许铮铧

职称：教授，博士生导师

教育背景：

2013-2017，英国牛津大学计算机科学系，博士

2010-2012，澳大利亚墨尔本大学计算机专业，硕士

2005-2009,北京邮电大学信息工程专业，本科

工作履历：

2018-至今，河北工业大学生物医学工程系，教授、博导

2018-至今，英国牛津大学计算机系，客座研究员、博导

2017-2018，英国牛津大学计算机系，副研究员、博导

2014-2015，英国牛津大学计算机系，助理研究员

2012-2013，澳大利亚皇家墨尔本理工大学，电气和电子工程学院，分析研究员

学术兼职：

    计算机国际顶会：

       1.国际人工智能联合会议（IJCAI）程序委员会委员、高级委员、分会主席（2019-23）

       2.AAAI国际人工智能会议（AAAI）程序委员会委员、高级委员、分会主席（2020-23）

       3.欧洲人工智能会议（ECAI）程序委员会委员（2020）

       4.国际医学图像计算和计算机辅助干预会议（MICCAI）程序委员会委员（2020）

学会任职：

    1.中国计算机学会计算机视觉专委会（CCF-CV）委员

    2.中国计算机学会青年计算机科技论坛天津分论坛（CCF YOCSEF天津）学术秘书

    3.中国生物医学工程学会医学影像工程与技术分会青年委员

    4.医学图像计算青年研讨会（MICS）委员

    5.河北数理医学学会健康大数据专委会常务委员

       6. 河北省生物医学工程学会医工融合成果转化专委会常务委员

研究领域与承担项目

      研究领域：

      智能医学影像与健康大数据分析、深度强化学习、神经科学与深度学习、推荐系统。

      基金和项目：

      （1）国家自然科学基金面上项目，2023-2026，主持

      （2）国家自然科学基金青年项目，2020-2022，主持

      （3）河北省引进海外高层次人才“百人计划”资助项目，2020-2022，主持

      （4）河北省自然科学基金优秀青年科学基金项目，2021-2023，主持

      （5）天津市自然科学基金青年项目，2019-2022，主持

      （6）海南省重点研发计划社会发展方向项目，2022-2024，第一主研

      （7）河北工业大学元光学者自主项目，2018-2023，主持

奖励与荣誉：

      （1）河北省引进海外高层次人才“百人计划”省级特聘专家

      （2）谷歌全球博士奖研金提名

（3）英国牛津大学Jason Hu博士奖学金

（4）Australasian Database Conference Runner-up for Best Paper Award（最佳论文奖）

学术成果：

概述：许铮铧教授聚焦人工智能、先进制造、生命健康和新一代信息技术等国家重点发展和支持的前沿探索领域，开展人工智能、深度学习、医学影像智能诊疗系统等方面研究。近五年主持国家自然科学基金项目2项，主持河北省“百人计划”、河北省“优青”等省部级高水平项目3项。近五年发表高水平论文30余篇，其中以第一作者或通讯作者发表CCF A/B类国际顶级会议论文和SCI一/二区高水平期刊论文20余篇，单篇论文最高引用333余次，另有数篇作为通讯作者的代表性工作正在Nature Neuroscience（第三轮返修）等具有高影响力的国际顶级期刊返修审稿中。申请专利8项，授权2项。

    十篇代表作：

[1] Yuhang Song*, Beren Millidge, Tommaso Salvatori, Thomas Lukasiewicz*, Zhenghua Xu* (通讯作者), Rafal Bogacz*. Inferring Neural Activity Before Plasticity: A Foundation for Learning Beyond Backpropagation. Nature Neuroscience第三轮返修审稿中. (Nature子刊, IF: 28.771)

[2] Shuo Zhang, Jiaojiao Zhang, Biao Tian, Thomas Lukasiewicz, Zhenghua Xu* (通讯作者). Multi-Modal Contrastive Mutual Learning and Pseudo-Label Re-Learning for Semi-Supervised Medical Image Segmentation. Medical Image Analysis, 2023. (SCI一区, IF: 13.828)

[3] Jianfeng Wang, Thomas Lukasiewicz, Xiaolin Hu, Jianfei Cai, Zhenghua Xu* (通讯作者). RSG: A Simple Yet Effective Module for Learning Imbalanced Datasets. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021. (CCF-A类人工智能国际顶级会议)

[4] Yixin Su, Rui Zhang*, Sarah Erfani, Zhenghua Xu* (通讯作者). Detecting Beneficial Feature Interactions for Recommender Systems via Graph Neural Networks. In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI), 2021. (CCF-A类人工智能国际顶级会议)

[5] Yuhang Song, Thomas Lukasiewicz, Zhenghua Xu* (通讯作者), Rafal Bogacz. Can the Brain Do Backpropagation? -- Exact Implementation of Backpropagation in Predictive Coding Networks. In Proceedings of the 34th Conference on Neural Information Processing Systems (NeurIPS), 2020. (CCF-A类人工智能国际顶级会议)

[6] Yuhang Song, Andrzej Wojcicki, Thomas Lukasiewicz, Jianyi Wang, Abi Aryan, Zhenghua Xu* (通讯作者), Mai Xu, Zihan Ding and Lianlong Wu. Arena: A General Evaluation Platform and Building Toolkit for Multi−Agent Intelligence. In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI), 2020. (CCF-A类人工智能国际顶级会议)

[7] Yuhang Song, Jianyi Wang, Thomas Lukasiewicz, Zhenghua Xu* (通讯作者), Shangtong Zhang, Andrzej Wojcicki and Mai Xu. Mega−Reward: Achieving Human−Level Play without Extrinsic Rewards. In the proceeding of AAAI Conference on Artificial Intelligence (AAAI), 2020. (CCF-A类人工智能国际顶级会议)

[8] Yuhang Song, Jianyi Wang, Thomas Lukasiewicz, Zhenghua Xu* (通讯作者), Mai Xu. Diversity-Driven Extensible Hierarchical Reinforcement Learning. In the proceeding of AAAI Conference on Artificial Intelligence (AAAI), 2019. (CCF-A类人工智能国际顶级会议)

[9] Zhenghua Xu* (第一作者兼通讯作者), Cheng Chen, Thomas Lukasiewicz, Yishu Miao and Xiangwu Meng. Tag-Aware Personalized Recommendation Using a Hybrid Deep Model. In the proceeding of International Joint Conference on Artificial Intelligence (IJCAI), 2017. (CCF-A类人工智能国际顶级会议)

[10] Andy Yuan Xue, Rui Zhang, Yu Zheng, Xing Xie, Jin Huang and Zhenghua Xu. Destination Prediction by Sub-trajectory Synthesis and Privacy Protection against Such Prediction. In the proceeding of 29th IEEE International Conference on Data Engineering (ICDE), 2013. (CCF-A类数据挖掘国际顶级会议, Google Scholar引用333次)

    其他CCF- A/B类国际顶级会议论文：

[11] Tommaso Salvatori‚ Yuhang Song*‚ Zhenghua Xu‚ Thomas Lukasiewicz and Rafal Bogacz. Reverse Differentiation via Predictive Coding. In Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI), 2022. (CCF-A类人工智能国际顶级会议)

[12] Tommaso Salvatori#, Yuhang Song#*, Yujian Hong, Simon Frieder, Lei Sha, Zhenghua Xu, Rafal Bogacz and Thomas Lukasiewicz. Associative Memories via Predictive Coding. In Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS), 2021. (CCF-A类人工智能国际顶级会议)

[13] Gang Xu, Shengxin Wang, Zhenghua Xu* (通讯作者), Thomas Lukasiewicz. Adaptive-Masking Policy with Deep Reinforcement Learning for Self-Supervised Medical Image Segmentation. Accepted to publish in Proceedings of the IEEE International Conference on Multimedia & Expo (ICME), 2023. (CCF-B类机器视觉国际顶级会议)

[14] Ruizhi Wang, Xiangtao Wang, Zhenghua Xu* (通讯作者), Wenting Xu, Junyang Chen, Thomas Lukasiewicz. MvCo-DoT: Multi-View Contrastive Domain Transfer Network for Medical Report Generation. Accepted to publish in Proceedings of the 48th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2023. (CCF-B类信号处理国际顶级会议)

[15] Xiangtao Wang, Ruizhi Wang, Biao Tian, Jiaojiao Zhang, Shuo Zhang, Junyang Chen, Thomas Lukasiewicz, Zhenghua Xu* (通讯作者). MPS-AMS: Masked Patches Selection and Adaptive Masking Strategy Based Self-Supervised Medical Image Segmentation. Accepted to publish in Proceedings of the 48th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2023. (CCF-B类信号处理国际顶级会议)

[16] Hexiang Zhang, Zhenghua Xu* (通讯作者), Dan Yao, Shuo Zhang, Junyang Chen, Thomas Lukasiewicz. Multi-Head Feature Pyramid Networks for Breast Mass Detection. Accepted to publish in Proceedings of the 48th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2023. (CCF-B类信号处理国际顶级会议)

[17] Zhenghua Xu*# (第一作者兼通讯作者), Di Yuan#, Thomas Lukasiewicz, Cheng Chen, Yishu Miao and Guizhi Xu*. Hybrid Deep-Semantic Matrix Factorization for Tag-Aware Personalized Recommendation. In Proceedings of the 45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2020. (CCF-B类信号处理国际顶级会议)

[18] Zhenghua Xu (第一作者), Chang Qi and Guizhi Xu*. Semi-Supervised Attention -Guided CycleGAN for Data Augmentation on Medical Images. In the proceeding of IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM), 2019. (CCF-B类生物信息国际顶级会议)

[19] Lei Wang, Bo Wang, Zhenghua Xu* (通讯作者). Tumor Segmentation Based on Deeply Supervised Multi-Scale U-Net. In the proceeding of IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM), 2019. (CCF-B类生物信息国际顶级会议)

[20] Bo Li, Zehua Cheng, Zhenghua Xu* (通讯作者), Wei Ye, Thomas Lukasiewicz, Shikun Zhang. Long Text Analysis Using Sliced Recurrent Neural Networks with Breaking Point Information Enrichment. In the proceeding of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019. (CCF-B类信号处理国际顶级会议)

[21] Cheng Chen, Thomas Lukasiewicz, Xiangwu Meng and Zhenghua Xu. Location-Aware News Recommendation Using Deep Localized Semantic Analysis. In the proceeding of 22nd International Conference on Database Systems for Advanced Applications (DASFAA), 2017. (CCF-B类数据库国际顶级会议)

[22] Zhenghua Xu (第一作者), Cheng Chen, Thomas Lukasiewicz, Yishu Miao and Xiangwu Meng. Tag-Aware Personalized Recommendation Using a Deep-Semantic Similarity Model with Negative Sampling. In the proceeding of 25th ACM International Conference on Information and Knowledge Management (CIKM), 2016. (CCF-B类数据处理国际顶级会议)

[23] Zhenghua Xu (第一作者), Rui Zhang, Ramamohanarao Kotagiri and Udaya Parampalli. An Adaptive Online Algorithm for Time Series Segmentation with Error Bound Guarantee. In the proceeding of 15th International Conference on Extending Database Technology (EDBT), 2012. (CCF-B类数据库国际顶级会议)

[24] Jianzhong Qi, Zhenghua Xu, Yuan Xue and ZeyiWen. A Branch and Bound Method for Min-dist Location Selection Queries. In the proceeding of 23rd Australasian Database Conference (ADC), 2012. (Runner-up Best Paper Award，最佳论文奖)

   其他SCI一/二区期刊论文:

[25] Junyang Chen, Zhiguo Gong*, Wei Wang, Cong Wang*, Zhenghua Xu, Jianming Lv, Xueliang Li, Kaishun Wu, Weiwen Liu. Adversarial Caching Training: Unsupervised Inductive Network Representation Learning on Large-Scale Graphs. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021. (SCI一区, IF: 10.451 )

[26] Zhenghua Xu (第一作者), Shijie Liu, Di Yuan, Lei Wang, Junyang Chen, Thomas Lukasiewicz, Zhigang Fu, Rui Zhang. ω-Net: Dual Supervised Medical Image Segmentation with Multi-Dimensional Self-Attention and Diversely-Connected Multi-Scale Convolution. Neurocomputing, 2022. (SCI 二区Top, IF: 5.779)

[27] Haozhe Lin, Yushun Fan, Jia Zhang, Bing Bai, Zhenghua Xu, Thomas Lukasiewicz. Toward Knowledge as a Service (KaaS): Predicting Popularity of Knowledge Services Leveraging Graph Neural Networks. In IEEE Transactions on Service Computing (TSC), 2022. (CCF A类国际顶级期刊, SCI二区Top, IF: 8.216)

[28] Di Yuan, Yunxin Liu*, Zhenghua Xu* (通讯作者), Yuefu Zhan, Junyang Chen, Thomas Lukasiewicz. Painless and Accurate Medical Image Analysis Using Deep Reinforcement Learning with Task-Oriented Homogenized Automatic Pre-Processing. Computers in Biology and Medicine, 2023. (SCI 二区, IF: 6.698)

[29] Zhenghua Xu* (第一作者兼通讯作者), Tianrun Li, Yunxin Liu, Yuefu Zhan*, Junyang Chen, Thomas Lukasiewicz. PAC-Net: Multi-Pathway FPN with Position Attention Guided Connections and Vertex Distance IoU for 3D Medical Image Detection. Frontiers in Bioengineering and Biotechnology, 2023. (SCI 二区, IF: 6.064)

[30] Junyang Chen, Mengzhu Wang, Haodi Zhang, Zhenghua Xu, Xueliang Li, Zhiguo Gong, Kaishun Wu, Victor C. M. Leung. IRLM: Inductive Representation Learning Model for Personalized POI Recommenda tion. IEEE Transactions on Computational Social System, 2022. (SCI二区, IF: 4.747)

五、专利申请与授权情况

[1] 针对分割任务的医学影像特征增强方法，许铮铧、齐畅、徐桂芝，已授权，专利号：ZL202011356102.9，授权公告日：2022.07.01。

[2] 基于全视野数字切片的病人级别肿瘤智能诊断方法，赵丹、徐桂芝、许铮铧，已授权，专利号：ZL202011137309.7，授权公告日：2022.08.30。

[3] 一种带有尺度增强和注意力融合的医疗图像病灶检测算法，许铮铧、张旭东，已申请，申请号：202210078271.3，申请日期：2022.01.24。

[4] 居民短期电力负荷自动化预测方法，许铮铧、余周涛，已申请，申请号：202210077357.4，申请日期：2022.01.24。

[5] 金字塔和损失函数增强的电力系统绝缘及缺陷检测网络，许铮铧、王博，已申请，申请号：202210036799.4，申请日期：2022.01.13。

[6] 基于多模态自监督的医学影像分割方法，许铮铧、张娇娇，已申请，申请号：202211374925.3，申请日期：2022.11.04。

[7] 针对医学影像分割任务的生成式模态补足方法，许铮铧、姚丹，已申请，申请号：202211533192.3，申请日期：2022.12.01。

[8] 基于因果奖励的多任务自监督强化学习，许铮铧、周杰，已申请，申请号：202310048598.0，申请日期：2023.01.31。