Bio
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
Education
- 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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Publications
- Wu, Shan, et al. “MOTT: A new model for multi-object tracking based on green learning paradigm.” AI Open 4 (2023): 145-153. [Paper]
- Wu, Shan, et al. “Transformer for multiple object tracking: Exploring locality to vision.” Pattern Recognition Letters 170 (2023): 70-76. [Paper]
- 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 2023, 23, 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 2022, 22, 3030. https://doi.org/10.3390/s22083030 [Paper]
- 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]