Tensorlet Team

The top ending tensor and deep learning technology lab!
A young team, professional in GPU tensor and Deep Learning technology, commits to creating top AI algorithms and solutions for corporates, labs, schools and communities.

Our team Includes professors, doctors, graduate students worldwide. The main lab located in Columbia University in the City of New York. By reaching us, you will reach the top ending AI science.

We are full of interesting creative ideas!

A iuvenis team, professio technology in omni doctrinae genere et GPU gluteus maximus, AI fecerit summo usque ad partum, et algorithms solutions ad corporates, Labs, et Communitates scholarum.

Projects

What we are good at.

cuTensor

We optimize GPU tensor library in CUDA environment, providing efficient memory access, batch mode computations and shards mode computations.
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Tensor Decomposition

We study tensor decompositions (CPU, Tucker, Tensor-train/Tensor-ring,tensor networks, etc) and propose a transform-based tensor decomposition.
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Tensor Networks

We propose secure protocols to support tensor computations on the cloud. Homomophic encrytion differential privacy in Cloud Computing and SN.
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Deep Learning

We apply deep learning and deep reinforcement learning to IoT tasks. Also, tensor structures are exploited and tensor operations are accelerated.
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AI4Finance

We design efficient algorithms for big data and deep learning. Our algorithms are effective algorithms for tensor time series prediction.
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AIoT

Tensor for Intelligent IoT & ITS: Sensory big data analysis; Smartphone applications; RF tomography (VR/AR); Intelligent transportation systems.
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Meet our team

Our team in detailed.

Team Members

Organizer of NeurIPS 2021, 2020 workshop on quantum tensor networks in machine learning. Link.
Organizer of IJCAI 2020 workshop on tensor network representation in machine learning. Link.
          NeurIPS 2021/2020/2019 Workshop on Machine Learning for Autonomous Driving. 2020 Link, 2019 Link.
          ICCV Workshop on Autonomous Vehicle Vision (AVVision), 2021 link.
Senior program committee for IJCAI 2021. Session Chair for IJCAI 2019;
TPC members for conferences NeurIPS, ICML, ICLR, AAAI, IJCAI, Multimedia, ICAIF, etc.
          Reviewers for IEEE PAMI, TNNLS, TIP, TPDS, ToN, TMC, JSAC, TDSC, TIFS, TITS, IoT Journal, etc.
IEEE International Symposium on Edge Computing Security and Blockchain (EdgeBlock 2020) with IEEE INFOCOM 2020. Link.
ACM International Symposium on Blockchain and Secure Critical Infrastructure (BSCI 2019). Link.
  • Miao Yin, Master, University of Electronic Science and Technology of China, 2016~2018. PhD student in Electrical and Computer Engineering of Rutgers University, 2019.09~present
[CVPR] M. Yin, S. Liao, X.-Y. Liu, X. Wang, B. Yuan*. Towards extremely compact recurrent neural networks: Enabling few thousand parameters-only RNN models for video recognition with fully decomposed hierarchical Tucker structure. CVPR, 2021.
[ICCAD] C. Deng, M. Yin, X.-Y. Liu, X. Wang, B. Yuan. High-performance hardware architecture for tensor singular value decomposition (Invited paper). International Conference on Computer-Aided Design (ICCAD), 2019.
[Geophysics] F. Qian, M. Yin, X.-Y. Liu, Y.-J. Wang, C. Lu, G.-M. Hu. Unsupervised seismic facies analysis via deep convolutional autoencoders. Geophysics, 2018.
[IJCAI Workshop] Z. Ding, X.-Y. Liu, M. Yin, L. Kong, Tensor super-resolution with generative adversarial nets: A large image generation approach. IJCAI Joint Workshop on Human Brain and Artificial Intelligence, 2019.
  • Hongyang Yang, Chief Data Scientist at AI4Finance, 03/01/2020~ 01/31/2021.
Master, Department of Statistics, Columbia University, 2016.09~2017.12.
Data Scientist at Gloabl AI, 2017.03~2018.07. Data Scientist at Credit Suisse (in Wall Street), 2018.07~2019.03.
Senior Data Scientist at Moody’s Analytics, 2019.03~2020.03. Machine Learning Job Hunting.
[NeurIPS] X.-Y. Liu, Z. Xia, J. Rui, J. Gao, Hongyang Yang, M. Zhu, C. Wang, Z. Wang, J. Guo. FinRL-Meta: Market environments and benchmarks for data-driven financial reinforcement learning. NeurIPS Special Track on Datasets and Benchmarks, 2022.
          [ICAIF] Xiao-Yang Liu, Hongyang Yang, J. Gao, C. Wang. FinRL: Deep reinforcement learning framework to automate trading in quantitative finance. ACM International Conference on AI in Finance,  2021.
[NeurIPS Workshop] Xiao-Yang Liu, Hongyang Yang, Q. Chen, R. Zhang, L. Yang, B. Xiao, C. D. Wang. FinRL: A deep reinforcement learning library for automated stock trading in quantitative finance. NeurIPS Workshop on Deep Reinforcement Learning, 2020.
[ICAIF] Hongyang Yang, Xiao-Yang Liu (co-primary), S. Zhong, A. Walid, Deep reinforcement learning for automated stock trading: an ensemble strategy. ACM International Conference on AI in Finance,  2020.
[BigData] Y. Fang, X.-Y. Liu, Hongyang Yang. Practical machine learning approach to capture the scholar data driven alpha in AI industry. IEEE Big Data, 5th Special Session on Intelligent Data Mining, 2019.
[NeurIPS Workshop] X. Li, Y. Li, Hongyang Yang, L. Yang, X.-Y. Liu. DP-LSTM: A differential privacy framework for stock prediction based on financial news. Robust AI in FS 2019 : NeurIPS 2019 Workshop on Robust AI in Financial Services: Data, Fairness, Explainability, Trustworthiness, and Privacy, 2019.
[NeurIPS Workshop] Z. Xiong, X.-Y. Liu, S. Zhong, Hongyang Yang, A. Walid. Practical deep reinforcement learning approach for stock trading. NeurIPS Workshop on Challenges and Opportunities for AI in Financial Services: the Impact of Fairness, Explainability, Accuracy, and Privacy, 2018.
[TrustCom] Hongyang Yang, X.-Y. Liu, Qingwei Wu. A practical machine learning approach for dynamic stock recommendation. IEEE TrustCom, 2018.

Advisors

  • Prof. Xiaodong Wang, IEEE Fellow, Columbia University in the City of New York.

  • Anwar Walid, IEEE Fellow, Director of the Network intelligence and Distributed Systems, Nokia-Bell Labs;  Adjunct Professor, Columbia University in the City of New York.

Collaborators

  • Zhaoran Wang, Assistant professor, Northwestern University.
  • Qibin Zhao, Unit Leader, Tensor Learning Unit, Riken Lab, Janpan.
  • Christina Dan Wang, Assistant Professor of Finance, NYU Shanghai; Global Network Assistant Professor, NYU.
  • Tao Zhang, Assistant Professor, Shanghai University.
  • Cai Fu, Professor of Computer School, Huazhong University and Science and Technology.

Current Student Team Members

  • Zihan Ding, Master, Artificial Intelligence, Imperial College London, 2018.9~2019.7; PhD in Princeton University.
[IJCAI Workshop] Zihan Ding, X.-Y. Liu, M. Yin, L. Kong, Tensor super-resolution with generative adversarial nets: A large image generation approach. IJCAI Joint Workshop on Human Brain and Artificial Intelligence, 2019.
[NeurIPS Workshop] X.-Y. Liu, Zihan Ding, S. Borst, A. Walid. Deep reinforcement learning for intelligent transportation systems. NeurIPS Workshop on Machine Learning for Intelligent Transportation Systems, 2018.
  • Yigong Hu, Master, Electrical Engineering, Columbia University, 2019.09~2020.04. PhD at UIUC, 2020.09.
  • Jiawei Ma, Master, Electrical Engineering, Columbia University, 2019.1~2019.08. Now, PhD at Columbia University.

[ICCV] Jiawei Ma, X.-Y. Liu (co-primary author), Z. Shou, X. Yuan. Deep tensor ADMM-net for snapshot compressive imaging. ICCV, 2019.
  • Yimeng Zhang, Master, Electrical Engineering, Columbia University, 2019.07~2020.12. Now, PhD at Michigan State University.

[ACMMM] Yimeng Zhang, X.-Y. Liu (co-primary author), B. Wu, A. Walid. Video synthesis via transform-based tensor neural networks. ACM Multimedia, 2020.
[AAAI] X. Han, B. Wu, Z. Shou, X.-Y. Liu, Yimeng Zhang, L.Kong. Tensor FISTA-net for real-time snapshot compressive imaging. AAAI, 2020.
[TALLIP] X.-Y. Liu, Yimeng Zhang, Y. Liao, L. Jiang. Dynamic updating of knowledge base for large-scale question answering system. ACM Transactions on Asian and Low-Resource Language Information Processing, 2020.

  • Iris Yang, Bachelor, Computer Science, Columbia University, 2019.6~

[EdgeBlock] Liuqing Yang, X.-Y. Liu, W. Gong, Secure smart home systems: A blockchain perspective. IEEE INFOCOM Workshop on International Symposium on Edge Computing Security and Blockchain (EdgeBlock), 2020.

[URTC] Liuqing Yang, X.-Y. Liu. Tensor nuclear-norm minimization for snapshot compressive imaging cameras. MIT Undergraduate Research Technology Conference, 2019. [PDF]

[SmartBlock] Liuqing Yang, X.-Y. Liu, X. Li, Y. Li. Price prediction of cryptocurrency: empirical analysis. SmartBlock 2019.
[NeurIPS Workshop] X. Li, Y. Li, H. Yang, Liuqing Yang, X.-Y. Liu. DP-LSTM: A differential privacy framework for stock prediction based on financial news. Robust AI in FS 2019 NeurIPS 2019 Workshop on Robust AI in Financial Services: Data, Fairness, Explainability, Trustworthiness, and Privacy, 2019.

Alumni

  • Xiaocheng Han, Bachelor, Computer Science, Shanghai Jiao Tong University, summer intern, 2019.07~2019.12.  Now at Carnegie Mellon University.
          [AAAI] Xiaocheng Han, B. Wu, Z. Shou, X.-Y. Liu, Y. Zhang, L.Kong. Tensor FISTA-net for real-time snapshot compressive imaging. AAAI, 2020.
  • Paul Zhuoran Xiong, Master, Electrical Engineering, Columbia University, 2018.09~2019.02. Ph.D student, McGill University, 2019.09~
[NeurIPS Workshop] Z. Xiong, X.-Y. Liu, S. Zhong, H. Yang, A. Walid. Practical deep reinforcement learning approach for stock trading. NeurIPS Workshop on Challenges and Opportunities for AI in Financial Services: the Impact of Fairness, Explainability, Accuracy, and Privacy, 2018.
  • Runjia Luna Zhang, Bachelor, Electrical Engineering, Columbia University, 2018.9~2019.08. Riken Lab, 2019.09~

[URTC] R. Zhang, Z. Huang, X.-Y. Liu. Machine learning approach for art market. MIT Undergraduate Research Technology Conference, 2019. [PDF]
[HPCC] H. Li, T. Zhang, R. Zhang, X.-Y. Liu. High-performance tensor decoder on GPUs for wireless camera networks in IoT. IEEE HPCC 2019.
  • Chen Shang, Bachelor, Mathematic, University of Michigan–Ann Arbor, 2017.6~2018.8.
[URTC] Chen Shang, X.-Y. Liu. Neural networks’ capability of recognizing symmetry structures: A tensor perspective. MIT Undergraduate Research Technology Conference, 2019. [PDF]
[TrustCom] H. Zhou, X.-Y. Liu, C. Fu, Chen Shang, X. Chang. Differentially private matrix completion via distributed matrix factorization. IEEE TrustCom, 2018.
  • Xinyi Li, Master, Department of Statistics, Columbia University, 2018.9~2019.06. Feizai Co. Ltd., 2019.07~

[NeurIPS Workshop] X. Li, Y. Li, H. Yang, L. Yang, X.-Y. Liu. DP-LSTM: A differential privacy framework for stock prediction based on financial news. Robust AI in FS 2019 : NeurIPS 2019 Workshop on Robust AI in Financial Services: Data, Fairness, Explainability, Trustworthiness, and Privacy, 2019.
[ICML Workshop] X. Li, Y. Li, Y. Zhan, X.-Y. Liu. Optimistic Bull or Pessimistic Bear: adaptive deep reinforcement learning for stock portfolio allocation. ICML Workshop on AI in Finance: Applications and Infrastructure for Multi-Agent Learning, 2019.
[KDD Workshop] X. Li, Y. Li, X.-Y. Liu, D. Wang, Risk management via anomaly circumvent: mnemonic deep learning for midterm stock prediction. KDD Workshop on Anomaly Detection in Finance, 2019.

  • Ming Zhu, Post Doc, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences.

  • Yinchuan Li, Visiting Scholar, PhD at Beijing Institute of Technology.
  • Rui She, Visiting Scholar, PhD at Tsinghua University, 2019.01~2019.07.

  • Tianyu Geng, Visiting Scholar, PhD at Nankai University, 2018.09~2019.09.
  • Wenhang Bao, Master, Department of Statistics, Columbia University, 2016.09~2017.12.  Now, Senior Consultant/Data Scientist at Capgemini.

[NeurIPS Workshop] W. Bao, X.-Y. Liu. Spatial influence-aware reinforcement learning for intelligent transportation system. NeurIPS Workshop on Machine Learning for Autonomous Driving, 2019.
[ICML Workshop] W. Bao, X.-Y. Liu, Multi-agent reinforcement learning for liquidation strategy analysis. ICML Workshop on AI in Finance: Applications and Infrastructure for Multi-Agent Learning, 2019.

  • Qingwei Wu, Master, Electrical Engineering, Columbia University, 2017.09~2018.05. Microsoft at Suzhou, China.

[NeurIPS Workshop] W. Lu, X.-Y. Liu, Qingwei Wu, Y. Sun, A. Walid. Transform-based multilinear dynamical system for tensor time series analysis. NeurIPS Workshop on Modeling and Decision-Making in the Spatiotemporal Domain, 2018.
H. Yang, X.-Y. Liu, Qingwei Wu. A practical machine learning approach for dynamic stock recommendation. IEEE TrustCom, 2018.

  • Chenxiao Zhu, Master, Hong Kong University of Science and Technology, 2017.09~2018.05.

Chenxiao Zhu, L. Xu, X.-Y. Liu, F. Qian. Tensor-generative adversarial network with two-dimensional sparse coding: application to real-time indoor localization. IEEE International Conference on Communications (ICC), 2018.

For manuscripts

TensorLet@gmail.com
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