Recent Updates

• AGU2026 asbtracts:

  • Reassessing Western U.S. Climate Extremes with a 100-Member, 9-km Machine-Learning–Downscaled Ensemble

  • Biosphere-atmosphere feedbacks, sustained drought, and ecological memory as mechanisms enabling wildfire prediction on S2S timescales (oral)

  • Wildfire Legacy Effects on Soil Respiration Persist for Decades Across Western U.S. Forests (oral)

  • Hotter but calmer: warming suppresses downslope wildfire spread

  • Western U.S. snow projections for 25,000 years: A 9-km AI-boosted ensemble (oral)

• Journal paper submitted: Emulating Dynamically Downscaled Snow Water Equivalent Across the Western United States: Training Data Composition Is Essential for Robust Climate Projections, Sep. 2026, GMD

• Journal paper submitted: Weakened downslope winds and slower simulated nighttime spread of the Camp Fire under warming, npj Natural Hazards, Aug. 2026

• Journal paper submitted: Temporal Coverage over Density: Parsimonious Training-Set Design for ML Climate Downscaling, AIES, Aug. 2026

• Journal paper submitted: Assessing Wildfire Risk During Santa Ana Wind Events Using Interpretable Machine Learning, JAMC, Aug. 2026

• Preprint: Temporal Coverage over Density: Parsimonious Training-Set Design for ML Climate Downscaling, Jun. 2026, https://arxiv.org/abs/2606.07898

• AGU2025 abstracts: 

  • Machine Learning for Efficient Climate Downscaling: Balancing Data Parsimony, Accounting for Time-Stationarity, and Ensuring Accessibility (oral)

  • Leveraging Machine Learning to Assess Santa Ana Wind-Driven Wildfire Risk in Southern California. Dec. 2025 (oral)

• Co-author of invited talk at AGU 2025: Integrating Atmospheric Science, Fire Science, and Public Policy to reduce Wildfire Risk in Southern California. Dec. 2025.

• Started working at ATMOS, UCLA, Nov. 2024

• Paper accepted: Self Supervised Vision for Climate Downscaling, IJCAI May 2024

• Paper submitted: Self Supervised Vision for Climate Downscaling, IJCAI Feb. 2024

• Preprint now online: Self Supervised Vision for Climate Downscaling, https://arxiv.org/abs/2401.09466

• Article submitted: Explaining Gender Wage Gaps: The Role of Gender-Stereotypical Communication During a Major Organizational Shock, Nature Human Behavior, Dec. 2023

• Paper accepted: Generating High-Resolution Regional Precipitation Using Conditional Diffusion Model, KAIA Nov. 2023

• Paper accepted: K. Singh, Y.-C. Tsai, C.-T. Li, M. Cha, and S. -D. Lin, “GraphFC: Customs Fraud Detection with Label Scarcity,” in 31st ACM International Conference on Information and Knowledge Management (CIKM2023), Jun. 2023

• Paper accepted: N. Shidqi, C. Jeong, S. Park, K. Singh, A. B. Nellikkattil, E. Zeller, M. Cha, "Leveraging Temporal Information for Self-Supervised Climate Downscaling in North America Region", Korea Computer Congress (KCC), Jun. 2023.

• Article published: Multi-Stage Learning Model for Hierarchical Tie Valence Prediction, ACM Transactions on Knowledge Discovery from Data (TKDD), Jan. 2023.

• Paper accepted: Self-supervised Deep Learning for Climate Downscaling, IEEE International Conference on Big data and Smart Computing (BIGCOMP 2023) , Oct. 2022.

• Article submitted: GraphFC: Graph Neural Networks for Customs Fraud Detection, IEEE Transactions on Knowledge and Data Engineering (TKDE), Jul. 2022.

• Paper accepted: Downscaling the Earth System Modeling with Deep Learning, SIGKDD Conference on knowledge discovery and data mining (KDD 2022), Mar. 2022.