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Yingli Tian

As seen in: Nature, MDPI
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Articles

Machine learning for cryospheric mass movements: challenges and pathways

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By Te Pei, Louise Farquharson, Margaret M. Darrow, Tong Qiu, Chaopeng Shen, Yingli Tian, Shemin Ge, Nicole Guinn, Ingmar Nitze, Charlotte Pearson, Louise Vick, Michael West, Chandi Witharana, Gabriel Wolken, Joseph Young, Lukas U. Arenson, Helena Bergstadt, Savinay Nagendra, Alexandra Runge
| Nature Verified
Abstract Cryospheric mass movements (CMMs) are accelerating under rapid Arctic and high-mountain warming, yet understanding and predicting them remains challenging due to sparse observations, complex multi-scale processes, and shifting environmental baselines. This Perspective argues that machine learning should advance CMM science not only through prediction, but through transferable and physics-aware inference.
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[2601.12785] Distilling Time Series Foundation Models for Efficient Forecasting

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By Chuanguang Yang, Szu-Yu Chen, Yingli Tian, Kuiye Ding
| arXiv
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AI-Driven Robust Kidney and Renal Mass Segmentation and Classification on 3D CT Images

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By Jingya Liu, Onur YILDIRIM, Oguz Akin, Yingli Tian
| MDPI
This is an early access version, the complete PDF, HTML, and XML versions will be available soon. Open AccessArticle by Jingya Liu 1, Onur Yildirim 2, Oguz Akin 2 and Yingli Tian 1,* 1 Department of Electrical Engineering, The City College of New York, 275 Convent Ave, New York, NY 10031, USA 2 Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA * Author to whom correspondence should be addressed.
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