Hong Man (hman)

Hong Man

Professor

Charles V. Schaefer, Jr. School of Engineering and Science

Department of Electrical and Computer Engineering

Burchard 201
(201) 216-5038
(201) 216-8246

Education

  • PhD (1999) Georgia Institute of Technology (Electrical Engineering)

Research

Computer perception: texture and edge features, feature selection, semantic segmentation
Visual understanding: object detection, localization, recognition and tracking, action recognition, face and gesture recognition, emotion and sentiment analysis
Joint textual and visual analysis: image to phrases, textual features, sentiment analysis in social media
Medical imaging: robust segmentation, fMRI brain activation detection, EEG and EKG time series analysis, tomographic imaging

General Information

Hong Man joined Stevens Institute of Technology in 2000, after he obtained his Ph.D. degree in Electrical Engineering from Georgia Tech. He is currently a Professor in the Electrical and Computer Engineering Department. His research interests include machine learning, multimodal sensing, image processing, medical imaging, and data networking, on which he has authored or coauthored over 170 technical journal and conference papers. His research works have been sponsored by NSF, DARPA, Microsoft, Lucent and other organizations. He has graduated more than 20 Ph.D. students.

Institutional Service

  • Academic Operations and Affairs Committee Member
  • ECE Faculty Council Chair
  • ECE Student Well-being Comittee Member
  • ECE Undergraduate Committee Member
  • Institute Curriculum Committee Chair
  • Teahing Professor Search Committee Chair
  • Faculty Senate Member
  • SES ABET preparation working group Member
  • SES ABET preparation working group Member
  • ECE Student Wellbeing Committee Member
  • BOT Nominating and Corporate Governance Committee Member
  • ECE Graduate Program Committee Member
  • ECE Undergraduate Program Committee Chair
  • Faculty Senate Member
  • SES Undergraduate Curriculum Committee Member
  • Lecturer recruitment committee Chair
  • BOT Nominating and Corporate Governance Committee Member
  • ECE Graduate Program Committee Member
  • ECE Undergraduate Program Committee Chair
  • Faculty Senate Member
  • SES ABET preparation working group Member
  • SES Undergraduate Curriculum Committee Member
  • SES Undergraduate Curriculum Revision Working group Member
  • CpE Undergraduate Program Committee Chair
  • ECE Graduate Program Committee Member
  • SES ABET preparation working group Member
  • SES Undergraduate Curriculum Committee Member
  • SES Undergraduate Curriculum Revision Working group Member
  • ECE Faculty Search Committee Chair

Professional Service

  • Journal of Multimedia Tools and Applications (Springer) Section Editor
  • International Conference on Pattern Recognition (ICPR) 2024 Meta-Reviewer
  • Journal of Multimedia Tools and Applications (Springer) Member of the Editorial Board
  • NSF NSF Panelist
  • NSF NSF Panelist
  • NSF NSF Panelist
  • IEEE North Jersey Advanced Communications Symposium • Member of organizing committee

Professional Societies

  • ACM Member
  • ASEE Member
  • IEEE Senior member

Selected Publications

Book Chapter

  1. Dai, S.; Su, P.; Man, H. (2021). Deep Metric Similarity Clustering. Transactions on Computational Science and Computational Intelligence (pp. 43-64). Springer International Publishing.
    https://doi.org/10.1007/978-3-030-71704-9_3.

Conference Proceeding

  1. Morcos, A.; Man, H.; Maguire, B.; West, A. (2023). Classification without gradients: multi-agent reinforcement learning approach to optimization. Proc. SPIE 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V. SPIE.
    https://doi.org/10.1117/12.2664025..
  2. Morcos, A.; Man, H.; West, A.; Maguire, B. (2022). Learning without Gradients: Multi-Agent Reinforcement Learning approach to optimization. Proc. SPIE 12276, Artificial Intelligence and Machine Learning in Defense Applications IV. SPIE.
    https://doi.org/10.1117/12.2636231.
  3. Dai, S.; Man, H. (2022). Deep Latent Similarity Model for Online Data Categorization. Proc. the 18th Int. Conference on Data Science . Springer.
  4. Man, H.; Dai, S.; Lawtence, V.; LaPeruta, T.; Hohil, M. (2021). Unsupervised Multi-view Object Proposal Ranking. Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III. SPIE Defense + Commercial Sensing (vol. 11746). Virtual: SPIE.
    http://dx.doi.org/10.1117/12.2587810.
  5. Dai, S.; Su, P.; Man, H. (2020). Deep Similarity Metric Clustering. The 16th Int. Conference on Data Science (DATA’20). Las Vegas, NV: Springer.
  6. Dai, S.; Man, H. (2019). Unsupervised Object Discovery and Inference with Deep Latent Similarity Model. Las Vegas, NV: The 23rd Int'l Conf on Image Processing, Computer Vision, & Pattern Recognition (IPCV’19), .
  7. Dai, S.; Man, H. (2018). Integrating Visual and Textual Affective Descriptors for Sentiment Analysis of Social Media Posts. 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) (pp. 13--18).
  8. Dai, S.; Su, P.; Man, H. (2018). Object discovery and localization via structural contrast. 2018 25th IEEE International Conference on Image Processing (ICIP) (pp. 2760--2764).
  9. Dai, S.; Su, P.; Man, H. (2018). Unsupervised Object Discovery And Localization Via Structural Discrimination. Athens, Greece: IEEE International Conference on Image Processing (ICIP 2018), .
  10. Dai, S.; Man, H. (2017). A convolutional Riemannian texture model with differential entropic active contours for unsupervised pest detection. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1028--1032).
  11. Dai, S.; Man, H. (2017). A statistic manifold kernel with graph embedding discriminant analysis for action and expression recognition. 2017 IEEE International Conference on Image Processing (ICIP) (pp. 1792--1796).
  12. Tang, B.; Xu, J.; He, H.; Man, H. (2017). ADL: Active dictionary learning for sparse representation. 2017 International Joint Conference on Neural Networks (IJCNN) (pp. 2723--2729).
  13. Dai, S.; Man, H.; Zhan, S. (2016). A Bregman divergence based Level Set Evolution for efficient medical image segmentation. 2016 23rd International Conference on Pattern Recognition (ICPR) (pp. 1113--1118).
  14. Dai, S.; Xu, X.; Jiang, B.; Man, H. (2016). Multi-scale sentiment classification using canonical correlation analysis on riemannian manifolds. 2016 IEEE International Symposium on Multimedia (ISM) (pp. 144--147).
  15. Xu, X.; Man, H. (2015). Interpreting sports tactic based on latent context-free grammar. 2015 IEEE International Conference on Image Processing (ICIP) (pp. 4072--4076).
  16. Dai, S.; Man, H.; Zhan, S. (2015). Minimum mutual information based level set clustering algorithm for fast mri tissue segmentation. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 3057--3060).
  17. Dai, S.; Man, H. (2015). Statistical Adaptive Metric Learning for Action Feature Set Recognition in the Wild. International Symposium on Visual Computing (pp. 657--667).

Journal Article

  1. Zeng, Y.; Wang, Y.; Liao, D.; Li, G.; Xu, J.; Xu, X.; Liu, B.; Man, H. (2023). Contrastive Topic-Enhanced Network for Video Captioning. Expert Systems With Applications. Elsevier.
  2. Tang, X.; Liao, D.; Zhu, L.; Shen, M.; Huang, S.; Li, G.; Man, H.; Xu, J. (2023). Confidence-aware Sentiment Quantification via Sentiment Perturbation Modeling. IEEE Transactions on Affective Computing. IEEE.
  3. Zeng, Y.; Wang, Y.; Liao, D.; Li, G.; Huang, W.; Xu, J.; Cao, D.; Man, H. (2022). Keyword-Based Diverse Image Retrieval with Variational Multiple Instance Graph. IEEE Transactions on Neural Networks and Learning Systems (doi: 10.1109/TNNLS.2022.3168431 ed.).
  4. Dai, S.; Man, H. (2017). Mixture statistic metric learning for robust human action and expression recognition. IEEE Transactions on Circuits and Systems for Video Technology (10 ed., vol. 28, pp. 2484--2499). IEEE.
  5. Cui, C.; Man, H.; Wang, Y.; Liu, S. (2016). Optimal cooperative spectrum aware opportunistic routing in cognitive radio ad hoc networks. Wireless Personal Communications (1 ed., vol. 91, pp. 101--118). Springer US.
  6. Xu, J.; Tang, B.; He, H.; Man, H. (2016). Semisupervised feature selection based on relevance and redundancy criteria. IEEE transactions on neural networks and learning systems (9 ed., vol. 28, pp. 1974--1984). IEEE.
  7. Dai, S.; Man, H. (2016). Statistical adaptive metric learning in visual action feature set recognition. Image and Vision Computing (vol. 55, pp. 138--148). Elsevier.