WELCOME TO THE COMPUTATIONAL INTELLIGENCE AND LEARNING SYSTEMS (CILS) LAB

ABOUT ME

Dr. Tang is an Assistant Professor in the Department of Electrical and Computer Engineering at Mississippi State University. He received his B.S. and M.S. degrees from Central South University in 2007, and Institute of Electronics, Chinese Academy of Sciences in 2010, respectively. From 2010 to 2012, he worked at Beijing National Railway Research Design Institute of Signals and Communications. From 2012 to 2016, he studied at University of Rhode Island (Kingstown, RI) under the supervision of Prof. Haibo He, and received his Ph.D. degree in electrical engineering in 2016. From 2016 to 2017, he worked as an Assistant Professor in the Department of Computer Science at Hofstra University, Hempstead, NY.
His research interests lie in the general areas of Statistical Machine Learning, Data Mining, Cyber Security, and their applications in Cyber-Physical Systems (e.g., smart grid, smart healthcare, smart transportation, etc.). In particular, he is interested in developing new statistical machine learning algorithms (e.g., Bayesian learning, transfer learning and deep learning) and building complex embedded systems (e.g., robotics and autonomous vehicles) that are robust, adaptive and fault tolerant to uncertain environments.
Dr. Tang is the recipient of NIJ New Investigator/Early Career Award (2018), Chinese Government Award for Outstanding Students Abroad (2016), Best Paper Award in IEEE CCWC (2018), Best Student Paper Award in IJCNN (2016), Junior Faculty Travel Award by Army Research Office (2016), Travel Award by IEEE CNAS (2015), IEEE Computational Intelligence Magazine Publication Spotlight Paper (2015), and Outstanding Academic Student Scholarship in Institute of Electronics, Chinese Academy of Sciences and Central South University for several times.

Multiple Ph.D. Assistantship Positions are Available:
Two new fully funded Ph.D. positions are available in the Department of Electrical and Computer Engineering at Mississippi State University (Starkville, MS). Highly motivated students for research with a strong mathematical background and proficiency in computer programming/simulation are welcome. Please contact Dr. Tang for more information (Interested students are encouraged to send your resume and transcripts with your TOEFL and GRE scores).

NEWS

  • April/2021: Our Cloud Computing-based Anomaly Detection paper has been accepted in Proceedings of IEEE.
  • April/2021: Bo received an ARO STIR Grant to support our Deep Transfer Learning research. Thanks ARO!
  • Mar/2021: Bo received the Research Excellence (2020) certificate in ECE Department at MSU.
  • Jan/2021: Our Lifelong Learning paper has been accepted in IEEE Trans. on Neural Networks and Learning Systems (TNNLS).
  • Jan/2021: Congratulations to Jason Farmer, who received the Dean's Office Undergraduate Researcher Award (2020).
  • Sep/2020: Our Fuzzy DQN paper has been accepted in IEEE Trans. on Intelligent Transportation Systems.
  • Aug/2020: Our Deraining paper has been accepted in IEEE Access.
  • Aug/2020: Bo received an NSF CCRI: Medium: Collaborative Research: Open AI Cellular (OAIC) (Co-PI), to support our open AI research. Thanks NSF!
  • July/2020: Our CSLM-DNC Lifelong Learning paper has been accepted in IEEE Trans. on Neural Networks and Learning Systems (TNNLS).
  • Apr/2020: Our NISC Lifelong Learning paper has been accepted in IEEE Trans. on Neural Networks and Learning Systems (TNNLS).
  • Dec/2019: Bo received an ONR grant (Co-PI), $249,471, to support our AI-enabled 5G security research. Thanks ONR!
  • May/2019: Bo delivered several talks in Chongqing University, Central South University, Chinese Academy of Sciences, and Beijing University of Chemical Technolgy.
  • Apr/2019: Our paper has been accepted in Pattern Recognition Letters.
  • Apr/2019: Our paper has been accepted in IEEE Signal Processing Letters.
  • Feb/2019: Bo received two-year research funding from the Tennessee Valley Authority (TVA), $100,000 per year, 2019-2020! Thanks TVA!
  • Dec/2018: Bo received the prestigious NIJ New Investigator/Early Career Award (PI), NIJ, $599,121, 2019 - 2021! Thanks NIJ!
  • Sept/2018: Congratulations to Nicholoas Smith, who received the Dean's Office Undergraduate Researcher Award (2019).
  • Sept/2018: Our magazine paper has been accepted in IEEE Intelligent Transportation System Magazine!
  • Mar/2018: Bo delivers an invited talk on "Detection of Unauthorized Electricity Consumption using Machine Learning" at 16th Annual i-PCGRID Workshop, San Francisco, 2018!
  • Jan/2018: "MILE: A Minimally Interactive Learning Framework for Visual Data Analysis" has received the Best Paper Award in IEEE CCWC 2018!
  • Nov/2017: Our journal paper has been accepted in IEEE Trans. on Image Processing!
  • Sept/2017: Our journal paper has been accepted in Mechanical Systems and Signal Processing!
  • Sept/2017: Our conference paper has been accepted by SSCI 2017 (Honolulu, Hawaii)!
  • May/2017: Our conference paper has been presented in IJCNN 2017 (Anchorage, AK)!
  • April/2017: Our journal paper has been accepted by Pattern Recognition!
  • March/2017: Our journal paper has been accepted by IEEE Trans. on Big Data!
  • March/2017: Our journal paper has been accepted by IEEE Trans. on Industrial Informatics!
  • March/2017: Our SPL paper has been presented in ICASSP 2017 (New Orleans, Louisiana)!
  • Feb/2017: Our journal paper has been accepted by Neurocomputing!
  • Dec/2016: Our journal paper has been accepted by IEEE Trans. on Neural Networks and Learning Systems!
  • SELECTED PUBLICATION LIST

    Journals



    1. Q. Du, Bo Tang, W. Xie, and W. Li. Parallel and Distributed Computing for Anomaly Detection from Hyperspectral Remote Sensing Imagery.  Proceedings of IEEE, In Press, 2021. [Impact Factor: 10.252]
    2. J. Peng, H. Li, Bo Tang. Overcoming Long-term Catastrophic Forgetting through Adversarial Neural Pruning and Synaptic Consolidation.  IEEE Trans. on Neural Networks and Learning Systems, In Press, 2021. [Impact Factor: 11.683]
    3. L. Chen, X. Hu, Bo Tang and Y. Cheng. Conditional DQN-based Motion Planning with Fuzzy Logic for Autonomous Driving.  IEEE Trans. on Intelligent Transportation Systems, In Press, 2020. [Impact Factor: 6.319]
    4. S. Sharma, Bo Tang, J. Ball, D. CARRUTH, and L. Dabbiru. Recursive Multi-Scale Image Deraining with Sub-Pixel Convolution Based Feature Fusion and Context Aggregation.  IEEE Access, In Press, 2020. [Impact Factor: 3.745]
    5. S. Xiang and Bo Tang. CSLM: Convertible Short-term and Long-term Memory in Differential Neural Computers.  IEEE Trans. on Neural Networks and Learning Systems, In Press, 2020. [Impact Factor: 11.683]
    6. Bo Tang, H. He, and S. Zhang. MCENN: A Variant of Extended Nearest Neighbor Method for Pattern Recognition.  Pattern Recognition Letters, In Press, 2019. [Impact Factor: 3.255]
    7. S. Xiang and Bo Tang. Kernel-based Edge-Preserving Methods for Abrupt Change Detection.  IEEE Signal Processing Letters, In Press 2019. [Impact Factor: 3.268]
    8. X. Hu, Bo Tang, S. Song, and X. Tong. A Deep Cascaded Neural Network for Multiple Motion Commands Prediction in Autonomous Driving.  Mechanical Systems and Signal Processing, In Submission 2019. [Impact Factor: 5.005]
    9. L. Chen, X. Hu, Bo Tang, Y. Cheng, and F. Wang. Conditional DQN-based Motion Planning with Fuzzy Logic for Autonomous Driving.  IEEE Transactions on Fuzzy Systems, In Submission 2019. [Impact Factor: 8.759]
    10. J. Ai, R. Tian, X. Yang, J. Zhao, J. Jin, and Bo Tang. Multi-Scale Rotation-Invariant Haar-Like Feature Integrated CNN based Ship Detection Algorithm of Multiple-Target Environment in SAR Imagery . IEEE Transactions on Geoscience and Remote Sensing, In Submission 2019. [Impact Factor: 5.63]
    11. B. Saravi, P. Nejadhashemi, and Bo Tang. Quantitative Model of Irrigation Effect on Maize Yield by Deep Neural Network. Neural Computing and Applications, In Press 2019. [Impact Factor: 4.664]
    12. J. Ball and Bo Tang. Machine Learning and Embedded Computing in Advanced Driver Assistance Systems (ADAS). Electronics, Editoral Paper, In Press 2019.
    13. J. Ai, R. Liu, Bo Tang, L. Jia, J. Zhao, F. Zhou. A Refined Bilateral Filtering Algorithm based on Adaptively-Trimmed-Statistics for Speckle Reduction in SAR Imagery. IEEE Access, In Press 2019. [Impact Factor: 4.098]
    14. X. Li, Bo Tang, J. Ball, M. Doude, D. Carruth. Rollover-Free Path Planning for Off-Road Autonomous Driving. Electronics, In Press 2019.
    15. S. Sharma, Bo Tang, J. Ball, M. Doude, D. Carruth, M. Islam. Semantic Segmentation with Transfer Learning for Off-Road Autonomous Driving. Sensors, In Press 2019. [Impact Factor: 3.427]
    16. L. Chen, X. Hu, Bo Tang and D. Cao. Parallel Motion Planning: Learning a Deep Planning Model Against Emergencies. IEEE Intelligent Transportation Systems Magazine, In Press, 2018. [Impact Factor: 3.363]
    17. H. Chen, F. Zhang, Bo Tang, Q. Yin, X. Sun. Slim and Efficient Neural Network Design for Recourse-Constrained SAR . Remote Sensing, In Press, 2018. [Impact Factor: 4.118]
    18. X. Ning, W. Li, Bo Tang and H. He. BULDP: Biomimetic Uncorrelated Locality Discriminant Projection for Feature Extraction in Face Recognition. IEEE Trans. on Image Processing (TIP), In Press, 2018. [Impact Factor: 5.071]
    19. X. Hu, L. Chen, Bo Tang, Dongpu Cao, and H. He. Dynamic Path Planning for Autonomous Driving on Various Roads with Avoidance of Static and Moving Obstacles. In Mechanical Systems and Signal Processing, , vol 100, pp. 482-500, 2018. [Impact Factor: 5.005]
    20. Bo Tang and H. He. GIR: An Intra-class Coherence based Sampling Approach for Imbalanced Learning. Pattern Recognition, vol. 71, pp. 306-319, 2017. [Impact Factor: 5.898]
    21. Z. Wan, H. He, and Bo Tang. A Generative Model for Sparse Hyper-Parameter Determination. IEEE Trans. on Big Data, 2017, In Press.
    22. Bo Tang, Z. Chen, T.Wei, H. He and Q. Yang. Incorporating Intelligence in Fog Computing for Big Data Analysis in Smart Cities. IEEE Trans. on Industrial Informatics, 2017, In Press. [Impact Factor: 7.377]
    23. Bo Tang and H. He. A Local Density-Based Approach for Local Outlier Detection. Neurocomputing, vol. 241, pp. 171-180, 2017. [Impact Factor: 4.438]
    24. J. Xu, Bo Tang, H. He and H. Man. Semi-Supervised Feature Selection Based on Relevance and Redundancy Criteria. IEEE Trans. on Neural Networks and Learning Systems (TNNLS), 2016. In Press. [Impact Factor: 11.683]
    25. Bo Tang, C. Jiang, H. He and Y. Guo. Human Mobility Modeling for Robot-Assisted Evacuation in Complex Indoor Environments. IEEE Trans. on Human-Machine Systems (THMS), vol. 46, no. 5, pp. 694-707, 2016. [Impact Factor: 3.374]
    26. Bo Tang, H. He, P. M. Baggenstoss and S. Kay. A Bayesian Classification Approach Using Class-Specific Features for Text Categorization. IEEE Trans. on Knowledge and Data Engineering (TKDE), vol. 28, no. 6, pp. 1602-1606, 2016. [Impact Factor: 4.935]
    27. Bo Tang, S. Kay, H. He and P. M. Baggenstoss. EEF: Exponentially Embedded Families with Class-Specific Features for Classification. IEEE Signal Processing Letters, vol. 23, no. 7, pp. 969-973, 2016. [Impact Factor: 5.23]
    28. L. Shen, Bo Tang and H. He. An Imbalanced Learning based MDR-TB Early Warning System. Journal of Medical Systems, vol. 40, no. 7, pp. 164-173, 2016. [Impact Factor: 2.415]
    29. Bo Tang, S. Kay and H. He. Toward Optimal Feature Selection in Naive Bayes for Text Categorization. IEEE Trans. on Knowledge and Data Engineering (TKDE), vol. 28, no. 9, pp. 2508-2521, 2016. [Impact Factor: 4.935]
    30. S. Kay, Q. Ding, Bo Tang and H. He. Probability Density Function Estimation using the EEF with Application to Subset/Feature Selection. IEEE Trans. on Signal Processing (TSP), vol.64, no.3, pp.641-651, 2016. [Impact Factor: 5.23]
    31. Bo Tang, H. He, S. Kay and Q. Ding. A Parametric Classification Rule Based on the Exponentially Embedded Family. IEEE Trans. on Neural Networks and Learning Systems (TNNLS), vol.26, no.2, pp.367-377, 2015, (IEEE CIM Publication Spotlight Paper). [Impact Factor: 11.683]
    32. Bo Tang and H. He. ENN: Extended Nearest Neighbor Method for Multivariate Pattern Classification. IEEE Computational Intelligence Magazine (CIM), vol.10, no.3, pp.52-60, 2015, (Research Frontier Paper) [Impact Factor: 5.857]
    33. Y. Dong, L. Zhou, Bo Tang, X. Liang and C. Ding. Design of Real-Time Signal Processing Platform for Airborne SAR Imaging. Journal of Systems Engineering and Electronics, vol.31, no.8, pp.1882-1886, 2009.

    Conferences



    1. S. Xiang, Bo Tang, and Y. Fu. PMU-based Abrupt Change Detection for Power System Reliability and Security Enhancement. URI Cyber-Physical Systems Security Workshop, 2019.
    2. R. Rafi, Bo Tang, and S. Sharma. Multi-layer Embedding Neural Architecture with External Memory for Large-Scale Text Categorization. 2018 IEEE International Conference on Big Data, 2019.
    3. R. Rafi, Bo Tang, Q. Du, N. Younan. Attention-based Domain Adaptation for Hyperspectral Image Classification. International Geoscience and Remote Sensing Symposium, 2019.
    4. J. Ma, Bo Tang, S. Srinivasan. Anomaly Detection and Prediction for Multiple-Channel Multi-Time Series Data in Military Vehicle Reliability System. 57th Army Operations Research Symposium, 2019.
    5. John Ball, Bo Tang, et al. Real-time LiDAR 3D Object Detection in an Industrial Vehicle. SPIE Defense + Commercial Sensing, 2019.
    6. Charlie Veal, Bo Tang, et al. Linear Order Statistic Neuron. IEEE International Conference on Fuzzy Systems, 2019.
    7. Bo Tang and H. He. MILE: A Minimally Interactive Learning Framework for Visual Data Analysis. IEEE Annual Computing and Communication Workshop and Conference (CCWC), 2018.
    8. Q. Ding, Bo Tang, P. Manden, and J. Ren. A Learning-based Cost Management System for Cloud Computing. IEEE Annual Computing and Communication Workshop and Conference (CCWC), 2018.
    9. Bo Tang, P. M. Baggenstoss, and H. He. Kernel-based Bayesian Learning in Distortion Feature Space. IEEE Symposium Series on Computational Intelligence (SSCI), 2017.
    10. Bo Tang, J. Xu, H. He and H. Man. ADL: Active Dictionary Learning for Spare Representation. International Joint Conference on Neural Networks (IJCNN), 2017.
    11. Bo Tang, J. Yan, H. He and S. Kay. Detection of False Data Injection with Colored Gaussian Noise in Smart Grid. IEEE Conference on Communications and Network Security (IEEE CNS), 2016. [acceptance rate: 38/131 = 29%]
    12. Bo Tang and H. He. FSMJ: Feature Selection with Maximum Jensen-Shannon Divergence for Text Categorization. The 12th World Congress on Intelligent Control and Automation, 2016.
    13. Bo Tang and H. He. A Local Density Based Approach for Local Outlier Detection. International Joint Conference on Neural Networks (IJCNN), 2016.
    14. J. Yan, Bo Tang and H. He. Detection of False Data Attacks in Smart Grid with Supervised Learning. International Joint Conference on Neural Networks (IJCNN), 2016. (Best Paper Award)
    15. J. Yan, Y. Tang, Bo Tang, H. He, and Y. Sun. Power Grid Resilience Against False Data Injection. IEEE Power and Energy Society General Meeting, 2016.
    16. Bo Tang, Z. Chen, T. Wei, H. He and Q. Yang. A Hierarchical Distributed Fog Computing Architecture for Big Data Analysis in Smart Cities. The Fifth ASE International Conference on Big Data (BigData), Kaohsiung, Taiwan, Oct. 7-9, 2015. [acceptance rate: 13%]
    17. Bo Tang and H. He. KernelADASYN: Kernel Based Adaptive Synthetic Data Generation for Imbalanced Learning. IEEE Congress on Evolutionary Computation (CEC), Sendai, Japan, May 25-28, 2015.
    18. Bo Tang, S. Khokhar and R. Gupta. Turn Prediction at Generalized Intersections. IEEE Intelligent Vehicles Symposium, Seoul, Korea, June 28 - July 1, 2015. 
    19. J. Kane, Bo Tang, Z. Chen, J. Yan, T. Wei, H. He and Q. Yang. Reflex-Tree: A Biologically Inspired Architecture for Future Smart Cities. International Conference on Parallel Processing (ICPP), Beijing, China, Sept. 1 - 4, 2015. [acceptance rate: 99/305 = 32%
    20. J. Hua, S. Li and Bo Tang. An Information Recommendation Method based on User Interest Model. International Conference on Fuzzy System and Data Mining (FSDM), Shanghai, China, Dec. 12 - 15, 2015.
    21. Bo Tang, Q. Ding, H. He and S. Kay. Hybrid classification with partial models. International Joint Conference on Neural Networks (IJCNN), Beijing, China, July 6 - 11, 2014.
    22. Z. Ni, S. Fu, Bo Tang, H. He and X. Huang. Experimental studies on indoor sign recognition and classification. IEEE Symposium on Computational Intelligence and Data Mining (CIDM), Florida, Orlando, Dec. 9 - 12, 2014.
    23. Bo Tang, S. Fu, Y. Tang and H. He. Robust Multiple Objects Tracking: Particle Filter with ePSO. International Conference on Cognitive and Neural Systems (CCNS), Boston, MA, June 4 - 7, 2013.
    24. Y. Tang, S. Fu, Bo Tang and H. He. A Modified PSO Based Particle Filter Algorithm for Object Tracking. SPIE Defense, Security, and Sensing, Baltimore, Maryland, Apr. 29 - May 3, 2013.

    Source Code, Demo, and Lecture Notes


    ENN (Extended Neareast Neighbor) for pattern recognition. [Lecture Notes in PPT] [Lecture Notes in PDF] [Source Code Link]
    This link inlcudes the source code and demo for the ENN algorithm, associatd with the following paper:
    Bo Tang and H. He, "ENN: Extended Nearest Neighbor Method for Pattern Recognition," IEEE Computational Intelligence Magazine, vol.10, no.3, pp.52 - 60, Aug. 2015


    Relative Density-based Outlier Score (RDOS) method for outlier detection. [Source Code]
    This link inlcudes the source code and demo for the RDOS algorithm, as well as the implementation of several other outlier detection algorithms: INFLO, LDF, KDEOS, INDEGREE, and MUTUALNN, associatd with the following paper:
    Bo Tang and H. He, "A Local Density-based Approach for Outlier Detection," Neurocomputing 241 (2017): 171-180

    GROUP

    Bo Tang

    Principal Investigator


    Dr. Bo Tang

    Assistant Professor
    Department of Electrical and Computer Engineering
    Mississippi State University
    Mississippi State, MS 39762

    PH.D. STUDENTS


    1. Xingyu Li
      Current: ECE, Mississippi State University
      Master: Stevens Institute of Technology
      Bachelor: Xiamen University
    2. Suvash Sharma
      ECE, Mississippi State University
    3. Jaime Yeckle
      ECE, Mississippi State University
      ECE, Interamerica University of Puerto Rico
    4. David Beam
      ECE, Mississippi State University
    5. Mohammad Obiedat
      ECE, Mississippi State University

    MASTER STUDENTS


    1. Nicholoas Smith
      Current: ECE, Mississippi State University
      Bachelor: Mississippi State University -- BCoE Dean’s Office Undergraduate Research Award
    2. Mohammad Mahmudur Rahman Khan (Graduated)
      Thesis: Non-parametric Learning for Energy Disaggregation, Fall 2017.
    3. Yuanquan Chen (Graduated)
      Thesis: Abnormal Behavior Analysis Based on Sentiment Data, Spring 2017.
    4. Pooja Mehta (Graduated)
      Thesis: Unsupervised Learning for Intelligent Financial Data Analysis, Spring 2017.

    UNDERGRADUATE STUDENTS


    1. Jason Farmer -- BCoE Dean’s Office Undergraduate Research Award
      Current: ECE, Mississippi State University
    2. Keith Hunter
      Current: ECE, Mississippi State University
    3. Josh Hopkins
      Current: ECE, Mississippi State University
    4. Mark McDonnell
      Current: ECE, Mississippi State University



    Our Location


    Simrall Building, Room 236,

    Mississippi State, MS USA 39762.

    Call: +1 (662)-325-8757

    Email: tang@ece.msstate.edu

    MSU