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

ABOUT ME

Dr. Tang is currently an Associate Professor in the Department of Electrical and Computer Engineering at Worcester Polytechnic Institute (WPI). His research focuses on bio-inspired artificial intelligence (AI), AI security, edge AI, and their applications in Cyber-Physical Systems (e.g., wireless networks, autonomous vehicles (underwater/ground), and power systems). He received his Ph.D. degree from the University of Rhode Island (Kingstown, RI) under the supervision of Prof. Haibo He (IEEE Fellow) and Prof. Steven Kay (IEEE Life Fellow) in 2016.

Dr. Tang is a Senior Member of IEEE and an Associate Editor for IEEE Transactions on Neural Networks and Learning Systems. Dr. Tang is also the recipient of MSU Emerging Research Scholar Award (2022), NSF CAREER Award (2021), NIJ New Investigator/Early Career Award (2019), 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), and IEEE Computational Intelligence Magazine Publication Spotlight Paper (2015).

Multiple Ph.D. Assistantship Positions are Available:
Several new fully funded Ph.D. positions are available in the Department of Electrical and Computer Engineering at WPI. 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

  • Sept./2023: Our Suvery paper "How Simulation Helps Autonomous Driving" has been accepted by IEEE Trans. on Intelligent Vehicles.
  • July/2023: Our Cycle Memory Networks paper for lifelong learning has been accepted by IEEE Trans. on Neural Networks and Learning Systems (TNNLS).
  • June/2023: Our Deep Graph Neural Networks for Autonomous Driving paper has been accepted by IEEE Trans. on Intelligent Transportation Systems.
  • March/2021: Welcome Ava Stockton and Paul Ogunsemore joining our lab as High School Interns from Worcester Technical High School!
  • March/2023: Xingyu successfully defended his Ph.D. dissertation in the topic of Secure and Efficient Federated Learning on March 10, 2023. Congratulations, Xingyu!
  • February/2023: Our Federated Learning Towards System Heterogeneity paper has been accepted by IEEE Trans. on Cybernetics. Congratulations, Xingyu!
  • January/2023: Bo and his team started a new journey at WPI!
  • November/2022: Bo received the 2022 Emerging Research Scholar Award at MSU!
  • November/2022: Our AI Testing Framework for ORAN has been accepted by IEEE Wireless Communication Magazine!
  • October/2022: Two papers have been accepted by ICMLA 2022!
  • August/2022: Our website for NSF CISE Community Research Infrastructure (CCRI) Project: Open AI Cellular (OAIC) is now public!
  • August/2022: Our paper in Underwater Wireless Electromagnetic/Radio Communication has been accepted by the Oceans 2022. Check out the design of our first Underwater Robot: SubWave and its Creek Test in Youtube!
  • August/2022: Our Multi-Server Federated Learning paper has been accepted by IEEE Trans. on Mobile Computing. Congratulations, Xingyu!
  • May/2022: Our Generalized Federated Learning paper has been accepted by ICML 2022. Congratulations, Xingyu!
  • Mar/2022: We just published the first real-world and labeled Traversability Dataset for Off-Road Autonomous Driving. Excellent Work, Team!
  • Mar/2022: Jason presented his AI-enabled Underwater Robot Localization work in the OCEAN SCIENCES Meeting 2022! Check out the design of our first Underwater Robot: SubWave and its Creek Test in Youtube!
  • Mar/2022: Suvash successfully defended his Ph.D. dissertation in the topic of Deep Learning for Autonomous Driving on March 04, 2022. Congratulations, Suvash!
  • Jan/2022: Bo was appointed as the Associated Editor of IEEE Transactions on Neural Networks and Learning Systems (TNNLS).
  • Jan/2022: Bo received a FAA Grant ($1.9M in total, PI: Shawn McNutt in RFRL) focusing on saftey and security of unmanned aircraft systems (UAS). Thanks, FAA!
  • Dec/2021: Our Secure Federated Learning paper has been accepted in IEEE Trans. on Dependable and Secure Computing (TDSC).
  • August/2021: Bo received the prestigious NSF CAREER Award to support our biologically-inspired lifelong learning research! Thanks, NSF!
  • July/2021: Welcome our first two K12 summer interns, Andrew and Allen, from Mississippi School for Mathematics and Science.
  • July/2021: Bo received a NSF CCRI Grant ($1.8M in total, Co-PI, in collaboration with Virginia Tech) to support our AI-Enabled Wireless Communication System and AI Security research. Thanks, NSF!
  • June/2021: Our Negative Obstacle Estimation for Autonomous Navigation paper has been accepted in IROS 2021.
  • 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).
  • Oct/2020: Our AI-enabled Power System Analysis paper has been accepted in IEEE Trans. on Neural Networks and Learning Systems (TNNLS).
  • 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 a NSF CCRI Planning Grant (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!
  • Jan/2019: 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. X. Li, Bo Tang. AdaER: An Adaptive Experience Replay Approach for Continual Lifelong Learning.  IEEE Trans. on Neural Networks and Learning Systems (TNNLS), Submitted, 2023.
    2. S. Sharma, Bo Tang. Local and Non-local Cross-Modal Attention-based Fusion for Semantic Road-Segmentation with Cycle Spinning.  IEEE Trans. on Cybernetics, Submitted, 2023.
    3. J. Peng, X. Sun, M. Deng, C. Tao, Bo Tang, et al. Learning by Active Forgetting for Neural Networks.  Nature Machine Intelligence, Submitted, 2023.
    4. X. Hu, S. Li, T. Huang, Bo Tang, etc. How Simulation Helps Autonomous Driving: A Survey of Sim2real, Digital Twins, and Parallel Intelligence.  IEEE Transactions on Intelligent Vehicles, Accepted, 2023. [Impact Factor: 11.8]
    5. J. Peng, D. Ye, Bo Tang, W. Li, Y. Lei, H. Li. Overcome Anterograde Forgetting with Cycle Memory Networks.  IEEE Trans. on Neural Networks and Learning Systems, Accepted, 2023. [Impact Factor:

      ]

    6. X. Hu, Y. Liu, Bo Tang, J. Yan, and L. Chen. Learning Dynamic Graph for Overtaking Strategy in Autonomous Driving.  IEEE Trans. on Intelligent Transportation Systems, In Press, 2023. [Impact Factor:

      ]

    7. X. Li, Q. Zhe, Bo Tang, FedLGA: Towards System-Heterogeneity of Federated Learning via Local Gradient Approximation.  IEEE Trans. on Cybernetics, In Press, 2023. [Impact Factor:

      ]
    8. Bo Tang, V. Shah, V. Marojevic, and J. Reed. AI Testing Framework for Next-G O-RAN Networks: Requirements, Design, and Research Opportunities.  IEEE Wireless Communication Magazine, In Press, 2022. [Impact Factor:

      ]
    9. Q. Zhe, X. Li, J. Xu, Bo Tang, Z. Lu. On the Convergence of Multi-Server Federated Learning with Overlapping Area.  IEEE Trans. on Mobile Computing (TMC), In Press, 2022.
    10. X. Li, Q. Zhe, Bo Tang, and Z. Lu. LoMar: A Local Defense Against Poisoning Attack on Federated Learning.  IEEE Trans. on Dependable and Secure Computing (TDSC), In Press, 2021. [Impact Factor: 7.329]
    11. 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]
    12. 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:

      ]

    13. Q. Gao, Z. Yang, J. Yu, W. Dai, X. Lei, Bo Tang, K. Xie, and W. Li. Model-Driven Architecture of Extreme Learning Machine to Extract Power Flow Features.  IEEE Trans. on Neural Networks and Learning Systems, 32, no. 10 (2020): 4680-4690. [Impact Factor:

      ]

    14. 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:

      ]

    15. 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:

      ]

    16. 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:

      ]

    17. 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:

      ]

    18. S. Xiang and Bo Tang. Kernel-based Edge-Preserving Methods for Abrupt Change Detection.  IEEE Signal Processing Letters, In Press 2019. [Impact Factor:

      ]

    19. X. Hu, Bo Tang, S. Song, and X. Tong. Learning A Deep Cascaded Neural Network for Multiple Motion Commands Prediction in Autonomous Driving.  Mechanical Systems and Signal Processing, In Submission 2019. [Impact Factor:

      ]

    20. 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, 57(12), pp.10070-10087, 2019. [Impact Factor:

      ]

    21. 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:

      ]

    22. J. Ball and Bo Tang. Machine Learning and Embedded Computing in Advanced Driver Assistance Systems (ADAS). Electronics, Editoral Paper, In Press 2019.
    23. 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:

      ]

    24. X. Li, Bo Tang, J. Ball, M. Doude, D. Carruth. Rollover-Free Path Planning for Off-Road Autonomous Driving. Electronics, In Press 2019.
    25. 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.
    26. 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: 5.293]
    27. 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.
    28. 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:

      ]
    29. 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:

      ]
    30. 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:

      ]
    31. Z. Wan, H. He, and Bo Tang. A Generative Model for Sparse Hyper-Parameter Determination. IEEE Trans. on Big Data, 2017, In Press.
    32. 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:

      ]
    33. Bo Tang and H. He. A Local Density-Based Approach for Local Outlier Detection. Neurocomputing, vol. 241, pp. 171-180, 2017.
    34. 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:

      ]
    35. 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:

      ]
    36. 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:

      ]
    37. 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]
    38. 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.
    39. 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:

      ]

    40. 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:

      ]
    41. 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:

      ]
    42. 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:

      ]
    43. 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. Q. Zhe, X. Li, R. Duan, Y. Liu, Z. Lu, Bo Tang. Generalized Federated Learning via Sharpness Aware Minimization.. International Conference on Machine Learning (ICML), 2022.
    2. V. Lebakula, Bo Tang, C. Goodin, and C. Bethel. Shape Estimation of Negative Obstacles for Autonomous Navigation. International Conference on Intelligent Robots and Systems (IROS), 2021.
    3. S. Smith, Bo Tang, D. John, and M. Young. Classifying WiFi "physical fingerprints" using complex deep learning. Automatic Target Recognition XXX, SPIE, 2020.
    4. W. Xie, Bo Tang, and M. He. Data-Enabled Correlation Analysis between Wildfire and Climate using GIS. International Conference on Information and Computer Technologies, 2020.
    5. S. Smith, Bo Tang, D. John, and M. Young. Identifying unlabeled WiFi devices with zero-shot learning. Automatic Target Recognition XXX, SPIE, 2020.
    6. S. Sharma, C. Carruth, W. Doude, Bo Tang, D. John, and C. Goodin. Performance analysis of semantic segmentation algorithms trained with JPEG compressed datasets. Real-Time Image Processing and Deep Learning, SPIE, 2020.
    7. D. Rayborn, S. Smith, Bo Tang, D. John, and M. Young. Towards simulating multipath interference at detectors: a tool for validating location fingerprinting methods. Signal Processing, Sensor/Information Fusion, and Target Recognition XXIX, SPIE, 2020.
    8. S. Sharma, C. Carruth, W. Doude, Bo Tang, D. John, and C. Goodin. Understanding How Rain Affects Semantic Segmentation Algorithm Performance. SAE International, 2020.
    9. C. Goodin, S. Sharma, C. Carruth, W. Doude, Bo Tang, D. John. Automated Scene Generation and Data Labeling for Training Classification Algorithms. SAE International, 2020.
    10. 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.
    11. 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.
    12. R. Rafi, Bo Tang, Q. Du, N. Younan. Attention-based Domain Adaptation for Hyperspectral Image Classification. International Geoscience and Remote Sensing Symposium, 2019.
    13. 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.
    14. John Ball, Bo Tang, et al. Real-time LiDAR 3D Object Detection in an Industrial Vehicle. SPIE Defense + Commercial Sensing, 2019.
    15. Charlie Veal, Bo Tang, et al. Linear Order Statistic Neuron. IEEE International Conference on Fuzzy Systems, 2019.
    16. 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.
    17. 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.
    18. Bo Tang, P. M. Baggenstoss, and H. He. Kernel-based Bayesian Learning in Distortion Feature Space. IEEE Symposium Series on Computational Intelligence (SSCI), 2017.
    19. Bo Tang, J. Xu, H. He and H. Man. ADL: Active Dictionary Learning for Spare Representation. International Joint Conference on Neural Networks (IJCNN), 2017.
    20. 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%]
    21. 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.
    22. Bo Tang and H. He. A Local Density Based Approach for Local Outlier Detection. International Joint Conference on Neural Networks (IJCNN), 2016.
    23. 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)
    24. 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.
    25. 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%]
    26. 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.
    27. Bo Tang, S. Khokhar and R. Gupta. Turn Prediction at Generalized Intersections. IEEE Intelligent Vehicles Symposium, Seoul, Korea, June 28 - July 1, 2015. 
    28. 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%
    29. 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.
    30. 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.
    31. 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.
    32. 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.
    33. 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.

    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
      Graduated: 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. Jason Farmer
      Current: ECE, Mississippi State University.
      Bachelor: Mississippi State University -- BCoE Dean’s Office Undergraduate Research Award
    2. Nicholoas Smith (Graduated)
      Thesis: Radio Frequency Dataset Collection System Development for Location and Device Fingerprinting, 2021.
      Bachelor: Mississippi State University -- BCoE Dean’s Office Undergraduate Research Award
    3. Mohammad Mahmudur Rahman Khan (Graduated)
      Thesis: Non-parametric Learning for Energy Disaggregation, 2017.
    4. Yuanquan Chen (Graduated)
      Thesis: Abnormal Behavior Analysis Based on Sentiment Data, 2017.
    5. Pooja Mehta (Graduated)
      Thesis: Unsupervised Learning for Intelligent Financial Data Analysis, 2017.

    UNDERGRADUATE STUDENTS


    1. Keith Hunter
      Current: ECE, Mississippi State University
    2. Josh Hopkins
      Current: ECE, Mississippi State University
    3. Mark McDonnell
      Current: ECE, Mississippi State University



    Our Location


    AK305

    WPI, Worcester, MA.

    Email: btang1@wpi.edu

    WPI