MediaBrain
Shanghai Jiao Tong University
Siheng Chen 陈思衡
Recent News
2021/10 We are hiring! I am looking for motivated students (PostDoc, PhD, master, and undergraduate) to join my group. For prospective students, if you are passionate about Graph-structured data science, Group intelligence, and Autonomous driving, and interested in working with us, feel free to drop me an email!
2021/09 Three papers were accepted by NeurIPS 2021
2021/07 Three papers were accepted by ICCV 2021
2021/01 One paper was accepted by TPAMI
2021/01 One paper was accepted by ICLR 2021
Siheng Chen is an associate professor at Shanghai Jiao Tong University. Before that, he was a research scientist at Mitsubishi Electric Research Laboratories (MERL) and an autonomy engineer at Uber Advanced Technologies Group, working on the perception and prediction systems of self-driving cars. Before joining the industry, he was a postdoctoral research associate at Carnegie Mellon University. He received the doctorate in Electrical and Computer Engineering from Carnegie Mellon University(CMU) in 2016, where he also received two masters degrees in Electrical and Computer Engineering and Machine Learning, respectively. He received his bachelor’s degree in Electronics Engineering in 2011 from Beijing Institute of Technology, China. His paper “Discrete signal processing on graphs: Sampling theory” won the 2018 IEEE Signal Processing Society Young Author Best Paper Award. His co-authored paper received the Best Student Paper Award at IEEE GlobalSIP 2018.
His research mainly focuses on graph-structured data science, whose goal is to develop theories and algorithms to analyze large-scale data associated with complex and irregular structures. His research is conducted from three aspects: theory (graph signal processing), algorithms (graph neural networks), and applications (autonomous systems, human behavior analysis, 3D point cloud processing, and smart infrastructure). Please see more information in his CV and Google Scholar page.
Graph-structured data science
Data today is often generated from a diverse sources, including social, citation, biological, and physical infrastructure. Unlike time-series signals or images, such signals possess complex and irregular structures, which can be modeled as graphs. Analyzing graph signals requires dealing with the underlying irregular relationships. Graph signal processing generalizes the classical signal processing toolbox to the graph domain and provides a series of techniques to process graph signals, including graph-based transformations, sampling and recovery of graph signals, and graph topology learning. Graph neural networks provide a powerful framework to learn from graph signals with graphs as induced biases. Permeating the benefits of deep learning to the graph domain, graph convolutional networks and variants have attained remarkable success in social network analysis, 3D point cloud processing, quantum chemistry and computer vision.
In our research team, we promote the interpretability of graph signal processing as well as the learning power of graph neural networks. Our paper on sampling theory of graph signals is one of TOP 20 most cited papers among 13,816 publications in IEEE signal processing society over the past 10 years and also won IEEE signal processing society best young author paper award (selected from 7 journals over past 5 years). We also propose two frameworks, including graph scattering networks and graph unrolling networks, which provide two bridges to connect and upgrade graph signal processing and graph neural networks to achieve theoretical interpretability and learning ability at the same time; see more details in our latest T-PAMI paper: https://ieeexplore.ieee.org/abstract/document/9200568/ and NeurIPS 2021 paper: https://arxiv.org/pdf/2110.09807.pdf
Group intelligence
Group intelligence occurs when intelligent agents come together and collaborate. In a recent report of European commission, group intelligence is listed as one of the 100 Radical Innovation Breakthroughs for the future. Scientists at the MIT Center for Collective Intelligence are exploring “how people and computers can be connected so that—collectively—they act more intelligently than any person, group, or computer has ever done before.”
In our research team, we study a new perspective from graph-structure data science for group intelligence. We propose a collaboration graph to model the internal relationship among the agents and convert the optimization of collaboration strategy to the task of graph learning. We improve the efficiency of information refinement in collaboration by over 192 times; see more details in our latest NeurIPS 2021 paper: https://github.com/ai4ce/DiscoNet
Autonomous driving
As one of the most exciting engineering projects of the modern world, autonomous driving is an aspiration for many researchers and engineers across generations. It is a goal that might fundamentally redefine the future of human society and everyone’s daily life. Once autonomous driving becomes mature, we will witness a transformation of public transportation, infrastructure and the appearance of our cities. The world is looking forward to exploiting autonomous driving to reduce traffic accidents caused by driver errors, to save drivers’ time and liberate the workforce, as well as to save parking spaces, especially in the urban area.
In our research team, we study a new generation of perception and prediction system for autonomous driving. Perception is the task of perceiving the surrounding environment and extracting information that is related to navigation. By introducing V2X-based communication system, we could fundamentally resolve the occlusion and long-range issues encountered in traditional perception. Prediction is the task of predicting the future potential trajectories of each object in the 3D scene. We leverage graph neural networks to explicitly model underlying interactions among the traffic actors and provide more precise and interpretable predictions; see more details in our latest NeurIPS 2021 paper: https://arxiv.org/pdf/2110.13947.pdf
Journal
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- Siheng Chen, Baoan Liu, Chen Feng, Carlos Vallespi-Gonzalez, Carl Wellington, “3D point cloud processing and learning for autonomous driving“, IEEE Signal Processing Magazine 2021.
- Siheng Chen, Yonina C Eldar, Lingxiao Zhao, “Graph unrolling networks: Interpretable neural networks for graph signal denoising“, IEEE Transactions on Signal Processing 2021.
- Maosen Li, Siheng Chen, Yangheng Zhao, Ya Zhang, Yanfeng Wang, Qi Tian, “Multiscale spatio-temporal graph neural networks for 3D skeleton-based motion prediction“, IEEE Transactions on Image Processing.
- Maosen Li, Siheng Chen, Xu Chen, Ya Zhang, Yanfeng Wang, Qi Tian, “Symbiotic graph neural networks for 3D skeleton-based human action recognition and motion prediction“, IEEE Transactions on Pattern Analysis and Machine Intelligence 2021.
- S. Chen, Y. C. Eldar, and L. Zhao,“Graph unrolling networks: Interpretable neural networks for graph signal denoising”, IEEE Transactions on Signal Processing, submitted
- V. Ioannidis, S. Chen, and G. Giannakis,“Efficient and stable graph scattering transforms via pruning”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020
- M. Li, S. Chen, X. Chen, Y. Zhang, Y. Wang, and Q. Tian,“Symbiotic graph neural networks for 3D skeleton-based human action recognition and motion prediction”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020
- X. Chen, S. Chen, H. Zheng, J. Yao, K. Cui, Y. Zhang, and I. W. Tsang,“Node attribute generation on graphs”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020
- S. Chen, B. Liu, C. Feng, C. Vallespi-Gonzalez, and C. Wellington, “3D point cloud processing and learning for autonomous driving””, IEEE Signal Processing Magazine, Special Issue on Autonomous Driving, 2020.
- J-Z. Peng, S. Chen, N. Aubry, Z. Chen, W-T. Wu, “Time-variant prediction of flow over an airfoil using deep neural network ”, Physics of Fluids 32 (12), 2020
- J-Z. Peng, S. Chen, N. Aubry, Z. Chen, W-T. Wu, “Unsteady reduced-order model of flow over cylinders based on convolutional and deconvolutional neural network structure”, Physics of Fluids 32 (12), 2020
- J. Liu,S. Chen, M. Berges, J. Bielak, J. H Garrett, J. Kovačević, and H. Y. Noh, “Damage diagnosis algorithms for indirect structural health monitoring of bridges”, Mechanical Systems and Signal Processing, 2019
- J. Liu,S. Chen, George Lederman, David Kramer, H. Y. Noh, J. Bielak, J. H Garrett, J.~Kova\v{c}evi\’c, and M. Berges, “Dynamic responses of two passenger trains with the corresponding GPS positions, environmental conditions and weekly maintenance schedules in Pittsburgh’s light rail network”, Nature Scientific Data, 2019
- S. Chen, C. Duan, Y. Yang, D. Li, C. Feng and D. Tian, “FoldingNet++: Deep unsupervised learning of 3D point clouds via graph topology inference and filtering”, IEEE Trans. Image Process., 2020.
- S. Chen, D. Tian, C. Feng, A. Vetro and J. Kovačević, “Fast resampling of 3D point clouds via graphs”, IEEE Trans. Signal Process., 2018.
- Y. Yang, S. Chen, M. Maddah-Ali, P. Grover, S. Kar and J. Kovačević, “Fast temporal path localization on graphs via multiscale Viterbi decoding”, IEEE Trans. Signal Process., 2018.
- S. Chen, Y. Yang, S. Zong, A. Singh, and J. Kovačević, “Detecting localized categorical attributes on graphs”, IEEE Trans. Signal Process., 2017.
- G. Lederman, S. Chen, J. H. Garrett, J. Kovačević, H. Y. Noh, and J. Bielak, “ A data fusion approach for track monitoring from multiple in-service trains”. J. Mech. Syst. Signal Process., 2017.
- G. Lederman, S. Chen, J. H. Garrett, J. Kovačević, H. Y. Noh, and J. Bielak, “ Track monitoring from the dynamic response of a passing train: a sparse approach”, J. Mech. Syst. Signal Process., 2016.
- G. Lederman, S. Chen, J. H. Garrett, J. Kovačević, H. Y. Noh, and J. Bielak, “ Rail-monitoring from the dymanic response of an operational train”, J. Mech. Syst. Signal Process., 2016.
- S. Chen, R. Varma, A. Singh, and J. Kovačević, “Signal recovery on graphs: Fundamental limits of sampling strategies”, IEEE Trans. Signal and Inform. Process. over Networks, sp. iss. Inference and Learning over Networks, 2016.
- S. Chen, R. Varma, A. Sandryhaila, and J. Kovačević, “Discrete signal processing on graphs: Sampling Theory”, IEEE Trans. Signal Process., 2015.
- S. Chen, A. Sandryhaila, J. M. F. Moura, and J. Kovačević, “Signal recovery on graphs: Variation minimization”, IEEE Trans. Signal Process., 2015.
- S. Chen, F. Cerda, P. Rizzo, J. Bielak, J. H. Garrett, and J. Kovačević, “Semi-supervised multiresolution classification using adaptive graph filtering with application to indirect bridge structural health monitoring”, IEEE Trans. Signal Process., 2014.
- F. Cerda, S. Chen, J. Bielak, J. H. Garrett, P. Rizzo, and J. Kovačević, “Indirect structural health monitoring of a simplified laboratory-scale bridge model ”, Int. J. Smart Struct. Syst., sp. iss. Challenge on bridge health monitoring utilizing vehicle-induced vibrations, 2014.
Conference
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- Xingyue Pu, Tianyue Cao, Xiaoyun Zhang, Xiaowen Dong, Siheng Chen, “Learning to Learn Graph Topologies“, NeurIPS 2021.
- Yiming Li, Shunli Ren, Pengxiang Wu, Siheng Chen, Chen Feng, Wenjun Zhang, “Learning Distilled Collaboration Graph for Multi-Agent Perception“, NeurIPS 2021.
- Bohan Tang, Yiqi Zhong, Ulrich Neumann, Gang Wang, Ya Zhang, Siheng Chen, “Collaborative Uncertainty in Multi-Agent Trajectory Forecasting“, NeurIPS 2021.
- Zhen Xiang, David J Miller, Siheng Chen, Xi Li, George Kesidis, “A backdoor attack against 3D point cloud classifiers“, ICCV 2021.
- Tianyue Cao, Lianyu Du, Xiaoyun Zhang, Siheng Chen, Ya Zhang, Yan-Feng Wang, “CaT: Weakly supervised object detection with category transfer“, ICCV 2021.
- Chen Ju, Peisen Zhao, Siheng Chen, Ya Zhang, Yanfeng Wang, Qi Tian, “Divide and conquer for single-frame temporal action localization“, ICCV 2021.
- Maosen Li, Siheng Chen, Zihui Liu, Zijing Zhang, Lingxi Xie, Qi Tian, Ya Zhang, “Skeleton graph scattering networks for 3D skeleton-based human motion prediction“, ICCV Workshop on Graph Signal Processing 2021.
- Chao Pan, Siheng Chen, Antonio Ortega, “Spatio-temporal graph scattering transform“, ICLR 2021.
- S. Chen, Y. C. Eldar, “Graph signal denoising via unrolling networks“, In Proc. IEEE Int. Conf. Acoust., Speech Signal Process, 2021.
- S. Chen, Y. C. Eldar, “Time-varing graph signal inpainting via unrolling networks“, In Proc. IEEE Int. Conf. Acoust., Speech Signal Process, 2021.
- C. Xu, S. Chen*, M. Li, Y. Zhang*, “Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation“, AAAI 2021, accepted.
- C Pan, S. Chen, A Ortega, “Spatio-temporal graph scattering transform“, ICLR 2021.
- M Li, S Chen, Y Zhang, IW Tsang, “Graph cross networks with vertex infomax pooling“, Neural Information Processing Systems (NeurIPS) 2020.
- P. Wu, S. Chen, and D. Metaxas,”MotionNet: Joint perception and motion prediction for autonomous driving based on BEV maps”, In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). 2020.
- M. Li, S. Chen, Y. Zhang, and Y. Wang,“Dynamic multiscale graph neural networks for category-agnostic 3D skeleton-based motion prediction”, In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2020, Oral.
- Y. Hu, S. Chen,Y. Zhang, and Y. Wang,“Collaborative motion prediction via neural message passing”, In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2020, Oral.
- J. Liu, B. Chen, S. Chen, M. Berges, J. Bielak, H. Noh,“Damage-sensitive and domain-invariant feature extraction for vehicle-vibration-based bridge health monitoring”, In Proc. IEEE Int. Conf. Acoust., Speech Signal Process. 2020.
- S. Chen, N. Zhang, and H. Sun,“Collaborative localization based on traffic landmarks for autonomous driving”, International Symposium on Circuits and Systems (ISCAS), 2020.
- V. Ioannidis, S. Chen, and G. Giannakis,“Pruned graph scattering transforms”, International Conference on Learning Representations (ICLR), 2020.
- S. Chen, S. Niu, T. Lan and B. Liu, “PCT: Large-scale 3D point cloud representations via graph inception networks with applications to autonomous driving”, In Proc. IEEE Int. Conf. on Image Process., Taipei, Taiwan, Sep. 2019.
- M. Li, S. Chen, X. Chen, Y. Zhang, Y. Wang and Q. Tian, “Actional-structural graph convolutional networks for 3D skeleton-based action recognition”, In Proc. IEEE Computer Vision and Pattern Recognition, Long Beach, CA, June 2019. [code]
- Y. Hu, S. Chen, X. Chen, Y. Zhang and X. Gu “Neural message passing for visual relationship detection”, ICML Workshop (Learning and Reasoning with Graph-Structured Representations), June. 2019.[code]
- C. Duan, S. Chen and J. Kovačević, “3D point cloud denoising via deep-neural-network based local surface estimation”, In Proc. IEEE Int. Conf. Acoust., Speech Signal Process., Brighton, UK, May 2019. [code]
- C. Duan, S. Chen and J. Kovačević, “Weighted multiple-projection: 3D point cloud denoising with tangent planes”, In Proc. IEEE Glob. Conf. Signal Information Process., Anaheim, California, Nov. 2018. [code]
- S. Niu*, S. Chen*, H. Guo, C. Targonski, M. C. Smith and J. Kovačević, “Generalized value iteration networks: Life beyond lattices”, In Proc. AAAI., New Orleans, Feb. 2018. [code]
- S. Chen, D. Tian, C. Feng, A. Vetro and J. Kovačević, “Contour-enhanced resampling of 3d point clouds via graphs”, Proc. IEEE Int. Conf. Acoust., Speech Signal Process., New Orleans, 2017.
- Y. Yang, S. Chen, M. Ali Maddah-Ali, P. Grover, S. Kar and J. Kovačević, “Fast path localization on graphs via multiscale Viterbi decoding”, Proc. IEEE Int. Conf. Acoust., Speech Signal Process., New Orleans, 2017.
- R. Varma, S. Chen and J. Kovačević, “Graph topology recovery for regular and irregular graphs”, In IEEE CAMSAP, Curacao, Netherlands Antilles, Dec. 2017.
- S. Chen, Y. Yang, A. Singh, and J. Kovačević, “Signal detection on graphs: Bernoulli noise model”, In Proc. IEEE Glob. Conf. Signal Information Process., Washington, DC, Dec. 2016.
- S. Chen, Y. Yang, C. Faloutsos, and J. Kovačević, “Monitoring Manhattan’s traffic at 5 intersections?”, In Proc. IEEE Glob. Conf. Signal Information Process., Washington, DC, Dec. 2016.
- S. Chen, R. Varma, A. Singh and J. Kovačević, “A statistical perspective of sampling scores for linear regression”, Proc. IEEE International Symposium on Information Theory, Barcelona, Spain, 2016.
- S. Chen, R. Varma, A. Singh and J. Kovačević, “Representations of piecewise smooth signals on graphs”, Proc. IEEE Int. Conf. Acoust., Speech Signal Process., Shanghai, China, 2016.
- R. Varma, S. Chen and J. Kovačević, “Spectrum-blind signal recovery on graphs”, IEEE CAMSAP, Cancun, Mexico, 2015.
- T. Ji, S. Chen, R. Varma and J. Kovačević, “Efficient route planning of autonomous vehicles based on graph signal recovery”, Allerton 2015.
- S. Chen, R. Varma, A. Singh and J. Kovačević, “Signal recovery on graphs: Random versus experimentally designed sampling”, SampTA 2015, Washington, D.C., May, 2015.
- S. Chen, A. Sandryhaila, and J. Kovačević, “Sampling theory for graph signals”, Proc. IEEE Int. Conf. Acoust., Speech Signal Process., Brisbane,Australia, April, 2015.
- S. Chen, A. Sandryhaila, and J. Kovačević, “Distributed algorithm for graph signal inpainting”, Proc. IEEE Int. Conf. Acoust., Speech Signal Process., Brisbane,Australia, April, 2015.
- S. Bittner, S. Chen, and J. Kovačević, “ Fast algorithm for neural network reconstruction”, Proc. IEEE Int. Symposium on Biomedical Imaging., Brooklyn, NY, April, 2015.
- S. Chen, A. Sandryhaila, J. M. F. Moura, and J. Kovačević, “Signal denoising on graphs via graph filtering,”, Proc. IEEE Glob. Conf. Signal Information Process., Atlanta, GA, Dec, 2014
- S. Chen, A. Sandryhaila, G. Lederman, Z. Wang, J. M. F. Moura, P. Rizzo, J. Bielak, J. H. Garrett, and J. Kovačević, “Signal inpainting on graphs via total variation minimization”, Proc. IEEE Int. Conf. Acoust., Speech Signal Process., Florence, Italy, May 2014.
- G. Lederman, Z. Wang, S. Chen, T. Tang, F. Cerda, J. Bielak, J. H. Garrett, J. Kovačević, H. Noh, and P. Rizzo, “Damage quantification and localization algorithms for indirect SHM of bridges”, Proc. Int. Conf. Bridge Maint., Safety Manag., Shanghai, China, July 2014.
- S. Chen, A. Sandryhaila, J. M. F. Moura, and J. Kovačević, “Adaptive graph filtering: Multiresolution classification on graphs”, Proc. IEEE Glob. Conf. Signal Information Process., Austin, TX, Dec. 2013.
- S. Chen, F. Cerda, J. Guo, J. B. Harley, Q. Shi, P. Rizzo, J. Bielak, J. H. Garrett and J. Kovačević, “Multiresolution classification with semi-supervised learning for indirect bridge structure health monitoring”, Proc. IEEE Int. Conf. Acoust., Speech Signal Process., Vancouver, Canada, May 2013.
- Z. Wang, S. Chen, G. Lederman, F. Cerda, J. Bielak, J. H. Garrett, P. Rizzo and J. Kovačević, “Comparison of sparse representation and Fourier discriminant methods: Damage location classification in indirect lab-scale bridge structural health monitoring”, Proc. Structures Congr., Pittsburgh, PA, May 2013.
- F. Cerda, J. H. Garrett, J. Bielak, P. Rizzo, J. A. Barrera, Z. Zhang, S. Chen, M. T. McCann, and J. Kovačević, “Indirect structural health monitoring in bridges: Scale experiments”, Proc. Int. Conf. Bridge Maint., Safety Manag., Lago di Como, Italy, July 2012.
Thesis
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- S. Chen, “Data science with graphs: A signal processing perspective”, Ph.D Thesis in ECE, 2016.
- S. Chen, “Adaptive sampling for urban traffic monitoring”, Master Thesis in MLD, 2016.
Preprint
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- S. Chen, S. Niu, L. Akoglu, J. Kovačević and C. Faloutsos, “Fast, Warped Graph Embedding: Unifying Framework and One-Click Algorithm ”. [code]
- S. Chen, Y. Yang, J. M. F. Moura, and J. Kovačević, “Localization, decomposition and dictionary learning of piecewise-constant signals on graphs”.
- S. Chen, R. Varma, A. Singh, and J. Kovačević, “Signal representations on graphs: Tools and applications”.
- S. Chen, Q. Gao, C. Li, J. Kovačević and C. Faloutsos “ZoomRank: Bridging PageRank and HITS”.
MediaBrain陈思衡老师 招【2022与2023级】直博/直硕生啦！
陈老师年初刚刚回国，是未来媒体网络协同创新中心的长聘轨副教授，2019年国家重大人才工程青年项目入选者！
陈老师在CMU获得博士学位，博士后完成后，在Uber Advanced Technologies Group、三菱电机实验室MERL担任Research Scientist，至今在TPAMI、TIP、NeurIPS（oral）、CVPR （oral）、AAAI （oral）、ICLR上发表了50余篇论文，Google Scholar引用2000余次，获得过IEEE信号处理协会最佳年轻作者论文奖！学界+业界影响力Max！
陈老师与海外各高校包括（CMU、Oxford、…）以及国内外各企业（Uber、滴滴、…）都有着很强的connection，可以推荐你到世界顶尖的平台学习交流！
如果你数学与编程功底扎实，踏实好学，对计算机视觉与机器学习有一定的了解；
如果你想做【一流的学术】工作、在国际顶级期刊和会议发表【高水平 & 有学术影响力】的论文；
如果你对图信号处理、图神经网络、无人系统等领域感兴趣，想要探索计算机视觉、机器学习和自动驾驶的问题；
如果对科研有热情，希望能够加入一个积极向上、开放包容、有激情有理想地探索科学的、顶级的科研氛围与团队；
那么欢迎你加入我们！
加入我们，你会接受系统的科研工作练习，陈老师与学长学姐带你快速适应科研节奏，参与到我们正在开展的工作中，有机会在一年做出优质的科研工作并发表顶会论文！
请有意向的同学发送简历至：sihengc[AT]sjtu[DOT]edu[DOT]cn
也欢迎朋友们帮忙转发，期待大家共同进步！
303A, Cooperative Medianet Innovation Center, Building 5
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University
800 Dongchuan Road, Minhang District, Shanghai 200240, China
Email: sihengc@sjtu.edu.cn