Ezekiel W. Cullen Building at the University of Houston, framed by flowers

About Me

Welcome! I am now a Ph.D student in Geosensing Systems Engineering and Sciences at the University of Houston. My current research focuses on remote sensing of hydrology, with a specific emphasis on flood inundation forecasting. For more information on my research interests and experience, check out my research page!

Education

  • Ph.D. in Geosensing Systems Engineering and Sciences, University of Houston, U.S., Present.

  • M.Sc. in Communication Engineering, University of Electronic Science and Technology of China, China, 2024.

  • B.Sc. in Communication Engineering, Hefei University, China, 2021.

News

  • 2026.10 — I received a computing allocation through NSF’s ACCESS program for my ongoing research on Physics-guided Super Resolution for High Spatiotemporal Surface Water Mapping.
  • 2025.12 — I presented my work, Real-time Flood Extent Forecasting Using an Encoder-decoder ConvLSTM Integrated with Data Assimilation, in a Hydrology session at AGU25 in New Orleans. Abstract · Poster

Publications

Journal

Conference

Research Experience

Real-time flood extent forecasting with deep learning and data assimilation

  • Combine an encoder-decoder ConvLSTM with Ensemble Kalman Filter (EnKF) data assimilation to forecast flood extent in the Upper Mississippi Alluvial Plain.
  • Use VIIRS water fraction observations for 3-day forecasts and reduce error accumulation through satellite observation updates.

AGU 2025 · Abstract · Poster

Open set recognition based on a hyperspherical embedding network

  • Model extracted features onto a unit hypersphere with von Mises-Fisher (vMF) distribution to improve the inter-class separability of feature representations.

  • Measure the similarity of samples to each class center and design a discrimination criterion to detect unknown targets based on this metric.

  • Calibrate the predicted probability based on uncertainty estimation.

  • Implement the algorithm and evaluate the performance on two public satellite imagery datasets (MSTAR).

A threshold-free open-set recognition network for satellite imagery

  • Propose a threshold-free open-set learning network for satellite target recognition.

  • Improve the network structure of GAN to formulate the open-set recognition problem as a K+1 classification task.

  • Implement the algorithm, evaluate the performance, and compare it with four SOTA models on two public satellite datasets (MSTAR and SAMPLE).

Honors and Awards

  • 2022 Outstanding Graduate Student
  • 2021 Outstanding Graduate of Anhui Province
  • 2019 University Special Prize Scholarship
  • 2018 University Special Prize Scholarship