information and enabling more effective preparation for severe weather events.
Beyond environmental forecasting, Lee has also applied machine learning techniques to address challenges associated with aging population health. His research on elderly mental health risk prediction developed an ensemble learning framework using demographic and psychological indicators to identify potential anxiety and depression risks among older adults. The model achieved an AUC of 0.81, demonstrating the potential of interpretable machine learning approaches to support early-stage risk identification and targeted intervention strategies for aging populations.
This research direction reflects Lee’s broader goal of developing trustworthy AI systems that can be adapted to different public-interest applications. While environmental forecasting and elderly mental health represent different domains, both require reliable predictive technologies capable of transforming complex data into actionable insights. Through ensemble learning, deep neural architectures, and interpretable modeling methods, Lee demonstrates the ability to bridge technical innovation with practical societal needs.
Lee’s research also aligns with the growing emphasis on responsible and trustworthy artificial intelligence. By prioritizing scalable, reproducible, and interpretable machine learning frameworks, his work contributes to the development of AI systems designed not only for technical accuracy but also for transparency and practical adoption. These characteristics are particularly important for public-sector and community-based applications where reliability, accessibility, and responsible deployment are essential.
Through his continued research in predictive AI systems, Lee aims to further advance machine learning frameworks that strengthen climate resilience, support aging-related healthcare needs, and improve data-driven decision-making in the United States. His work demonstrates how advanced artificial intelligence methodologies can be transferred across critical public-interest domains to address emerging national challenges and promote more resilient communities.
（By Yao Xia)
About us
Senior Associate, Analytics at Dentsu America. Columbia MS in Applied Analytics. Skilled in SQL, R, Python, Tableau & NLP. Turning raw data into actionable insights and strategic business recommendations
Media ContactCompany Name: Chi-Han LeeContact Person: Media RelationsEmail: Send EmailPhone: 5104144797Country: United StatesWebsite: https://www.linkedin.com/in/chi-han-lee-1457091a1/
