About Me


I am a Ph.D. candidate specializing in ensuring pedestrian safety for autonomous vehicles and also a member of the ITS Lab at the Institute of Computer Science, University of Tartu. I have been involved in many projects related to Intelligent Transportation Systems.

I received my MSc in Robotics & Computer Engineering from the University of Tartu, Estonia, in 2019, and my Bachelor’s degree from the University of Shanghai for Science and Technology, China.

Meanwhile, I’m also a GYM enthusiast, who was a professional swimmer for nearly 12 years.

Research Field

  • Machine Learning
  • Vision Transformer
  • Pedestrian Tracking
  • Intelligent Transportation Systems
  • Data Mining


  • Ph.D. candidate in Computer Science (Current)
    Institute of Computer Science – University of Tartu – Estonia
  • MSc in Robotics and Computer Engineering
    Institute of Technology – University of Tartu – Estonia
  • Exchange program in Computer System Engineering
    Tallinn University of Technology – Estonia
  • Bachelor in Information management and Information Systems
    Faculty of Management – University of Shanghai for Science and Technology – China

List of Projects

  • Name: ModSplit (2021-2022)
    Cooperation: Tartu Linnavalitsus
    Description: Designing a methodology for real-time visualisation and estimation of mobility modality distribution in Tartu City
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  • Name: NutikasUGV (2018-2021)
    Cooperation: AS Milrem
    Description: Applied research on system of sensors and software algorithms for safety and driver assistance on remotely operated ground vehicles for off-road applications
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  • Name: MAUM (2018-2019)
    Cooperation: Taxify (now Bolt)
    Description: Research in Methods and Algorithms for Urban Mobility
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  • S. Wu, A. Hadachi, D. Vivet and Y. Prabhakar, “This is The Way: Sensors Auto-calibration Approach Based on Deep Learning for Self-driving Cars,” in IEEE Sensors Journal, doi: 10.1109/JSEN.2021.3124788. [Paper][Code]
  • Wu, Shan, et al. “NetCalib: A Novel Approach for LiDAR-Camera Auto-calibration Based on Deep Learning.” 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021. [Paper][Code]
  • Wu, Shan, and Amnir Hadachi. “Road Surface Recognition Based on DeepSense Neural Network using Accelerometer Data.” 2020 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2020. [Paper][Code]
  • Lind, A.; Wu, S.; Hadachi, A. Application of Gaussian Mixtures in a Multimodal Kalman Filter to Estimate the State of a Nonlinearly Moving System Using Sparse Inaccurate Measurements in a Cellular Radio Network. Sensors 202323, 3603. https://doi.org/10.3390/s23073603 [Paper]
  • Khoshkhah, K.; Pourmoradnasseri, M.; Hadachi, A.; Tera, H.; Mass, J.; Keshi, E.; Wu, S. Real-Time System for Daily Modal Split Estimation and OD Matrices Generation Using IoT Data: A Case Study of Tartu City. Sensors 202222, 3030. https://doi.org/10.3390/s22083030 [Paper]