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About

Jianing Qiu is an interdisciplinary researcher working at the intersection of artificial intelligence, health informatics, computer vision, and robotics. His most recognized contribution lies in the study of large AI models — often called foundation models — and their transformative applications in healthcare. His 2023 paper on this topic has accumulated an impressive 224 citations, establishing him as a prominent voice in understanding how systems like ChatGPT and other large-scale pretrained models can be adapted for clinical and health informatics tasks, while candidly addressing their challenges and limitations. Beyond health AI, Qiu has demonstrated a broad technical range. His work on event-based monocular depth estimation tackles real-world autonomous driving challenges under adverse nighttime conditions, while his research on egocentric trajectory forecasting explores wearable-camera systems to assist visually impaired individuals in crowded environments. More recently, he has ventured into robotics, contributing a large-scale dataset for human throw-and-catch demonstrations and pioneering bimodal tactile tomography techniques for robotic tissue palpation in surgical settings. Collectively, Qiu's research reflects a commitment to bridging cutting-edge AI with meaningful real-world applications — from hospitals to autonomous systems — making his work highly relevant for students pursuing AI, medical technology, or human-robot interaction.