Jianhua Chen

Research Interests

• Artificial Intelligence, Machine Learning and Data Mining, Data Clustering
• Applications of Machine Learning in Cognitive Science
  
• Machine Learning Applications in Engineering, Security, and Environment Science
• Web/text Mining, Natural Language Processing, Ontology Learning
  
• Fuzzy Logic and Fuzzy Systems
• Intelligent Information Retrieval
• Knowledge Representation, Logic in AI, Non-monotonic reasoning

Recent NSF Grant

From August 2003 to January 2010, Professor Chen was the Co-Principal Investigator of a large NSF-funded project on the profiling of terrorists and malicioius cyber transactions for counter terrorism and crimes. For more detailed information about this project and its publications, please browse the web page  www.csc.lsu.edu/~chen/NSF-Project.htm

Courses Taught

• CSC3102 (Advanced Data Structures and Algorithm Analysis)          
• CSC4402 (Introduction to Database Systems)                                      
• CSC4444 (Artificial Intelligence)                                                            
• CSC7333 (Machine Learning)                                                                
• CSC7442 (Data Mining and Knowledge Discovery from Databases)   
• CSc7444 (Advanced Artificial Intelligence)                                           
• CSC7700 (Special Topics in Computer Science)

Selected Journal Publications

• Guoji Xu, Qin Chen, Jianhua Chen. Prediction of Solitary Wave Forces on Coastal Bridge Decks Using Artificial Neural Networks, Journal of Bridge Engineering. 23(5), (2018)
• David Sithiaraj. Xinbo Huang, Jianhua Chen.  Predicting climate types for the Continental United States using unsupervised clustering techniques.  Environmetrics. 2019;30:e2524
  
• Jianhua Chen.  Properties of a new adaptive sampling method with applications to scalable learning. Web Intelligence 13(4): 215-227 (2015)
• Ömer M. Soysal, Jianhua Chen.  Object recognition by spectral feature derived from canonical shape representation. Machine Vision and Applications 24(4): 855-868 (2013)
• Ömer M. Soysal, Jianhua Chen, Helmut Schneider.  Efficient photometric feature extraction in a hierarchical learning scheme for nodule detection. IJGCRSIS 2(4): 314-326 (2012)
• Janardhana Punuru, Jianhua Chen.  Learning non-taxonomical semantic relations from domain texts. Journal of Intelligent Information Systems 38(1): 191-207 (2012)
• Guoli Ding, Robert F. Lax, Jianhua Chen, Peter P. Chen, Brian D. Marx. Transforms of pseudo-Boolean random variables. Discrete Applied Mathematics 158(1): 13-24 (2010)
  
• Patrick McDowell, Brian Bourgeois, Pamela J. McDowell, S.S. Iyengar and Jianhua Chen.  Relative Positioning for Team Robot Navigation. Autonomous Robots 22(2): 133-148 (2007)
  
• S. Seiden, P. Chen, R. Lax, J. Chen, G. Ding. New Bounds on Randomized Busing. Theoretical Computer Science,  332 (2005), 63-81.
• D. Kraft, M.J. Martin-Bautista, J. Chen, and D. Sanchez,  Rules and Fuzzy Rules in Text: Concept, Extraction and Usage. Special Issue of International Journal of Approximate Reasoning, August 2003.
  
• S. N. Sanchez, E. Triantaphyllou, J. Chen, W. Liao, An incremental learning algorithm for constructing Boolean functions from positive and negative examples,  Journal of Computers and Operations Research, 29(2002), pp. 1677-1700.
  
• J. Chen, The Generalized Logic of Only Knowing That Covers the Notion of Epistemic Specifications, Journal of Logic and Computation, Vol. 7, No. 2, 1997, pp. 159-174.
• S. Kundu, J. Chen, Fuzzy Logic or Lukasiewicz Logic: A Clarification, Fuzzy Sets and Systems Vol. 95, 1998, pp. 369-379.
• R.R. Brooks, S.S. Iyengar, J. Chen, Automatic Correlation and Calibration of Noisy Sensor Readings using Elite Genetic Algorithms, Artificial Intelligence, 84(1-2), July 1996, pp. 339-354.
• J. Chen, Relating Only Knowing to Minimal Knowledge and Negation as Failure, Journal of Experimental and Theoretical Artificial Intelligence, 6(1994), pp. 409-429.
• W. Wang, J. Chen, Learning by Discovering Problem Solving Heuristics through Experience, IEEE Transactions on Knowledge and Data Engineering, (4) Vol 3, 1991, pp. 415-420.

Selected Conference Publications

• Marzieh Mousavian, Jianhua Chen, Steven G. Greening.  Feature Selection and Imbalanced Data Handling for Depression Detection. Brain Informatics. 2018: 349-358
• Guoji Xu, Jianhua Chen, Qin Chen. Application of artificial neural networks to wave load prediction for coastal bridges. ICCIP 2017: 526-531
  
• Qian Wang, Jianhua Chen, Kelin Hu. Storm Surge Prediction for Louisiana Coast Using Artificial Neural Networks. ICONIP (3) 2016: 396-405
  
• Jianhua Chen. Boosting with Adaptive Sampling for Multi-class Classification. ICMLA 2015: 667-672
  
• Robert Firth, Jianhua Chen. Neural Network Implementation of a Mesoscale Meteorological Model.  Proceedings of International Symposium on Methodologies for Intelligent Systems (ISMIS) 2014: 164-173
• Jianhua Chen, Jian Xu. Sampling  Adaptively Using the Massart Inequality for Scalable Learning. ICMLA (2) 2013: 362-367
  
• Jianhua Chen.  Scalable Ensemble Learning by Adaptive Sampling. ICMLA (1) 2012: 622-625
  
• Janardhana Punuru, Jianhua Chen. Discovering Semantic Relations Using Prepositional Phrases. ISMIS 2012: 149-154
  
• Jianhua Chen, Xinjia Chen. A New Method for Adaptive Sequential Sampling for Learning and Parameter Estimation. ISMIS 2011: 220-229
• Jian Xu, Jianhua Chen, Bin Li.  Random forest for relational classification with application to terrorist profiling.  IEEE Int. Conf. on Granular Computing. 2009: 630-633
  
• J.R. Punuru and J. Chen. Automatic Acquisition of Concepts from Domain Texts. Proceedings of the IEEE Int. Conf. on Granular Computing. Atlanta, GA, May 2006.
• R. Lax, G. Ding, P. Chen and J. Chen.  Approximating Pseudo-Boolean Functions in Non-Uniform Domains.
  Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), Scotland, August 2005.
• G. Ding, J. Chen, R. Lax, P. Chen. Efficient Learning of Pseudo-Boolean Functions from Limited Training Data.
  Lecture Notes in Computer Science, Vol. 3488(2005), Proceedings of International Symposium on Methodologies for Intelligent Systems, Saratoga Springs, NY, May 2005. pp. 323-331.
• L. Moscovich and J. Chen. Learning Hidden Markov Models using the State distribution Oracle.
  Proceedings of Int. Conference on Machine Learning and Applications (ICMLA), Louisville, KY, Dec. 2004.
• J. Chen, J. Chen and G.P. Kemp, Fuzzy Clustering and Decision Tree Learning for Time-series Tidal Data
  Classification.  Proceedings of FUZZ-IEEE2003, May 2003, St. Louis.