2021年1月，出生于1995年4月的冯磊，于被重庆大学计算机学院，直接作为弘深青年学者人才引进，并聘任为博导、教授，其主要研究方向为机器学习、数据挖掘、人工智能。 冯磊入职时仅25岁，这是重大计算机学院目前年龄最小的引进人才，也是该学院有史以来首次直接给应届博士毕业生正高/博导岗位。http://www.cs.cqu.edu.cn/info/1325/5242.htm入职半年，冯磊撰写的论文《Pointwise Binary Classification with Pairwise Confidence Comparisons》在第38届国际机器学习会议（The 38th International Conference on Machine Learning）（CCF A类）上发表。这是机器学习领域公认的顶级国际学术会议，在学术界享有极高的声誉，这也是重庆大学计算机学院首次以第一单位在该会议上发表学术论文，实现了零的突破。冯磊，重庆大学弘深青年学者引进人才（教授、博导），兼任日本理化学研究所先进智能研究中心（RIKEN Center for Advanced Intelligence Project）Visiting Scientist。博士毕业于新加坡南洋理工大学（Nanyang Technological University, Singapore），在提前毕业的情况下，获得南洋理工大学计算机科学与工程学院杰出博士学位论文奖第二名（NTU SCSE Outstanding PhD Thesis Award Runner-Up）。中国计算机学会（CCF）会员，中国人工智能学会（CAAI）会员，国际人工智能促进学会（AAAI）会员，美国计算机学会（ACM）会员，中国人工智能学会机器学习专委会通讯委员。担任IJCAI 2021与AAAI 2022高级程序委员会委员（senior program committee member），ICML 2021 专家审稿人（expert reviewer），以及其他国际顶级（CCF A类）会议（包括NeurIPS、KDD、CVPR、ICCV、AAAI）的程序委员会委员/审稿人，并受邀担任多个国际顶级期刊（包括JMLR、IEEE-TPAMI、IEEE-TIP、IEEE-TNNLS、MLJ）审稿人。
主要研究方向为机器学习、数据挖掘、人工智能。已在International Conference on Machine Learning (ICML)，Annual Conference on Neural Information Processing Systems (NeurIPS), ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)，IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), International Conference on Computer Vision (ICCV), AAAI Conference on Artificial Intelligence (AAAI), International Joint Conference on Artificial Intelligence (IJCAI)等国际顶级（CCF A类）会议与中科院一区期刊上发表论文近二十篇。
冯磊还入选了2021福布斯中国30 Under 30 科学和医疗健康领域榜单。
重庆大学是教育部直属的全国重点大学，国家“211工程”和“985工程”重点建设的高水平研究型综合性大学，国家“世界一流大学建设高校（A类）”。
学校创办于1929年，在20世纪40年代就发展为拥有文、理、工、商、法、医6个学院的国立综合性大学。经过1952年全国院系调整，成为国家高教部（高教部1958年并入教育部）直属的、以工科为主的多科性大学。1960年被确定为全国重点大学。改革开放以来，学校大力发展人文社科类学科专业，促进了多学科协调发展，逐步发展为综合性研究型大学。1998年，学校成为国家“211工程”重点建设高校。2000年5月，原重庆大学、重庆建筑大学、重庆建筑高等专科学校三校合并组建成新的重庆大学。2001年，学校成为“985工程”重点建设高校。2004年，学校被确定为中管高校。2017年9月，学校入选国家“世界一流大学建设高校（A类）”。
学校学科门类齐全，涵盖理、工、经、管、法、文、史、哲、医、教育、艺术11个学科门类。设7个学部35个学院，以及附属肿瘤医院、附属三峡医院、附属中心医院。教职工5300余人，在校学生47000余人，其中研究生20000余人，本科生26000余人，来华留学生1700余人。校园占地面积5200余亩，有A校区、B校区、C校区和虎溪校区。
冯磊学术成果
[20] Tao Liang, Guosheng Lin, Lei Feng, Yan Zhang, Fengmao Lv. Attention is not Enough: Mitigating the Distribution Discrepancy in Asynchronous Multimodal Sequence Fusion. Proceedings of the International Conference on Computer Vision (ICCV'21), to appear, 2021. (CCF A)[19] Lei Feng, Senlin Shu, Yuzhou Cao, Lue Tao, Hongxin Wei, Tao Xiang, Bo An, Gang Niu. Multiple-Instance Learning from Similar and Dissimilar Bags. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'21), to appear, 2021. (CCF A)[18] Lei Feng, Senlin Shu, Nan Lu, Bo Han, Xin Geng, Gang Niu, Bo An, Masashi Sugiyama. Pointwise Binary Classification with Pairwise Confidence Comparisons. Proceedings of the 38th International Conference on Machine Learning (ICML'21), to appear, 2021. (CCF A)[17] Yuzhou Cao, Lei Feng, Xitian Xu, Bo An, Gang Niu, Masashi Sugiyama. Learning from Similarity-Confidence Data. Proceedings of the 38th International Conference on Machine Learning (ICML'21), to appear, 2021. (CCF A)[16] Dengbao Wang, Lei Feng, Minling Zhang. Learning from Complementary Labels via Partial-Output Consistency Regularization. Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI'21), to appear, 2021. (CCF A)[15] Zhuoyi Lin, Lei Feng*, Rui Yin, Chi Xu, Chee Keong Kwoh. GLIMG: Global and Local Item Graphs for Top-N Recommender Systems. Information Sciences (INS), to appear, 2021. (IF=6.795, 中科院一区, *通讯作者)[14] Lei Feng, Jiaqi Lv, Bo Han, Miao Xu, Gang Niu, Xin Geng, Bo An, Masashi Sugiyama. Provably consistent Partial-Label Learning. Proceedings of the 34th Annual Conference on Neural Information Processing Systems (NeurIPS'20), to appear, 2020. (CCF A)[13] Lei Feng*†, Takuo Kaneko†, Bo Han, Gang Niu, Bo An, Masashi Sugiyama. Learning with Multiple Complementary Labels. Proceedings of the 37th International Conference on Machine Learning (ICML'20), pp.3072-3081, 2020. (CCF A, *通讯作者, †共同一作)[12] Jiaqi Lv, Miao Xu, Lei Feng, Gang Niu, Xin Geng, Masashi Sugiyama. Progressive Identification of True Labels for Partial-Label Learning. Proceedings of the 37th International Conference on Machine Learning (ICML'20), pp.6500-6510, 2020. (CCF A)[11] Jun Huang*, Linchuan Xu, Jing Wang, Lei Feng*, Kenji Yamanishi. Discovering Latent Class Labels for Multi-Label Learning. Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI'20), pp.3058-3064, 2020. (CCF A, *通讯作者)[10] Lei Feng, Senlin Shu, Zhuoyi Lin, Fengmao Lv, Li Li, Bo An. Can Cross Entropy Loss Be Robust to Label Noise? Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI'20), pp.2206-2212, 2020. (CCF A)[9] Hongxin Wei, Lei Feng*, Xiangyu Chen, Bo An. Combating Noisy Labels by Agreement: A Joint Training Method with Co-Regularization. Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR'20), pp.13726-13735, 2020. (CCF A, *通讯作者)[8] Lei Feng, Jun Huang, Senlin Shu, Bo An. Regularized Matrix Factorization for Multi-Label Learning with Missing Labels. IEEE Transactions on Cybernetics (IEEE-TCYB), DOI: 10.1109/TCYB.2020.3016897. (IF=11.079, 中科院一区)[7] Yan Yan, Shining Li, Lei Feng*. Partial Multi-Label Learning with Mutual Teaching. Knowledge-Based Systems (KBS), DOI: 10.1016/j.knosys.2020.106624. (IF=5.921, 中科院一区, *通讯作者)[6] Lei Feng, Hongxin Wei, Qingyu Guo, Zhuoyi Lin, Bo An. Embedding-Augmented Generalized Matrix Factorization for Recommendation with Implicit Feedback. IEEE Intelligent Systems (IEEE-IS), DOI: 10.1109/MIS.2020.3036136. (IF=3.21, 中科院三区)[5] Lei Feng, Bo An. Partial Label Learning with Self-Guided Retraining. Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI'19), pp.3542-3549, 2019. (CCF A)[4] Lei Feng, Bo An, Shuo He. Collaboration based Multi-Label Learning. Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI'19), pp.3550-3557, 2019. (CCF A)[3] Lei Feng, Bo An. Partial Label Learning by Semantic Difference Maximization. Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI'19), pp.2294-2300, 2019. (CCF A)[2] Shuo He, Lei Feng, Li Li. Estimating Latent Relative Labeling Importances for Multi-Label Learning. Proceedings of the 2018 IEEE Conference on Data Mining (ICDM'18), pp.1013-1018, 2018. (CCF B)[1] Lei Feng, Bo An. Leveraging Latent Label Distributions for Partial Label Learning. Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), pp.2107-2113, 2018. (CCF A)本文来源：综合冯磊自个人主页、重庆大学官网、重庆大学计算机学院，版权属于原作者，仅用于学术分享关注我们并回复001到004，即可直接领取科研资源，绝对干货（例如origin使用教程，Endnote软件及教程，科研PPT模版，18款实用科研）回复:SCI可领取SCI论文写作教程；回复：文献神器，获取文献下载神器等等。更多科研资讯、干货分享欢迎关注。
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