Sihan Shao (邵思涵)

I am a M.Sc. student, currently working at the Millimetre Wave and THz Techniques Lab. at Aalto University, advised by Prof. Zachary Taylor and D.Sc. Aleksi Tamminen. My research interests includes computational imaging in optics and THz, computer vision and antenna design .

I am pursuing a M.Sc. in Electronics and Nanotechnology (major) and Machine Learning (minor) from Aalto University, Finland. Previously, I obtained my B.Eng. degree in Opto-Electronics Information Science and Engineering at Changchun University of Science and Technology University (CUST), China.

In my free time, I enjoy running, swimming, and sauna.

Email / CV / GitHub / LinkedIn

Research

I'm interested in computational imaging, computer vision, machine learning, and optimization.

	Rim-Integrated Bluetooth Antenna for Smart Watches with Full-Metallic Structure Ali Kourani, Sihan Shao, Mohamed Raanen, Jan Bergman, Rasmus Luomaniemi, Jari Holopainen European Conference on Antennas and Propagation 2025, 2024 paper / code / This paper explores the design, manufacturing, and a prototype measurement of a Bluetooth antenna integrated into the metallic rim of a smartwatch.
	Neural Dispersive Hologram for Computational Submillimeter-wave Imaging Sihan Shao, advised by Prof. Zachary Taylor and D.Sc. Aleksi Tamminen. Master's Thesis, 2024 paper / code / Developed a multi-wavelength wave propagation simulation using PyTorch, co-optimized a sub-millimeter imaging system’s hardware and neural networks, introduced a surrogate gradient method for optimizing quantized holograms, and validated the system with real measurements.
	Quantized THz Diffractive Optics Design via Automatic Differentiation Sihan Shao, Aleksi Tamminen, Samu-Ville Pälli, Shanuka Gamaethige, Zachary Taylor International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz), 2024 paper / code / We explored the angular spectrum method (ASM) with automatic differentiation to optimize highly quantized diffractive optical elements (DOEs) for terahertz applications, facilitating precise control over radiation modulation while accounting for practical manufacturing limitations of 3D printer.
	Harnessing Frequency Diversity for Improved Holographic Imaging Systems Shanuka Gamaethige, Samu-Ville Pälli, Aleksi Tamminen, Sihan Shao, Zachary Taylor, Marlene Bonmann, Tomas Bryllert, Jan Stake, Duncan A. Robertson International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz), 2024 The study examines how frequency diversity over time in a 340 GHz FMCW radar system with a dispersive hologram enhances target imaging and discrimination by capturing more detailed spatial information.
	SAR Ship Detection from Complex Background Based on Dynamic Shrinkage Attention Mechanism Sihan Shao, Haoran Li, Shifeng Wang 2021 SAR in Big Data Era conference (BIGSARDATA), 2021 paper / We developed a SAR ship detection method addressing challenges like speckle noise and complex backgrounds using a feature-level denoising approach called dynamic shrinkage attention.
	Soft Thresholding Attention Network for Adaptive Feature Denoising in SAR Ship Detection Rui Wang, Sihan Shao, Mengyu An, Jiayi Li, Shifeng Wang & Xiping Xu IEEE Access, 2021 paper / code / Proposed a feature-level denoising methodology for synthetic aperture radar ship detection, leveraging soft thresholding and attention mechanisms, which enhanced detection precision across several neural network models.
	Lidar Sensor-Based Object Recognition Using Machine Learning Rui Wang, Mengyu An, Sihan Shao, Mingyang Yu, Shifeng Wang & Xiping Xu Journal of Russian Laser Research, 2021 paper / We developed a Lidar-based data collection system and applied filtering, clustering, PCA, and SVM techniques to improve the classification efficiency and accuracy of pedestrians and vehicles in point cloud data.