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漆远
复旦大学复旦大学浩清教授、博士生导师，人工智能创新与产业研究院院长，原蚂蚁集团首席AI科学家
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详细介绍
漆远
复旦大学浩清教授、博士生导师，人工智能创新与产业研究院院长，原蚂蚁集团首席AI科学家
个人履历：
漆远：男，1974年生，复旦大学浩清教授、博士生导师，人工智能创新与产业研究院院长，原蚂蚁集团副总裁、首席AI科学家。曾赴剑桥大学、哥伦比亚大学、伦敦城市大学、杜克大学、SAMSI、布朗大学等名校和研究院做访问学者。曾任美国普渡大学计算机系和统计系终身教授。1995年毕业于华中科技大学,自动控制专业，获学士学位；1998年毕业于中国科学院自动化研究所模式识别与人工智能专业，获硕士学位；2000年毕业于美国马里兰大学电子与计算机工程，获硕士学位；2004年毕业于美国麻省理工学院媒体实验室，获博士学位。2014年回国担任阿里巴巴副总裁，2015年加入蚂蚁金服公司，创建并统领蚂蚁金服人工智能团队，担任蚂蚁金服公司首席数据科学家。2021年至今，复旦大学人工智能创新与产业研究院院长，教授。长期从事机器学习及计算生物学相关理论研究和应用，是图学习、隐私计算、贝叶斯推理等领域的世界知名学者与业界领导者。他建立并带领团队开发了阿里首个基于参数服务器的超大规模机器学习平台，该平台目前在阿里和蚂蚁的100多个业务场景中广泛使用；他还建立起阿里第一个专业的基于深度学习的语音识别团队，迅速将语音识别能力提升到世界先进水平；在蚂蚁金服，漆远领导的人工智能部门将机器学习应用于各个业务线，其中，为支付宝的证件审核系统开发的基于深度学习的OCR系统，使证件校核时间从一天缩短到一秒。推动并领导了全国高校算力第一的复旦大学CFFF 智算平台的建设。作为评委会主席创办了首个国内综合性科学智能大赛“世界科学智能大赛”。在人工智能和计算生物学顶会和刊物上发表论文100余篇，获得授权专利十余项。曾任JMLR 编辑，ICML、AISTATS 等领域主席。曾获美国科学基金NSF Career 奖、微软牛顿研究突破奖、威康信托基金会研究奖、2021年中国人工智能学会优秀科技工作者。其工作被经济学人、MIT 技术评论报道，被哈佛大学商学院收录为AI 创新落地案例。
论文代表：
A machine learning model that outperforms conventional global subseasonal forecast models [J]. Nat Commun, 2024, 15(1): 6425.
Bandit Samplers for Training Graph Neural Networks, Advances in Neural Information Processing Systems (NeurIPS), 2020.
Continuous-Time Dynamic Graph Learning via Neural Interaction Processes, In Proceedings of CIKM, 2020.
Cost-Effective Incentive Allocation via Structured Counterfactual Inference. In Proceedings of the Thirty-Forth AAAI Conference on Artificial Intelligence (AAAI-20), 2020.
Double Neural Counterfactual Regret Minimization. In Proceedings of The International Conference on Learning Representations (ICLR), 2020.
Efficient Probabilistic Logic Reasoning with Graph Neural Networks，In Proceedings of The International Conference on Learning Representations (ICLR), 2020.
Loan Default Analysis with Multiplex Graph Learning, CIKM, 2020.
Financial Risk Analysis for SMEs with Graph-based Supply Chain Mining in Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20), 2020.
Practical Privacy Preserving POI Recommendation, ACM Transactions on Intelligent Systems and Technology, July 2020, Article No.: 52 https://doi.org/10.1145/3394138.
Uncovering Insurance Fraud Conspiracy with Network Learning， In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retri (SIGIR'19)， 2019.
Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning. NIPS, 2019.
Generative adversarial user model for reinforcement learning based recommendation system. In Proceedings of International Conference on Machine Learning (ICML), 2019.
Graph Representation Learning for Merchant Incentive Optimization in Mobile Payment Marketing，In Proceedings of the 28th ACM International Conference on Information and Knowledge Management（CIKM '19）, 2019.
A Semi-Supervised Graph Attentive Network for Financial Fraud Detection, In Proceedings of IEEE International Conference on Data Mining (ICDM), 2019.
Distributed Deep Forest and its Application to Automatic Detection of Cash-Out Fraud，ACM Transactions on Intelligent Systems and Technology，Vol. 10, No. 5，2019.
Generative Adversarial User Model for Reinforcement Learning Based Recommendation System. in Proceedings of the 36th International Conference on Machine Learning, 2019.
GeniePath: Graph Neural Networks with Adaptive Receptive Paths，in Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), USA, 2019.
Cash-out user detection based on attributed heterogeneous information network with a hierarchical attention mechanism，in Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), USA, 2019 .
Latent Dirichlet Allocation for Internet Price War, in Proceedings of he Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), USA, 2019.
TitAnt: Online Real-time Transaction Fraud Detection in Ant Financial. 45th International Conference on Very Large Data Bases (VLDB-19)， 2019.
Personalized Behavior Prediction with Encoder-to-Decoder Structure, IEEE International Conference on Networking, Architecture and Storage (NAS), 2018
Fintech: AI powers financial services to improve people's lives, Communications of the ACM, October, 2018.
NetDP: An Industrial-Scale Distributed Network Representation Framework for Default Prediction in Ant Credit Pay，In Proceedings of IEEE International Conference on Big Data (Big Data)，2018.
主讲课题：
《从ChatGPT到AI for Science》
《科学智能十大前沿观察》
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