Chen Gong (宫辰)'s Homepage
Welcome to my homepage! 欢迎访问我的主页!
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Chen Gong
IEEE Senior Member
Address: Room 4030, Dingxin Building, Nanjing University of Science and Technology
E-mail: chen.gong@njust.edu.cn |
Opening Positions (研究岗位)
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1. There are opening research positions in my group at all levels, including Postdoc, research assistant, Ph.D. student, and master student.
Please drop me an E-mail which contains your CV, score sheet, certificates, and representative publications if you are interested.
2. I'm always looking for self-motivated undergraduate students (mainly @NJUST) to join my group for research training!
1. 我的课题组目前有研究岗位若干，包括博士后、研究助理、博士、硕士等。如果您感兴趣，请将您的简历、成绩单、获奖证书、代表作发至我的邮箱。
2. 十分欢迎优秀的本科生（主要面向本校同学）来我的课题组参加科研训练！
Brief Biography (个人简介)
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Chen Gong is a full professor in the School of Computer Science and Engineering, Nanjing University of Science and Technology. He received his B.E. degree from East China University of Science and Technology (ECUST) in 2010, and dual doctoral degree from Shanghai Jiao Tong University (SJTU) and University of Technology Sydney (UTS) in 2016 and 2017, respectively. He has published more than 100 technical papers at prominent journals and conferences such as IEEE T-PAMI, IEEE T-NNLS, IEEE T-IP, IEEE T-CYB, ICML, NeurIPS, CVPR, AAAI, IJCAI, ICDM, etc, and also holds 7 granted inventory patents. He serves as the associate editor for IEEE T-CSVT and NePL, reviewer for more than 30 international journals such as AIJ, JMLR, IEEE T-PAMI, IJCV, IEEE T-NNLS, IEEE T-IP,IEEE T-KDE, and also the SPC/PC member of several top-tier conferences such as ICML, NeurIPS, ICLR, CVPR, ICCV, ECCV, AAAI, IJCAI, ICDM, etc. He received the "Wu Wen-Jun AI Excellent Youth Scholar Award", "Young Elite Scientists Sponsorship Program" of China Association for Science and Technology, The Science Fund for Distinguished Young Scholars of Jiangsu Province, "Hong Kong Scholar", the second prize of Shanghai Natural Science Award, and "Excellent Doctorial Dissertation award" by Shanghai Jiao Tong University (SJTU) and Chinese Association for Artificial Intelligence (CAAI). He was also selected to the Global Top Chinese Young Scholars in AI released by Baidu.
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宫辰现任南京理工大学计算机科学与工程学院教授、博导；获国家级青年人才计划、江苏省杰青。于2010年获得华东理工大学学士学位，并分别于2016、2017年获上海交通大学和悉尼科技大学双博士学位。
已在世界顶级期刊或会议上发表100余篇学术论文，主要包括IEEE T-PAMI, IEEE T-NNLS, IEEE T-IP, IEEE T-CYB, ICML, NeurIPS, CVPR, AAAI, IJCAI, ICDM等，另有7项发明专利获得授权。
目前担任IEEE T-CSVT、NePL副编委，AIJ、JMLR、IEEE T-PAMI、IJCV、IEEE T-NNLS、IEEE T-IP、IEEE T-KDE等30余家国际权威期刊审稿人，以及ICML、NeurIPS、ICLR、CVPR、ICCV、ECCV、AAAI、IJCAI、ICDM等多个国际会议的(S)PC member。曾获吴文俊人工智能优秀青年奖、中国科协“青年人才托举工程”、中国人工智能学会“优秀博士学位论文”奖、上海市自然科学二等奖、上海交通大学“优秀博士学位论文”奖、江苏省“六大人才高峰”、江苏省“双创博士”、江苏省科协“青年人才托举工程”、“香江学者”等，并入选百度发布的全球华人AI青年学者榜单。
News (新闻)
I was invited to be an Area Chair for ASITATS 2024, an SPC member for AAAI 2024, and a PC member for ICLR 2024 我受邀担任AISTATS 2024领域主席、AAAI 2024高级程序委员会成员、ICLR 2024程序委员会成员
Papers on ordinal label noise learning and class-imbalanced semi-supervised learning were accepted by PRICAI 2023 and BMVC 2023, respectively! 关于有序标签噪声学习和类不平衡半监督学习的论文分别被PRICAI 2023和BMVC 2023录用！
Our paper "Boundary-Restricted Metric Learning" was accepted by MLJ! 论文"Boundary-Restricted Metric Learning"被MLJ录用！
Two papers were accepted by ICCV 2023! 两篇论文被ICCV 2023录用！
Our paper "Boosting Graph Contrastive Learning via Adaptive Sampling" was accepted by IEEE T-NNLS! 论文"Boosting Graph Contrastive Learning via Adaptive Sampling"被IEEE T-NNLS录用！
Our paper "Server-Client Collaborative Distillation for Federated Reinforcement Learning" was accepted by ACM T-KDD! 论文"Server-Client Collaborative Distillation for Federated Reinforcement Learning"被ACM T-KDD录用！
Our paper "Towards Few-shot Inductive Link Prediction on Knowledge Graphs: A Relational Anonymous Walk-guided Neural Process Approach" was accepted by ECML-PKDD 2023! 论文"Towards Few-shot Inductive Link Prediction on Knowledge Graphs: A Relational Anonymous Walk-guided Neural Process Approach"被ECML-PKDD 2023录用！
I was invited to be an Area Chair for ACM MM 2023 and ACML 2023 我受邀担任ACM MM 2023及ACML 2023领域主席
Congratulations to Mr. Sheng Wan for successfully defending his PhD thesis! 祝贺万升通过博士学位论文答辩！
I was invited to be a reviewer for NeurIPS 2023 我受邀担任NeurIPS 2023审稿人
I was invited to be an SPC member/Meta-reviewer for ECAI 2023 and ECML-PKDD 2023 我受邀担任ECAI 2023及ECML-PKDD 2023 高级程序委员会成员
Our paper "Robust Generalization against Corruptions via Worst-Case Sharpness Minimization" was accepted by CVPR 2023! 论文"Robust Generalization against Corruptions via Worst-Case Sharpness Minimization"被CVPR 2023录用！
I was invited to be a PC Member for ICDM 2023 我受邀担任ICDM 2023程序委员会成员
Our paper "KRADA: Known-region-aware Domain Alignment for Open-set Domain Adaptation in Semantic Segmentation" was accepted by TMLR! 论文"KRADA: Known-region-aware Domain Alignment for Open-set Domain Adaptation in Semantic Segmentation"被TMLR录用！
Our paper "Harnessing Out-Of-Distribution Examples via Augmenting Content and Style" was accepted by ICLR 2023! 论文"Harnessing Out-Of-Distribution Examples via Augmenting Content and Style"被ICLR 2023录用！
Our paper "Hyperspectral Image Classification With Contrastive Graph Convolutional Network" was accepted by IEEE T-GRS! 论文"Hyperspectral Image Classification With Contrastive Graph Convolutional Network"被IEEE T-GRS录用！
I was invited to be an Associate Editor of IEEE T-CSVT 我受邀担任IEEE T-CSVT编委
I was invited to be a PC Member for ICML 2023 我受邀担任ICML 2023程序委员会成员
Mingyu Li and Zicheng Zhao won the 3rd prize of Jittor AI competition! Congrats! 课题组李明语、赵子成获计图人工智能挑战赛三等奖！祝贺！
1 paper on OOD data detection and 1 paper on contrastive learning were accepted by NeurIPS 2022! 关于分布外数据检测及对比学习的论文被NeurIPS 2022接收！
I was promoted to IEEE Senior Member! 我晋升为IEEE高级会员！
3篇论文"Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning"、"Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model"、"Learning with Group Noise" 被AAAI 2021接收！
Research (研究领域)
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My research interests lie in machine learning, data mining, and learning-based vision problems. Particularly, I'm interested in weakly-supervised learning and its applications, such as semi-supervised learning, positive-unlabeled learning, label noise learning, partial label learning, etc.
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我的研究方向主要为机器学习、数据挖掘及基于学习的计算机视觉问题。特别地，我关注弱监督学习方法及其应用，比如半监督学习、PU学习、标签噪声学习、偏标记学习等。
