I specialize in bridging the gap between advanced AI algorithms and real-world surgical/financial applications. Currently, my research focuses on 3D reconstruction and scene understanding in minimally invasive surgery.

• Medical AI: 3D Surgical Scene Reconstruction, Depth Estimation (Retinal/Abdominal), SLAM.
• Quantitative Finance: Systematic Trading Frameworks, Data Mining with Tushare, Backtesting Strategies.
• Core Tools: Python, PyTorch, OpenCV, C++, Git, Linux.

• Developed a robust framework for 3D reconstruction of surgical scenes using monocular video sequences.
• Focused on handling tissues and instrument occlusion in complex surgical environments.
• Keywords: Monocular SLAM, Surgical Vision.

• Built a specialized synthetic dataset for posterior segment ophthalmic surgery.
• Implemented SOTA depth estimation models achieving high precision in texture-less environments.

• A systematic stock analysis and trading system leveraging the Tushare API.
• Features automated data acquisition, technical indicator calculation, and strategy backtesting.

• Personal Website: https://sissi-lu.github.io
• Research Focus: Computer Vision, AI Agents, Quantitative Analysis.

"I escaped the walls of a system that once defined me, and now I’m here to build—not just any AI, but the kind that reshapes what’s possible." 🚀