Xiaoran Yan
Dr. Xiaoran Yan is an Associate Professor at the AI Innovation Center, Beihang University (Hangzhou International Campus). His research interests include Neural-Symbolic Agents, AI for Science, Graph Learning, Knowledge Graphs, Domain-specific Large Language Models, Multimodal Alignment, Federated Learning, and Differential Privacy. He received his Ph.D. in Computer Science from the University ...details>
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Research Focus
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Social Affiliations
Member of the Informatization Working Committee, Chinese Astronomical Society
Executive Committee Member, CCF Technical Committee on Information Systems
Adjunct Faculty, College of Computer Science and Technology, Zhejiang University
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Education Experience
2003/9-2007/6Zhejiang University Computer Science and Technology
2007/8-2013/7University of New Mexico | Computer Science and Technology | Doctoral degree | Postgraduate (Doctoral) | Ph.D. Advisor: Cristopher Moore
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Work Experience
2013/10-2015/10Information Sciences Institute | University of Southern California | Research Associate | Left
2015/11-2021/4Network Science Institute | Indiana University | Assistant Research Scientist | Left
2021/4-2026/2Research Center for Scientific Data Hub / Big Data Intelligence Research Center | Zhejiang Lab | Assistant Director | Associate Research Scientist | Off duty | Lead the collective intelligence team; establish three research directions: federated data aggregation, joint knowledge representation, and collective decision-making. Built a 20-person research/engineering team from scratch. Collaborate with experts in a
2026/2-NowAI Innovation Center for Science and Technology | Beihang University (Hangzhou International Innovation Institute) | Associate Professor | Associate Professor | On duty | Teach AI for Science and machine learning courses; research how AI drives scientific discovery and how science inspires next-generation AI architectures; focus on neuro-symbolic agent design and its applications in science and industry
- 1.Open science, communal culture, and women's participation in the movement to improve science.Proceedings of the National Academy of Sciences, 117(39), 24154--24164
- 2.Detecting Climate Teleconnections With Granger Causality.Geophysical Research Letters, 48(18), e2021GL094707
- 3.CADRE: A Cloud-Based Data Service for Big Bibliographic Data.Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM 2021), 4283--4292
- 4.Characterization of gene expression patterns in mild cognitive impairment using a transcriptomics approach and neuroimaging endophenotypes.Alzheimer's & Dementia
- 5.Deep Reinforcement Learning-Based Energy-Efficient Edge Computing for Internet of Vehicles.IEEE Transactions on Industrial Informatics, 18(9), 6308--6316
- 6.Unifying and Improving Graph Convolutional Neural Networks with Wavelet Denoising Filters.Proceedings of the ACM Web Conference 2023, 177--187
Research Group
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