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Dr. Qin Xuye is an Assistant Professor at the School of Computer Science, Peking University. His primary research interests lie at the intersection of artificial intelligence and scientific discovery, often termed AI for Science (AI4Science). He focuses on developing novel machine learning methodologies, particularly graph neural networks and deep learning models, to address complex challenges in fields such as drug discovery, materials science, and quantum chemistry. Dr. Qin aims to build robust, interpretable, and efficient AI models that can accelerate the pace of scientific research and lead to new discoveries. He received his Ph.D. from The Hong Kong University of Science and Technology (HKUST) in 2019.
Qin Xuye's work history includes a series of influential roles in various companies. Here is a detailed list of his professional journey:
Nominated for the Baidu Scholar AI Young Scholar Award in 2021, recognizing his potential and contributions to the field of Artificial Intelligence.
Contributed significantly to the development and application of graph neural networks for molecular modeling, materials property prediction, and other scientific domains. His work often involves creating novel architectures tailored for scientific data.
Authored and co-authored numerous influential research papers published in top-tier AI conferences (e.g., NeurIPS, ICML, ICLR) and prestigious scientific journals (e.g., Nature Communications), advancing knowledge in AI and its scientific applications.
Actively involved in creating AI-driven tools and platforms that aim to accelerate the drug discovery pipeline, from target identification to lead optimization, by leveraging machine learning techniques.
As an Assistant Professor at Peking University, he teaches and mentors students in computer science and artificial intelligence, contributing to the development of the next generation of AI researchers and practitioners.
Shanghai Jiao Tong University - Year 2011
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Xprobe is an AI-powered External Attack Surface Management (EASM) platform. It assists organizations in continuously discovering, assessing, and prioritizing their external-facing digital assets and associated vulnerabilities. The platform aims to provide a comprehensive view of an organization's internet exposure, helping security teams to proactively manage risks and reduce their attack surface before malicious actors can exploit them.
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