Curriculum Vitae

Education

• 2015-2019, Ph.D. in Signal and Image Processing, IMAGES/LTCI, Télécom ParisTech / University Paris-Saclay
• 2012–2015, Master of Science, Department of Spatial Information and Digital Technology, China University of Mining and Technology (CUMT)
• 2008–2012, Bachelor of Science, Department of Spatial Information and Digital Technology, CUMT (Ranking : 4/160)

Vocational experience

• December 2020 - Present, Senior Machine Learning Researcher, Deep Planet, LONDON, UK
  • Using AttentionU-Net and high-resolution opticalremote sensing images to find Frankincense trees in the Somaliland, with a small number of training sets, the detection accuracy can reach 90%. This task was completed in cooperation with researchers from a computer vision group at Oxford University.
  • Multi-sensor time series missing value imputation with attention Conv-BiLSTM network. With the processed data, we can monitor vegetation with high-precision (10m) and high-frequency (5 days) data during the rainy season.
  • Improved the Savitzky-golay time series filtering method. The improved method allows us to retain useful temporal features.
  • Based on historical yield information, we used the Random Forest method to forecast the yield of grapes.
• July 2019 - November 2020, Research Fellow, University College London, LONDON, UK
  • Time series missing value imputation (regression, RF, LSTM/BiLSTM) with multisensor data
  • Multisensor time-series images (SAR/optical) fusion with 3D coherent radiative transfer model
  • Time-series forecasting with dual-stage attention-based RNNs
  • Algorithm evaluation over Google Cloud
• May 2015 - November 2015, Research assistant, The Chinese University of Hong Kong, HONG KONG, China
  • Time-series images processing and software development
  • Phase unwrapping with Minimum Cost Flow (MCF) optimization method

Main Skills

• Programming Python: (NumPy, SciPy, Pandas, Matplotlib, scikit-learn), C++ (Armadillo), MATLAB, JavaScript
• Machine learning frameworks: Keras, TensorFlow, PyTorch, Scikit-learn, XGBoost, MATLAB Deep Learning Toolbox
• Operating systems: GNU/Linux (Debian/ubuntu), Windows, macOS
• Cloud platform: AWS services(AWS Lambda, EC2, S3), Google Cloud (GCP/GEE/Colab)
• Data base: SQL
• Other: Docker, Git, Bitbucket, MLflow, Postman, JIRA, Postman, Confluence, LATEX, OpenOffice, ArcGIS

Publications

[1] Zhao, W. and Efremova, N., 2024. Prediction of Sentinel-2 multi-band imagery with attention BiLSTM for continuous earth surface monitoring (submitted)

[2] Zhao, W., Chuluunbat, G., Unagaev, A. and Efremova, N., 2024. Soil nitrogen forecasting from environmental variables provided by multisensor remote sensing images. arXiv preprint arXiv:2406.09812

[3] Zhao, W. and Efremova, N., 2024. Grapevine Disease Prediction Using Climate Variables from Multi-Sensor Remote Sensing Imagery via a Transformer Model. arXiv preprint arXiv:2406.07094.

[4] Zhao, W., Unagaev, A. and Efremova, N., 2024. Vineyard detection from multitemporal Sentinel-2 images with a Transformer model (No. EGU24-20025). Copernicus Meetings.

[5] Zhao, W. and Efremova, N., 2023. Soil Organic Carbon Estimation from Climate-related Features with Graph Neural Network. arXiv preprint arXiv:2311.15979.

[6] Zhao, W., Yin, F., Ma, H., Wu, Q., Gomez-Dans, J. and Lewis, P., 2023. Combining multitemporal optical and SAR data for LAI imputation with BiLSTM network. arXiv preprint arXiv:2307.07434.

[7] Zhao, W., Deledalle, C.A., Denis, L., Maître, H., Nicolas, J.M. and Tupin, F., 2019. Ratio-based multitemporal SAR images denoising: RABASAR. IEEE Transactions on Geoscience and Remote Sensing, 57(6), pp.3552-3565.

[8] Zhao, W., Deledalle, C.A., Denis, L., Maître, H., Nicolas, J.M. and Tupin, F., 2023. Multitemporal SAR images change detection and visualization using RABASAR and simplified GLR. arXiv preprint arXiv:2307.078

[9] Zhao, W., Riot, P., Deledalle, C.A., Maître, H., Nicolas, J.M. and Tupin, F., 2024. Patch-based adaptive temporal filter and residual evaluation. arXiv preprint arXiv:2402.09561.

[10] Zhao, W., Deledalle, C.A., Denis, L., Maître, H., Nicolas, J.M. and Tupin, F., 2018, July. RABASAR: A fast ratio based multi-temporal SAR despeckling. In IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium (pp. 4197-4200). IEEE.

[11] Lobry, S., Denis, L., Zhao, W. and Tupin, F., 2017. Décomposition de séries temporelles d’images SAR pour la détection de changement. Traitement du Signal, 34.

[12] Zhao, W., Lobry, S., Maitre, H., Nicolas, J.M. and Tupin, F., 2017, June. Urban area change detection based on generalized likelihood ratio test. In 2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) (pp. 1-4). IEEE.