石荣晔 - 北京航空航天大学

个人简介

石荣晔，博士，现任北京航空航天大学人工智能研究院准聘副教授，副教授职称，曾于2019年从美国卡耐基梅隆大学电子与计算机工程专业获得哲学博士学位，师从Manuela M. Veloso (美国工程院院士，IEEE/ACM/AAAI/AAAS Fellow)和Peter Steenkiste (IEEE Fellow)。2019~2020在美国哥伦比亚大学担任Postdoctoral Research Scientist，师从Xuan Di和Qiang Du (AAAS Fellow)。2020~2023在华为技术有限公司担任Principal Engineer。长期从事领域知识内嵌人工智能算法方面的研究，在物理信息神经网络、多智能体系统、强化学习及其在智慧城市领域的应用等方面开展了许多工作，以“领域知识+人工智能”为创新导向，从数据效率、可解释性、可拓展性等维度解决人工智能在智慧城市场景中融合深度不足、应用广度受限的问题。已发表SCI/EI学术论文20余篇，多篇以第一作者或通讯作者身份发表在AAAI、ECML-PKDD、IEEE TITS、Information Processing & Management、IEEE TASE等人工智能和智慧城市领域的顶级会议/期刊上，曾获Amazon AWS Machine Learning Research Award、GLSVLSI 2017会议最佳论文奖、NSF Travel Award、John Bardeen Research Award、ICML (Top 10%) Outstanding Reviewers等奖项，相关工作被福布斯(Forbes)、ScienceDaily、机器之心等主流科技资讯平台报道，曾被华为任命为交通系统建模与算法领域的“助理首席专家”。曾担任ECML-PKDD 2021、IEEE ICHMS 2020/2021等会议程序委员会委员，ITSC 2018 Regular Session共同主席，担任IEEE Transactions on Intelligent Transportation Systems、IEEE Transactions on Mobile Computing、IEEE Internet of Things (IoT) Journal、ACM Transactions on Knowledge Discovery from Data、IEEE Transactions on Automation Science and Engineering等国际期刊审稿人，以及ICML、NeurIPS、KDD、ICHMS等国际会议审稿人。 I am very fortunate to receive the following selected awards and honors: Outstanding Reviewers (Top 10%) for ICML 2022 Elected to Huawei "Assistant Chief Experts" of Central Research Institute in 2022 Received Shenzhen"Peacock Plan" – Shenzhen Overseas High-Caliber Personnel (Level C) Elected to Huawei Talented Youth Program in 2020 2019 Amazon AWS Machine Learning Research Awards NSF Student Travel Award (BDCAT 2017) Best Paper Award (GLSVLSI 2017) 2015 SONIC John Bardeen Student Research Award Meritorious Winner of 2011 Mathematical Contest in Modeling Second Prize of 26th National Undergraduate Physics Competition, 2009

研究领域

My research focuses on machine learning, reinforcement learning, multiagent systems, and their applications on smart cities and intelligent transportation systems.

近期论文

查看导师最新文章（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）

ESP: Exploiting Symmetry Prior for Multi-Agent Reinforcement Learning Xin Yu,?Rongye Shi(*), Pu Feng, Yongkai Tian, Jie Luo, and Wenjun Wu ECAI, 2023. (Supplementary here) [CCF-B] Air-M: A Visual Reality Many-Agent Reinforcement Learning Platform for Large-Scale Aerial Unmanned System Jiabin Lou, Wenjun Wu, Shuhao Liao, and?Rongye Shi(*) IEEE/RSJ IROS, 2023. [CCF-C] Energy Harvest of Multiple Smart Sensors With Real-Time Fault-Detection Chen Hou,?Rongye Shi(*), Qilong Huang(*), and Yifang Wang IEEE Transactions on Automation Science and Engineering (TASE), 2023.?[中科院1区Top, CCF-B] Physics-Informed Deep Learning for Traffic State Estimation: A Survey and the Outlook Xuan Di(*),?Rongye Shi, Zhaobin Mo, and Yongjie Fu Algorithms, 2023. [SCI] Robust Data Sampling in Machine Learning: A Game-Theoretic Framework for Training and Validation Data Selection Zhaobin Mo, Xuan Di(*), and?Rongye Shi Games, 2023. [SCI] A physics-informed deep learning paradigm for traffic state and fundamental diagram estimation Rongye Shi, Zhaobin Mo, Kuang Huang, Xuan Di(*), and Qiang Du IEEE Transactions on Intelligent Transportation Systems (TITS), 2022.?[中科院1区Top, CCF-B] TCACNet: Temporal and channel attention convolutional network for motor imagery classification of EEG-based BCI Xiaolin Liu(#),?Rongye Shi(#), Qianxin Hui, Susu Xu, Shuai Wang, Rui Na, Ying Sun, Wenbo Ding, Dezhi Zheng(*), and Xinlei Chen(*) Information Processing and Management (IP&M), 2022.?[中科院1区Top, CCF-B] ST-ICM: Spatial-Temporal Inference Calibration Model for Low Cost Fine-grained Mobile Sensing Chengzhao Yu(#), Ji Luo(#),?Rongye Shi, Xinyu Liu, Fan Dang, and Xinlei Chen(*) MobiCom, 2022. [CCF-A] Location Selection for Air Quality Monitoring with Consideration of Limited Budget and Estimation Error Zhiyong Yu, Huijuan Chang, Zhiwen Yu(*), Bin Guo, and?Rongye Shi IEEE Transactions on Mobile Computing (TMC), 2022.?[CCF-A, 中科院2区] Physics-Informed Deep Learning for Traffic State Estimation: A Hybrid Paradigm Informed By Second-Order Traffic Models Rongye Shi, Zhaobin Mo, and Xuan Di AAAI, 2021.?[CCF-A] Improving the On-Vehicle Experience of Passengers through SC-M*: A Scalable Multi-Passenger Multi-Criteria Mobility Planner Rongye Shi(*), Peter Steenkiste, and Manuela M. Veloso IEEE Transactions on Intelligent Transportation Systems (TITS), 2021.?[中科院1区Top, CCF-B] TRAMESINO: Traffic Memory System for Intelligent Optimization of Road Traffic Control Cristian Axenie(#),?Rongye Shi(#,*), Daniele Foroni(#), Alexander Wieder(#), Mohamad Al Hajj Hassan(#), Paolo Sottovia(#), Margherita Grossi(#), Stefano Bortoli(#), and Gotz Brasche Advanced Analytics and Learning on Temporal Data: 6th ECML PKDD Workshop, AALTD, 2021. [EI] OBELISC: Oscillator-Based Modelling and Control Using Efficient Neural Learning for Intelligent Road Traffic Signal Calculation Cristian Axenie(#),?Rongye Shi(#,*), Daniele Foroni(#), Alexander Wieder(#), Mohamad Al Hajj Hassan(#), Paolo Sottovia(#), Margherita Grossi(#), Stefano Bortoli(#), and Gotz Brasche ECML-PKDD, 2021. [CCF-B] A Physics-Informed Deep Learning Paradigm for Car-Following Models Zhaobin Mo, Rongye Shi, and Xuan Di(*) Transportation Research Part C: Emerging Technologies, 2021. [中科院1区Top] A survey on autonomous vehicle control in the era of mixed-autonomy: From physics-based to ai-guided driving policy learning Xuan Di(*), and?Rongye Shi Transportation Research Part C: Emerging Technologies, 2021. [中科院1区Top] Using Naturalistic Driving Data to Predict Mild Cognitive Impairment and Dementia: Preliminary Findings from the Longitudinal Research on Aging Drivers (LongROAD) Study Xuan Di,?Rongye Shi, Carolyn DiGuiseppi, David W. Eby, Linda L. Hill, Thelma J. Mielenz, Lisa J. Molnar, David Strogatz, Howard F. Andrews, Terry E. Goldberg, Barbara H. Lang, Minjae Kim, and Guohua Li(*) Geriatrics, 2021. [SCI] (Reported by?Forbes?and?ScienceDaily) An LSTM-Based Autonomous Driving Model Using a Waymo Open Dataset Zhicheng Li, Zhihao Gu, Xuan Di, and?Rongye Shi(*) Applied Sciences (Appl. Sci.), 2020.?[SCI Q2] SC-M*: A Multi-Agent Path Planning Algorithm with Soft-Collision Constraint on Allocation of Common Resources Rongye Shi(*), Peter Steenkiste(*), and Manuela M. Veloso(*) Applied Sciences (Appl. Sci.), 2019.?[SCI Q2] Generating Synthetic Passenger Data through Joint Traffic-Passenger Modeling and Simulation Rongye Shi, Peter Steenkiste, and Manuela Veloso ITSC, 2018. (I am a Co-Chair for a Data Mining Session!) [EI] Second-Order Destination Inference using Semi-Supervised Self-Training for Entry-Only Passenger Data Rongye Shi, Peter Steenkiste, and Manuela Veloso BDCAT, 2017. (received?NSF Student Travel Award) [EI] LightNN: Filling the Gap between Conventional Deep Neural Networks and Binarized Networks Ruizhou Ding, Zeye Liu,?Rongye Shi, Diana Marculescu, and R. D. (Shawn) Blanton GLSVLSI, 2017. (Best Paper Award) [CCF-C] On the Design of Phase Locked Loop Oscillatory Neural Networks: Mitigation of Transmission Delay Effects Rongye Shi, Thomas Jackson, Brian Swenson, Soummya Kar, and Lawrence Pileggi, IJCNN,?2016. [CCF-C] Implementing delay insensitive oscillatory neural networks using CMOS and emerging technology Thomas Jackson(*),?Rongye Shi, Abhishek A. Sharma, James A. Bain, Jeffrey A. Weldon, and Lawrence Pileggi, Analog Integrated Circuits and Signal Processing (AICSP), 2016. [SCI Q4] Digital-locking optically pumped cesium magnetometer Rongye Shi, Chang Liu, Sheng Zhou, and Yanhui Wang, EFTF, 2014. [EI] An Optically Detected Cesium Beam Frequency Standard with Magnetic State Selection Chang Liu,?Rongye Shi, Yanhui Wang, Shuqin Liu, and Taiqian Dong, EFTF, 2014. [EI] Study on Sensitivity-Related Parameters of DFB Laser-Pumped Cesium Atomic Magnetometer Gu Yuan,?Shi Rong-Ye, and Wang Yan-Hui(*) Acta Physica Sinica (Acta Phys. Sin.), 2014. [SCI Q4] Analysis of influence of RF power and buffer gas pressure on sensitivity of optically pumped cesium magnetometer Shi Rong-Ye, and Wang Yan-Hui(*) Chinese Physics B (Chin. Phys. B), 2013.?[SCI Q3]