Talks and Presentations

Presentations

A Generative AI Approach to Assess Ecosystem Risks and Restoration Potential. Accelerating Computing for Emerging Sciences (ACES) Workshop (July 2025)
Advancing Hydrological Predictions at National Scales with Deep Learning. Tethys Summit (July 2025)
A Foundation Model for Understanding Landscape-Ecosystem Coevolution. Lawrence Berkeley National Laboratory (June 2025, Poster)
From RNN to Transformer: a comprehensive evaluation of time-series deep learning models. PIERS 2025 (April 2025)

Foundation Model for Understanding Landscape Hydrologic Coevolution. AGU Fall Meeting (Dec 2024)
A General Framework for Hydrological Modeling: From RNN to Transformer. AGU Fall Meeting (Dec 2024)
Can Attention Models Surpass LSTM in Hydrology? EGU Conference (April 2024)

Exploring Transformer Architecture in Predictive Hydrologic Modeling. AGU Fall Meeting (Dec 2023)
Enhancing Soil Moisture Forecasting Using the PatchTST Model for Agricultural Risk Management. IBM Research (Aug 2023)
A Multiscale Deep Learning Model Integrating Satellite‑Based and In‑Situ Data for High‑Resolution Soil Moisture Predictions. EGU Conference (April 2023)

The Machine Learning Strategy for Rainfall‑Induced Landslide Susceptibility Prediction. Google Geo for Good Summit (Oct 2022)
A Global Soil Moisture Dataset Based on a Multi‑Scale Deep Learning Framework. HydroML Symposium (May 2022)

Automated Deep‑Learning‑Based Soil Moisture Planning and Forecast System for Planning Against Natural Disasters. AGU Fall Meeting (Dec 2020)