Summary
Lalith Sharan is a postdoctoral researcher specializing in safe and trustworthy surgical AI, combining computer vision, deep learning, and healthcare informatics to improve patient outcomes across large-scale, multi-institutional surgical datasets. Building on a summa cum laude PhD from Heidelberg where he developed deep learning tools for precision mitral valve repair, he now focuses on surgical vision-language models, medical foundation models, rare event detection, and interpretable AI. With 8 years of experience, 15+ high-impact publications, multiple awards and invited talks, he brings both strong research rigor and practical impact in real-world operating-room data. He has mentored numerous students and actively contributes to science communication, reaching thousands through public presentations and science slams. Unusually for an academic, he blends systems-level thinking about foundation models with hands-on engineering for registration, segmentation, and depth reconstruction used in surgical workflows. Based in Strasbourg, he is open to collaborations that translate trustworthy AI research into safer surgical practice.
8 years of coding experience
Master's degree, Master's degree at Otto-von-Guericke University Magdeburg
Bachelor of Engineering (BE), Bachelor of Engineering (BE) at Manipal Institute of Technology
Doctor of Philosophy - PhD, Doctor of Philosophy - PhD at Heidelberg University