王杰 - 中国科学技术大学

个人简介

王杰，中国科技大学教授，天才青年学院副院长，教育部“脑启发智能感知与认知”重点实验室副主任，MIRA实验室创始人。2005年获中国科技大学电子信息科学与技术学士学位，2011年获佛罗里达州立大学计算科学博士学位。然后，他继续在亚利桑那州立大学和密歇根大学进行博士后研究。在加入中国科学技术大学之前，王博士自2015年起在密歇根大学担任研究助理教授。他对人工智能、机器学习、强化学习、自然语言处理和大规模优化等领域有着广泛的兴趣。他在顶级机器学习和数据挖掘期刊和会议上发表了许多论文，如JMLR、TPAMI、NeurIPS、ICML和SIGKDD。他曾担任ICML、NeurIPS、ICLR等机构的区域主席。他是IEEE高级会员、CCF杰出会员、IEEE TPAMI副主编、神经计算、数据挖掘和知识发现编委，以及国家优秀青年科学基金资助的研究项目的PI。教育背景 2005年在中国科学技术大学电子科学与技术系获得学士学位， 2011年在在美国Florida State University计算科学系获得博士学位。工作经历 2015年在美国Arizona State University和University of Michigan先后任博士后研究员和研究助理教授。 2017年加入中国科学技术大学电子工程与信息科学系，担任特任教授，博士生导师。主持的项目国家自然科学基金企业创新发展联合基金重点项目，面向复杂推理的知识图谱技术，2020/01-2023/12，在研，主持国家自然科学基金优秀青年科学基金，大规模机器学习优化算法，2019/01-2021/12，主持国家自然科学基金重点项目，基于脑成像的视听深度神经网络构建与应用，2019/01-2023/12，在研，主持国家人才计划，2017/08-2020/08，主持

研究领域

图机器学习，图学习基础大模型研究；基于知识图谱的认知大模型研究；基于图机器学习的量子化学算法研究、PDE求解器研究学习优化，运筹优化求解器开发；芯片电子设计自动化强化学习，深度强化学习算法研究；自动驾驶智能决策规划

近期论文

查看导师新发文章（温馨提示：请注意重名现象，建议点开原文通过作者单位确认）

Towards General Algorithm Discovery for Combinatorial Optimization: Learning Symbolic Branching Policy from Bipartite Graph July 2024 ICML 2024 Yufei Kuang, Jie Wang, Yuyan Zhou, Xijun Li, Fangzhou Zhu, Jianye Hao, Feng Wu Reinforcement Learning within Tree Search for Fast Macro Placement July 2024 ICML 2024 Zijie Geng, Jie Wang, Ziyan Liu, Siyuan Xu, Zhentao Tang, Mingxuan Yuan, Jianye Hao, Yongdong Zhang, Feng Wu Coarse-to-Fine Highlighting: Reducing Knowledge Hallucination in Large Language Models July 2024 ICML 2024 Qitan Lv, Jie Wang, Hanzhu Chen, Bin Li, Yongdong Zhang, Feng Wu Accelerating PDE Data Generation via Differential Operator Action in Solution Space July 2024 ICML 2024 Huanshuo Dong, Hong Wang, Haoyang Liu, Jian Luo, Jie Wang A Hierarchical Adaptive Multi-Task Reinforcement Learning Framework for Multiplier Circuit Design July 2024 ICML 2024 Zhihai Wang, Jie Wang, Dongsheng Zuo, Yunjie Ji, Xilin Xia, Yuzhe Ma, Jianye Hao, Mingxuan Yuan, Yongdong Zhang, Feng Wu A Circuit Domain Generalization Framework for Efficient Logic Synthesis in Chip Design July 2024 ICML 2024 Zhihai Wang, Lei Chen, Jie Wang, Yinqi Bai, Xing Li, Xijun Li, Mingxuan Yuan, Jianye Hao, Yongdong Zhang, Feng Wu Learning to Cut via Hierarchical Sequence/Set Model for Efficient Mixed-Integer Programming July 2024 TPAMI Jie Wang, Zhihai Wang, Xijun Li, Yufei Kuang, Zhihao Shi, Fangzhou Zhu, Mingxuan Yuan, Jia Zeng, Yongdong Zhang, Feng Wu SAC-KG: Exploiting Large Language Models as Skilled Automatic Constructors for Domain Knowledge Graph May 2024 ACL 2024 Hanzhu Chen, Xu Shen, Qitan Lv, Jie Wang, Xiaoqi Ni, Jieping Ye Rethinking Branching on Exact Combinatorial OPtimization Solver: The First Deep Symbolic Discovery Framework May 2024 ICLR 2024 Yufei Kuang, Jie Wang, Haoyang Liu, Fangzhou Zhu, Xijun Li, Jia Zeng, Jianye Hao, Bin Li, Feng Wu Accelerating Data Generation for Neural Operators via Krylov Subspace Recycling (Spotlight) May 2024 ICLR 2024 Hong Wang, Zhongkai Hao, Jie Wang, Zijie Geng, Zhen Wang, Bin Li, Feng Wu Learning to Stop Cut Generation for Efficient Mixed-Integer Linear Programming February 2024 AAAI 2024 Haotian Ling, Zhihai Wang, Jie Wang State Sequences Prediction via Fourier Transform for Representation Learning (Spotlight) December 2023 NeurIPS 2023 Mingxuan Ye, Yufei Kuang, Jie Wang, Rui Yang, Wengang Zhou, Houqiang Li, Feng Wu Learning Rule-Induced Subgraph Representations for Inductive Relation Prediction December 2023 NeurIPS 2023 Tianyu Liu, Qitan Lv, Jie Wang, Shuling Yang, Hanzhu Chen A Deep Instance Generative Framework for MILP Solvers Under Limited Data Availability (Spotlight) December 2023 NeurIPS 2023 Zijie Geng, Xijun Li, Jie Wang, Xiao Li, Yongdong Zhang, Feng Wu LMC: Fast Training of GNNs via Subgraph Sampling with Provable Convergence (Spotlight) May 2023 ICLR 2023 Zhihao Shi, Xize Liang, Jie Wang Learning Cut Selection for Mixed-Integer Linear Programming via Hierarchical Sequence Model May 2023 ICLR 2023 Zhihai Wang, Xijun Li, Jie Wang, Yufei Kuang, Mingxuan Yuan, Jia Zeng, Yongdong Zhang, Feng Wu Learning Cut Selection for Mixed-Integer Linear Programming via Hierarchical Sequence Model De Novo Molecular Generation via Connection-aware Motif Mining May 2023 ICLR 2023 Zijie Geng, Shufang Xie, Yingce Xia, Lijun Wu, Tao Qin, Jie Wang, Yongdong Zhang, Feng Wu, Tie-Yan Liu Efficient Exploration in Resource-Restricted Reinforcement Learning February 2023 AAAI 2023 Zhihai Wang, Taoxing Pan, Qi Zhou, Jie Wang Modeling Diverse Chemical Reactions for Single-step Retrosynthesis via Discrete Latent Variables August 2022 CIKM 2022 Huarui He, Jie Wang, Yunfei Liu, Feng Wu Learning Task-relevant Representations for Generalization via Characteristic Functions of Reward Sequence Distributions May 2022 KDD 2022 Rui Yang, Jie Wang, Zijie Geng, Mingxuan Ye, Shuiwang Ji, Bin Li, Feng Wu Compressing Deep Graph Neural Networks via Adversarial Knowledge Distillation May 2022 KDD 2022 Huarui He, Jie Wang, Zhanqiu Zhang, Feng Wu Duality-Induced Regularizer for Semantic Matching Knowledge Graph Embeddings March 2022 TPAMI Jie Wang, Zhanqiu Zhang, Zhihao Shi, Jianyu Cai, Shuiwang Ji, Feng Wu Rethinking Graph Convolutional Networks in Knowledge Graph Completion February 2022 WWW 2022 Zhanqiu Zhang, Jie Wang, Jieping Ye, Feng Wu Sample-Efficient Reinforcement Learning via Conservative Model-Based Actor-Critic February 2022 AAAI 2022 Zhihai Wang, Jie Wang, Qi Zhou, Bin Li, Houqiang Li Learning Robust Policy against Disturbance in Transition Dynamics via State-Conservative Policy Optimization February 2022 AAAI 2022 Yufei Kuang, Miao Lu, Jie Wang, Qi Zhou, Bin Li, Houqiang Li ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs December 2021 NeurIPS 2021 Zhanqiu Zhang, Jie Wang, Jiajun Chen, Shuiwang Ji, Feng Wu Deep Cognitive Reasoning Network for Multi-hop Question Answering over Knowledge Graphs July 2021 Findings of ACL 2021 Jianyu Cai, Zhanqiu Zhang, Feng Wu, Jie Wang On Explainability of Graph Neural Networks via Subgraph Explorations July 2021 ICML 2021 Hao Yuan, Haiyang Yu, Jie Wang, Kang Li, and Shuiwang Ji Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking March 2021 CVPR 2021 Ning Wang, Wengang Zhou, Jie Wang, Houqiang Li Learning to Optimize Industry-Scale Dynamic Pickup and Delivery Problems February 2021 ICDE 2021 Xijun Li, Weilin Luo, Mingxuan Yuan, Jun Wang, Jiawen Lu, Jie Wang, Jinhu Lv, Jia Zen Topology-Aware Correlations Between Relations for Inductive Link Prediction in Knowledge Graphs February 2021 AAAI 2021 Jiajun Chen, Huarui He, Feng Wu, Jie Wang Interpreting the Latent Space of GANs via Measuring Decoupling February 2021 IEEE TAI Ziqiang Li, Rentuo Tao, Jie Wang, Fu Li, Hongjing Niu, Mingdao Yue, Bin Li Promoting Stochasticity for Expressive Policies via a Simple and Efficient Regularization Method December 2020 NeurIPS 2020 Qi Zhou, Yufei Kuang, Zherui Qiu, Houqiang Li, Jie Wang Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion December 2020 NeurIPS 2020 Zhanqiu Zhang, Jianyu Cai, Jie Wang Interpreting Image Classifiers by Generating Discrete Masks October 2020 TPAMI Hao Yuan, Lei Cai, Xia Hu, Jie Wang, Shuiwang Ji Self-Adaptive Embedding for Few-shot Classification by Hierarchical Attention July 2020 ICME 2020 Xueliang Wang, Feng Wu, Jie Wang Deep Model-Based Reinforcement Learning via Estimated Uncertainty and Conservative Policy Optimization February 2020 AAAI 2020 Qi Zhou, Houqiang Li, Jie Wang D-SPIDER-SFO: A Decentralized Optimization Algorithm with Faster Convergence Rate for Nonconvex Problem February 2020 AAAI 2020 Taoxing Pan, Jun Liu, Jie Wang Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction February 2020 AAAI 2020 Zhanqiu Zhang, Jianyu Cai, Yongdong Zhang, Jie Wang Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction For the sake of readability, we only report here charts for FB15k-237, WN18RR, and YAGO3-10, ..., and the best performing models for each family, i.e., ..., HAKE for the geometric ones, ...

学术兼职

Journal Editorship Associate Editor, IEEE TPAMI Associate Editor, Neurocomputing Editorial board member, Data Mining and Knowledge Discovery Conference & Workshop Organizing Activities Organizer, AAAI Workshop on Artificial Intelligence for Operations Research, 2024 Tutorial Chair, ICME 2021 (Senior) Program Committee Member (Selected) Area Chair, ICLR 2025 Area Chair, ICML 2024 Area Chair, NeurIPS 2023-2024 Area Chair, SIGKDD ADS Track 2023 NeurIPS, 2015-2022 ICML, 2018-2019, 2021-2023 ICLR, 2018-2020, 2022-2024 SIGKDD, 2018-2023 AAAI, 2018-2020, 2022-2024