工作及教育经历

2022.09-至今       中国科学院国家空间科学中心，项目研究员，硕士生导师 

2022.01-2022.09  中国科学院国家空间科学中心，副研究员，硕士生导师 

2019.08-2019.11  英国谢菲尔德大学，数学和统计系，访问学者

2013.01-2021.12  中国科学院国家天文台， 太阳活动预报中心， 副研究员， 硕士生导师

2010.07-2012.12  中国科学院国家天文台， 太阳活动预报中心， 博士后

2006.09-2010.06   哈尔滨工业大学， 先进动力控制与可靠性研究所， 博士

2004.09-2006.07   哈尔滨工业大学，工业电子技术研究所， 硕士

2000.09-2004.07   哈尔滨工业大学 ，计算机与电气工程学院， 本科

个人简介

研究方向为科学智能(AI for science)，主要包括 AI for space science 和 AI for medicine。 在空间天气预报建模、医学图像处理、疾病风险预测、治疗策略选择等相关方向开展研究工作。主 持国家自然科学基金 2 项，主持军委科技委国防创新项目 2 项，作为子课题负责人和主要人员参加 国家重点研发项目，军民融合项目，自然科学基金项目。发表 SCI 论文 20 余篇，google 学术总引 用 300 余次，取得发明专利 2 项。积极参与国际合作与交流，多个国际期刊审稿人，获得 2021 年

度 RAA 期刊杰出审稿人，国际会议邀请报告和口头报告 10 余次。文章 Deep Learning Based Solar Flare Forecasting Model. I. Results for Line-of sight Magnetograms 获 得 2021 年 天 文 和 天 体 物 理 领 域 中 国 高 被 引 文 章 奖 https://ioppublishing.org/china-top-cited- paper-award/。出版专著 1 本《Deep Learning in Solar Astronomy》(ISBN 978-981-19- 2745-4)。

项目情况

(1) 国家重点研发计划，“第 25 太阳周重大爆发活动与空间天气研究”第 4 课题“太阳活动数 值模 拟与预报”国家天文台负责人，2022.01-2026.12，230 万元。

(2)国家重点研发计划，“政府间国际科技创新合作”重点专项，基于人工智能的稀疏阵列合成波 束效应消除关键技术研究，骨干成员，2023.1-2025.12，30 万(课题共 200 万)，

(3)国家自然科学基金面上项目， 项目负责人， 基于海量观测数据的太阳爆发事件预报建模研究， 63 万元， 2019.01-2022.12

(4)国家自然科学基金青年科学基金项目， 项目负责人， 日冕物质抛射源区特性及三维重构研究， 29 万元， 2014.01-2016.12

(5)国家自然科学基金天文联合基金，联合项目天文台负责人， 融合太阳磁场和黑子序列特征的 太阳耀斑深度学习预报模型研究， 60 万元， 2019.01-2021.12

(6)ISSI-BJ 2021 (International Space Science Institute Beijing)，核心成员，Step forward in solar flare and coronal mass ejection (CME) forecasting，2022.01-2024.01

(7)国防科技创新特区，课题负责人，机理与数据相互驱动的空间环境事件预报模型研究，30 万 元，2019.10-2020.9

(8)国防科技创新特区，课题负责人，机理与数据相互驱动的空间环境事件预报模型研究，50 万 元，2020.11-2021.10

(9)中国医学科学院医学与健康科技创新工程，主要成员，“大庆糖尿病研究”队列长期随访 (1986-2020)，200 万元，2020.1-2020.12

(10)中国气象局空间天气重点实验室开放课题，项目负责人， 基于深度学习方法的太阳耀斑预报 模型研究， 4 万元， 2018.01-2019.12

(11)中国科学院国家天文台青年基金，项目负责人， 日冕物质抛射源区特性研究， 5 万元， 2014.01-2015.01

发表文章及专利

AI for medicine

(一)已发表

(1) Feng X, Zhang C, Huang X#(共同一作), Liu J, Jiang L, Xu L, Tian J, Zhao X, Wang D, Zhang Y, Sun K, Xu B, Zhao W, Hui R, Gao R, Yuan J, Wang J, Duan Y, Song L. Machine learning improves mortality prediction in three-vessel disease. Atherosclerosis. 2023, 367:1-7. doi: 10.1016/j.atherosclerosis.2023.01.003.

(2) Jie L, Feng XX, Duan YF, Liu JH, Zhang C, Jiang L, Xu LJ, Tian J, Zhao XY, Zhang Y, Sun K, Xu B, Zhao W, Hui RT, Gao RL, Wang JZ, Yuan JQ, Huang X*(共同通讯),

Song L. Using machine learning to aid treatment decision and risk assessment for severe three-vessel coronary artery disease. J Geriatr Cardiol. 2022, 19(5):367-376. doi: 10.11909/j.issn.1671-5411.2022.05.005.

(3) Feng, X.; Zhang, S.; Xu, L.; Huang, X*(共同通讯); Chen, Y. Robust Classification Model for Diabetic Retinopathy Based on the Contrastive Learning Method with a Convolutional Neural Network. Appl. Sci. 2022, 12, 12071. https://doi.org/ 10.3390/app122312071

(4) Suxiang Yu, Xinxing Feng, Bin Wang, Hua Dun, Shuai Zhang, Ruihong Zhang, Xin Huang*(通讯作者), Automatic Classification of Cervical Cells Using Deep Learning Method, IEEE Access, 2021, 9, 32559-32568

(5) Suxiang Yu, Shuai Zhang, Bin Wang, Hua Dun, Long Xu, Xin Huang, Ermin Shi, and Xinxing Feng, Generative adversarial network based data augmentation to improve cervical cell classification model, Mathematical Biosciences and Engineering, 2021, 18 (2), 1740-1752

(6) Du Yong, Wang Yu, Huang Xin, Hu Qinghua, Driver state analysis based on imperfect multi-view evidence support，Neural Process Lett, 2018

专利

死亡风险预测模型的生成方法、终端及计算机存储介质，黄鑫、段岩峰，2021.03.24，实 审

(二)在研

(1) 心肌病患者猝死风险预测模型，合作单位:中国医学科学院阜外医院

(2) 心肌病患者心衰风险预测模型，合作单位:中国医学科学院阜外医院

(3) 冠脉三支患者心梗风险预测模型，合作单位:中国医学科学院阜外医院

(4) 冠脉三支患者脑卒中风险预测模型，合作单位:中国医学科学院阜外医院

(5) Automatic Left Ventricle Segmentation on Echocardiograms based on the ConvNeXt network，合作单位:东莞人民医院

(6) An Attempt to Enhance the Accuracy of Left Venticle Segmentation in Echocardiography by Using Transformer，合作单位:东莞人民医院

AI for space science

(一)已发表

(1) Dezhi Sun, Xin Huang*(通讯作者), Zhongrui Zhao, and Long Xu, Deep-learning- based Solar Flare Forecasting Model. III. Extracting Precursors From EUV Images, ApJS，accepted.

(2) Zhongrui Zhao, Long Xu, Xiaoshuai Zhu, Xinze Zhang, Sixuan Liu, Xin Huang*(共 同通讯), Zhixiang Ren, and Yonghong Tian, A Large-Scale Dataset of Three- Dimensional Solar Magnetic Fields Extrapolated by Nonlinear Force-Free Method， Scientific Data，accepted.

(3) Yang Chen, Shane Maloney, Enrico Camporeale, Xin Huang, Zhenjun Zhou, Editorial: Machine Learning and Statistical Methods for Solar Flare Predictions, Frontiers in Astronomy and Space Sciences, section Stellar and Solar Physics， accepted.

(4) Sixuan Liu, Long Xu, Zhongrui Zhao, R. Erdélyi, M. B. Korsos, and Xin Huang*(通 讯作者), Deep Learning Based Solar Flare Forecasting Model. II. Influence of Image Resolution, 2022, ApJ, 941, 20

(5) Huaning Wang, Changhui Rao1, Naiting Gu, Libo Zhong, and Xin Huang, Light Bridge and Magnetic Field in a Solar Active Region, 2022, ApJ, 939, 49

(6) Du, Z.L., Huang, X., Yan, Y. Revising a less-reliable prediction for the solar cycle based on the variation in correlation. Indian J Phys (2022). https://doi.org/10.1007/s12648-022-02424-x

(7) Yanru Sun, Zongxia Xie, Haocheng Wang, Xin Huang, Qinghua Hu, Solar Wind Speed Prediction via Graph Attention Network, Space Weather, 2022, https://doi.org/10.1029/2022SW003128

(8) M. B. Korsos, R. Erdelyi, X. Huang, and H. Morgan, Magnetic Helicity Flux Oscillations in the Atmospheres of Flaring and Non–flaring Active Regions, ApJ, 2022, 933, 66

(9) Robertus Erdelyi, Marianna Brigitta Korsos, Xin Huang, Yong Yang, Danielle Pizzey, Steven A Wrathmall, Ifan Hughes, Martin Dyer, Vikram S Dhillon, Bernadett Belucz et al., The Solar Activity Monitor Network – SAMNet, J. Space Weather Space Clim. 2022, 12, 2

(10) Lin Quan, Long Xu, Ling Li, Huaning Wang, Xin Huang*(通讯作者), Solar Active

Region Detection Using Deep Learning，Electronics,2021, 10(18), 2284

(11) Yanru Sun, Zongxia Xie, Yanhong Chen, Xin Huang, Qinghua Hu. SolarWind Speed PredictionWith Two‐Dimensional Attention Mechanism. Space Weather, 2021, 19(7): e2020SW002707.

(12) Jiajia Liu, Yimin Wang, Xin Huang, Marianna B. Korsos, Ye Jiang, Yuming Wang, Robert Erdelyi, Reliability of AI-generated magnetograms from only EUV images, Nature Astronomy, 2021, 5, 108–110

(13) X. Huang*(通讯作者), I. Usoskin, L. Y. Zhang, H. N. Wang, Big Data Processing and Modeling in Solar Physics, Advances in Astronomy, 2020, 6967925

(14) Feng Li, Gan Weiqun, Liu Siqing, Wang Huaning, Li Hui, Xu Long, Zong Weiguo, Zhang Xiaoxing, Zhu Yaguang, Wu Haiyan, Chen Anqin, Cui Yanmei, Dai Xinghua, Guo Juan, He Han, Huang Xin, Lu Lei, Song Qiao, Wang Jingjing, Zhong Qiuzhen, Chen Ling, Du Zhanle, Guo Xingliang, Huang Yu, Li Hu, Li Ying, Xiong Senlin, Yang Shenggao, Ying Beili，Space Weather Related to Solar Eruptions With the ASO-S Mission，Frontiers in Physics，8，2020，DOI=10.3389/fphy.2020.00045

(15)Huang Xin*(通讯作者), Wang Huaning, Xu Long, et. al., Deep learning based solar flare forecasting model. I. results for line-of-sight magnetograms, The Astrophysical Journal，2018

(16) Han He, Huaning Wang, Zhanle Du, Xin Huang, Yan Yan, Xinghua Dai, Juan Guo, and Jialong Wang，A brief history of Regional Warning Center China (RWC- China)，Hist. Geo Space. Sci., 9, 41–47, 2018，https://doi.org/10.5194/hgss-9-41- 2018

(17) Yan, Yan; Du, Zhan-Le; Wang, Hua-Ning; He, Han; Guo, Juan; Huang, Xin; Zhu, Xiao-Shuai; Dai, Xing-Hua; Lin, Gang-Hua ， Decades of Chinese Solar and Geophysical Data ， IAU Symposium, vol. 340, pp. 71 – 72, 2018. doi:10.1017/S1743921318001916.

(18)Huang Xin*(通讯作者), Wang Huaning, Xu Long, et. al., Learning solar flare forecasting model from magnetograms, IEEE International Conference on Visual Communications and Image Processing, 2017

( 19 ) Dai Xinghua, Wang Huaning, Du Zhanle, He Han, Huang Xin, An improvement on mass calculations of solar coronal mass ejections via polarimetric reconstruction, Astrophysical Journal, 2015

(20)Chen Huadong, Zhang Jun, Ma Suli, Yang Shuhong, Li Leping, Huang Xin, Xiao Junmin, Confined Flares in Solar active region 12192 from 2014 October 18 to 29, Astrophysical Journal Letters, 2015

( 21 ) Dai Xinghua, Wang Huaning, Huang Xin, et. al., The classification of ambiguity in polarimetric reconstruction of coronal mass ejection, The Astrophysical Journal, 2014

(22)Huang Xin*(通讯作者), Zhang Liyun, Wang Huaning, et. al., Improving the performance of solar flare prediction using active longitudes information, Astronomy & Astrophysics, 2013

(23)Huang Xin*(通讯作者), Wang Huaning, Solar flare prediction using highly stressed longitudinal magnetic field parameters , Research in Astronomy and Astrophysics, 2013

( 24 ) Huang Xin*( 通 讯 作 者 ), Wang Huaning, Dai Xinghua, Influences of misprediction costs on solar flare prediction , Science China Physics,Mechanics & Astonomy, 2012

(25)Huang Xin*(通讯作者), Wang Huaning, Li leping, Ensemble prediction model of solar proton events associated with solar flares and coronal mass ejections, Research in Astronomy and Astrophysics, 2012

(26)Li Rong, Wang Huaning, Cui Yanmei, Huang Xin, Solar flare forecasting using learning vector quantity and unsupervised clustering techniques, Science China Physics, Mechanics & Astonomy, 2011

(27)Huang Xin*(通讯作者), Yu Daren, Hu Qinghua, et. al., Short-term solar flare prediction using predictor teams, Solar Physics, 2010

(28)Yu Daren, Huang Xin(通讯作者), Hu Qinghua, et. al., Short-term solar flare prediction using multiresolution predictors, The Astrophysical Journal, 2010

(29)Yu Daren, Huang, Xin(通讯作者), Wang Huaning, et. al., Short-term solar flare level prediction using a Bayesian network approach, The Astrophysical Journal, 2010

(30)Yu Daren, Huang Xin(通讯作者), Wang Huaning, et. al., Short-term solar flare prediction using a sequential supervised learning method, Solar Physics, 2009

(31)黄鑫, 胡清华, 于达仁, 崔延美, 动态邻域模型及其在太阳耀斑预报中的应用, 计算机科 学, 2009.8.1, 36(8A): 20~23

(32)李蓉, 黄鑫, 崔延美, 基于统计学习技术的太阳质子事件预报模型, 科学技术与工程, 2014.10.8, (28): 5~8

(33)李蓉 , 朱杰, 黄鑫, 太阳耀斑预报研究进展, 科学通报, 2014.9.10, (25), 2452~2463

(34)王林萍, 王华宁, 黄鑫, 日冕物质抛射的偏转特性研究, 天文研究与技术, 2015.9.28

专利:

(1)黄鑫, 王华宁, 戴幸华, 基于卷积神经网络模型的太阳耀斑预报方法, 201510727599.3, 发明专利， 授权

(2)黄鑫, 王华宁, 王林萍, 基于机器学习技术预报模型的太阳爆发事件预报方法, 201510729561.X, 发明专利，授权

(二)在研

(1) Kai Feng, Sixuan Liu, Long Xu, and Xin Huang*(通讯作者), Model Compression for Deep learning based Solar Flare Forecasting Model, submitted to ApJL