University of South Carolina Department of Computer Science and Engineering College of Engineering & Computing Machine Learning and Evolution Group (MLEG)

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Jianjun Hu CV Professor Department of Computer Science and Engineering University of South Carolina Columbia, SC 29208 Office: 2223 Storey Innovation Center Phone: 803-777-7304      Fax: 803-777-3767  Email: jianjunh_AT_cse.sc.edu Office hours:TBD or by appointment via Email.

I am looking for highly motivated Ph.D. and Master students with RA/TA assistantship and outstanding undergraduate students to join my lab and work on projects in Deep Learning, material informatics, bioinformatics,data mining or machine learning. You are expected to have good programming skills and teamwork spirit.

Teaching Fall 2022 Data Mining and Warehousing (CSCE822) TTH 10:05PM- 11:30PM, SWGN 2A11

Research Interests My research interests are in the areas of machine learning, deep learning, data mining, big data, evolutionary computation and their applications in bioinformatics, material informatics, and health informatics. Our research has been sponsored by NSF, NIH, Nvidia, and the South Carolina Department of Transportation. Currently, our major research focus is developing deep learning algotrithms for solving challenging application problems such as:  Intelligent audio/sound processing Data-driven material discovery Disease diagnosis and prediction, medical image analysis Bioinformatics: protein-peptide binding prediction for drug design, and protein design Big data driven predictive analytics for healthcare Fault diagnosis, text mining, and intelligent transportation   For more information, pls visit my lab's homepage here: Machine Learning and Evolution Group (MLEG)

Bio: I joined CSE department of the University of South Carolina in August 2007. I am now working on deep learning, data mining and their applications in audio processing, material informatics, bioinformatics, and health informatics.  I got my Ph.D. in Computer Science in the area of machine learning and particularly evolutionary computation at the Genetic Algorithm Research and Application Group (GARAGe) of Michigan State University. My dissertation focuses on sustainable evolutionary computation algorithms and automated computational synthesis. I have worked as Postdoc at  Purdue University Computational Molecular Biology Division at the University of Southern California.

Education

Postdoc	University of Southern California, Computational Molecular Biology	09/05 - 06/07
Postdoc	Purdue University, Biological Science	09/04 - 08/05
Ph.D.	Michigan State University, Computer Science	08/00 - 07/04
M.S.	Wuhan University of Technology, China, Mechanical Engineering	07/95 - 04/98
B.S.	Wuhan University of Technology, China, Mechanical Engineering	09/91 - 07/95

Selected Publications [Complete List] [Check Google Scholar for latest publications]

Dissertation

J. Hu. Sustainable Evolutionary Algorithms and Scalable Evolutionary Synthesis of Dynamic Systems”, PhD thesis, Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, 48823, USA, 2004. Advisor: Erik Goodman

Selected Journal Publications

• Generative design of stable semiconductor materials using deep learning and dft
  ,npj Computational Materials, 2022.
  Siriwardane, Edirisuriya, Zhao, Yong, Perera, Indika and Hu, Jianjun


• Accurate prediction of voltage of battery electrode materials using attention based graph neural networks
  , 2022.
  Louis, Steph-Yves, Siriwardane, Edirisuriya, Joshi, Rajendra, Omee, Sadman, Kumar, Neeraj and Hu, Jianjun


• Studying patterns and predictors of hiv viral suppression using a big data approach: a research protocol
  BMC Infectious Diseases 22, 1-12, 2022.
  Zhang, Jiajia, Olatosi, Bankole, Yang, Xueying, Weissman, Sharon, Li, Zhenlong, Hu, Jianjun and Li, Xiaoming


• Large scale dataset of real space electronic charge density of cubic inorganic materials from density functional theory (dft) calculations
  Scientific Data 9, 1-9, 2022.
  Wang, Fancy Qian, Choudhary, Kamal, Liu, Yu, Hu, Jianjun and Hu, Ming


• Crystal structure prediction using an age-fitness multiobjective genetic algorithm and coordination number constraints
  The Journal of Physical Chemistry A, 2022.
  Yang, Wenhui, Dilanga Siriwardane, Edirisuriya M and Hu, Jianjun


• Deepxrd, a deep learning model for predicting of xrd spectrum from materials composition
  arXiv preprint arXiv:2203.14326, 2022.
  Dong, Rongzhi, Zhao, Yong, Song, Yuqi, Fu, Nihang, Omee, Sadman Sadeed, Dey, Sourin, Li, Qinyang, Wei, Lai and Hu, Jianjun


• Physics guided generative adversarial networks for generations of crystal materials with symmetry constraints
  arXiv preprint arXiv:2203.14352, 2022.
  Zhao, Yong, Siriwardane, Edirisuriya M Dilanga, Wu, Zhenyao, Hu, Ming, Fu, Nihang and Hu, Jianjun


• Genetic programming-based learning of carbon interatomic potential for materials discovery
  arXiv preprint arXiv:2204.00735, 2022.
  Eldridge, Andrew, Rodriguez, Alejandro, Hu, Ming and Hu, Jianjun


• Materialsatlas. org: a materials informatics web app platform for materials discovery and survey of state-of-the-art
  npj Computational Materials 8, 1-12, 2022.
  Hu, Jianjun, Stefanov, Stanislav, Song, Yuqi, Omee, Sadman Sadeed, Louis, Steph-Yves, Siriwardane, Edirisuriya, Zhao, Yong and Wei, Lai


• Tcsp: a template-based crystal structure prediction algorithm for materials discovery
  Inorganic Chemistry, 2022.
  Wei, Lai, Fu, Nihang, Siriwardane, Edirisuriya MD, Yang, Wenhui, Omee, Sadman Sadeed, Dong, Rongzhi, Xin, Rui and Hu, Jianjun


• Crystal transformer: self-learning neural language model for generative and tinkering design of materials
  arXiv preprint arXiv:2204.11953, 2022.
  Wei, Lai, Li, Qinyang, Song, Yuqi, Stefanov, Stanislav, Siriwardane, Edirisuriya, Chen, Fanglin and Hu, Jianjun


• Scalable deeper graph neural networks for high-performance materials property prediction
  Patterns, 100491, 2022.
  Omee, Sadman Sadeed, Louis, Steph-Yves, Fu, Nihang, Wei, Lai, Dey, Sourin, Dong, Rongzhi, Li, Qinyang and Hu, Jianjun

2021

• Alphacrystal: contact map based crystal structure prediction using deep learning
  arXiv preprint arXiv:2102.01620, 2021.
  Hu, Jianjun, Zhao, Yong, Yang, Wenhui, Song, Yuqi, Siriwardane, Edirisuriya, Li, Yuxin and Dong, Rongzhi


• Node-select: a graph neural network based on a selective propagation technique
  arXiv preprint arXiv:2102.08588, 2021.
  Louis, Steph-Yves, Nasiri, Alireza, Rolland, Fatima J, Mitro, Cameron and Hu, Jianjun


• Deep learning pan-specific model for interpretable mhc-i peptide binding prediction with improved attention mechanism
  Proteins: Structure, Function, and Bioinformatics, 2021.
  Jin, Jing, Liu, Zhonghao, Nasiri, Alireza, Cui, Yuxin, Louis, Stephen, Zhang, Ansi, Zhao, Yong and Hu, Jianjun


• Soundclr: contrastive learning of representations for improved environmental sound classification
  arXiv preprint arXiv:2103.01929, 2021.
  Nasiri, Alireza and Hu, Jianjun


• Meta-learning for few-shot bearing fault diagnosis under complex working conditions
  Neurocomputing 439, 197-211, 2021.
  Li, Chuanjiang, Li, Shaobo, Zhang, Ansi, He, Qiang, Liao, Zihao and Hu, Jianjun


• Real-time motion tracking of cognitive baxter robot based on differential inverse kinematics
  International Journal of Advanced Robotic Systems 18, 17298814211024052, 2021.
  Li, Shaobo, Zhang, Xingxing, Yang, Jing, Bai, Qiang, Hu, Jianjun, Song, Qisong and Li, Zhiang


• Contact map based crystal structure prediction using global optimization
  CrystEngComm 23, 1765-1776, 2021.
  Hu, Jianjun, Yang, Wenhui, Dong, Rongzhi, Li, Yuxin, Li, Xiang, Li, Shaobo and Siriwardane, Edirisuriya MD


• Mlatticeabc: generic lattice constant prediction of crystal materials using machine learning
  ACS omega 6, 11585-11594, 2021.
  Li, Yuxin, Yang, Wenhui, Dong, Rongzhi and Hu, Jianjun


• Deepseqpanii: an interpretable recurrent neural network model with attention mechanism for peptide-hla class ii binding prediction
  IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021.
  Liu, Zhonghao, Jin, Jing, Cui, Yuxin, Xiong, Zheng, Nasiri, Alireza, Zhao, Yong and Hu, Jianjun


• Computational discovery of new 2d materials using deep learning generative models
  ACS Applied Materials \& Interfaces, 2021.
  Song, Yuqi, Siriwardane, Edirisuriya M Dilanga, Zhao, Yong and Hu, Jianjun


• First-principles investigation of ti2cso and ti2csse janus mxene structures for li and mg electrodes
  The Journal of Physical Chemistry C, 2021.
  Siriwardane, Edirisuriya M Dilanga and Hu, Jianjun


• First-principles investigation of ti₂cso and ti₂csse janus mxene structures for li and mg electrodes
  , 2021.
  Siriwardane, Edirisuriya M Dilanga and Hu, Jianjun


• Accurate prediction of voltage of battery electrode materials using attention based graph neural networks
  , 2021.
  Siriwardane, Edirisuriya, Louis, Steph-Yves, Joshi, Rajendra, Kumar, Neeraj and Hu, Jianjun


• Predicting lattice phonon vibrational frequencies using deep graph neural networks
  arXiv preprint arXiv:2111.05885, 2021.
  Nguyen, Nghia, Louis, Steph-Yves, Wei, Lai, Choudhary, Kamal, Hu, Ming and Hu, Jianjun


• Piezoelectric modulus prediction using machine learning and graph neural networks
  arXiv preprint arXiv:2111.05557, 2021.
  Hu, Jeffrey and Song, Yuqi


• Big data driven product design: a survey
  arXiv preprint arXiv:2109.11424, 2021.
  Quan, Huafeng, Li, Shaobo, Zeng, Changchang, Wei, Hongjing and Hu, Jianjun


• Scalable deeper graph neural networks for high-performance materials property prediction
  arXiv e-prints, arXiv-2109, 2021.
  Sadeed Omee, Sadman, Louis, Steph-Yves, Fu, Nihang, Wei, Lai, Dey, Sourin, Dong, Rongzhi, Li, Qinyang and Hu, Jianjun


• Tcsp: a template based crystal structure prediction algorithm and web server for materials discovery
  arXiv preprint arXiv:2111.14049, 2021.
  Wei, Lai, Fu, Nihang, Siriwardane, Edirisuriya, Yang, Wenhui, Omee, Sadman Sadeed, Dong, Rongzhi, Xin, Rui and Hu, Jianjun


• Physics guided deep learning generative models for crystal materials discovery
  arXiv preprint arXiv:2112.03528, 2021.
  Zhao, Yong, Siriwardane, Edirisuriya and Hu, Jianjun


• Intelligent optimization algorithm-based path planning for a mobile robot
  Computational Intelligence and Neuroscience 2021, 2021.
  Song, Qisong, Li, Shaobo, Yang, Jing, Bai, Qiang, Hu, Jianjun, Zhang, Xingxing and Zhang, Ansi


• Semi-supervised teacher-student deep neural network for materials discovery
  arXiv preprint arXiv:2112.06142, 2021.
  Gleaves, Daniel, Siriwardane, Edirisuriya M Dilanga, Zhao, Yong, Fu, Nihang and Hu, Jianjun


• High-throughput discovery of novel cubic crystal materials using deep generative neural networks
  Advanced Science 8, 2100566, 2021.
  Zhao, Yong, Al-Fahdi, Mohammed, Hu, Ming, Siriwardane, Edirisuriya MD, Song, Yuqi, Nasiri, Alireza and Hu, Jianjun


• Composition based crystal materials symmetry prediction using machine learning with enhanced descriptors
  Computational Materials Science 198, 110686, 2021.
  Li, Yuxin, Dong, Rongzhi, Yang, Wenhui and Hu, Jianjun


• Crystal structure prediction of materials with high symmetry using differential evolution
  Journal of Physics: Condensed Matter 33, 455902, 2021.
  Yang, Wenhui, Siriwardane, Edirisuriya M Dilanga, Dong, Rongzhi, Li, Yuxin and Hu, Jianjun


• Active-learning-based generative design for the discovery of wide-band-gap materials
  The Journal of Physical Chemistry C 125, 16118-16128, 2021.
  Xin, Rui, Siriwardane, Edirisuriya MD, Song, Yuqi, Zhao, Yong, Louis, Steph-Yves, Nasiri, Alireza and Hu, Jianjun

2020

• Evaluating explorative prediction power of machine learning algorithms for materials discovery using k-fold forward cross-validation
  Computational Materials Science 171, 109203, 2020.
  Xiong, Zheng, Cui, Yuxin, Liu, Zhonghao, Zhao, Yong, Hu, Ming and Hu, Jianjun


• Machine learning-based prediction of crystal systems and space groups from inorganic materials compositions
  ACS Omega, 2020.
  Zhao, Yong, Cui, Yuxin, Xiong, Zheng, Jin, Jing, Liu, Zhonghao, Dong, Rongzhi and Hu, Jianjun


• Improving graph convolutional networks based on relation-aware attention for end-to-end relation extraction
  IEEE Access 8, 51315-51323, 2020.
  Hong, Yin, Liu, Yanxia, Yang, Suizhu, Zhang, Kaiwen, Wen, Aiqing and Hu, Jianjun


• Hydrothermal syntheses and crystal structures of molybdenum tellurites
  Journal of Solid State Chemistry, 121317, 2020.
  Ling, Jie, Zhang, Hongxia, Yuan, Kunpeng, Burgess, Dawanya, Hu, Jianjun and Hu, Ming


• Computational prediction of critical temperatures of superconductors based on convolutional gradient boosting decision trees
  IEEE Access 8, 57868-57878, 2020.
  Dan, Yabo, Dong, Rongzhi, Cao, Zhuo, Li, Xiang, Niu, Chengcheng, Li, Shaobo and Hu, Jianjun


• Remaining useful strength (rus) prediction of sicf-sicm composite materials using deep learning and acoustic emission
  Applied Sciences 10, 2680, 2020.
  Louis, Steph-Yves M, Nasiri, Alireza, Bao, Jingjing, Cui, Yuxin, Zhao, Yong, Jin, Jing, Huang, Xinyu and Hu, Jianjun


• Machine learning based prediction of noncentrosymmetric crystal materials
  Computational Materials Science 183, 109792, 2020.
  Song, Yuqi, Lindsay, Joseph, Zhao, Yong, Nasiri, Alireza, Louis, Steph-Yves, Ling, Jie, Hu, Ming and Hu, Jianjun


• Joint extraction of entities and relations using graph convolution over pruned dependency trees
  Neurocomputing 411, 302-312, 2020.
  Hong, Yin, Liu, Yanxia, Yang, Suizhu, Zhang, Kaiwen and Hu, Jianjun


• Identification of a heme activation site on the md-2/tlr4 complex
  Frontiers in Immunology 11, 1370, 2020.
  Belcher, John D, Zhang, Ping, NGUYEN, JULIA, Kiser, Zachary M, Nath, Karl A, Hu, Jianjun, Trent, John O and Vercellotti, Gregory M


• Inverse design of composite metal oxide optical materials based on deep transfer learning and global optimization
  Computational Materials Science, 110166, 2020.
  Dong, Rongzhi, Dan, Yabo, Li, Xiang and Hu, Jianjun


• Distance matrix-based crystal structure prediction using evolutionary algorithms
  The Journal of Physical Chemistry A, 2020.
  Hu, Jianjun, Yang, Wenhui and Dilanga Siriwardane, Edirisuriya M


• Generative adversarial networks (gan) based efficient sampling of chemical composition space for inverse design of inorganic materials
  NPJ Computational Materials 6, 2020.
  Yabo, Dan, Zhao, Yong, Li, Xiang, Li, Shaobo, Hu, Ming and Hu, Jianjun


• Graph convolutional neural networks with global attention for improved materials property prediction
  Physical Chemistry Chemical Physics 22, 18141-18148, 2020.
  Louis, Steph-Yves, Zhao, Yong, Nasiri, Alireza, Wang, Xiran, Song, Yuqi, Liu, Fei and Hu, Jianjun


• Predicting elastic properties of materials from electronic charge density using 3d deep convolutional neural networks
  The Journal of Physical Chemistry C 124, 17262-17273, 2020.
  Zhao, Yong, Yuan, Kunpeng, Liu, Yinqiao, Louis, Steph-Yves, Hu, Ming and Hu, Jianjun


• Lattice thermal conductivity prediction using symbolic regression and machine learning
  The Journal of Physical Chemistry A, 2020.
  Loftis, Christian, Yuan, Kunpeng, Zhao, Yong, Hu, Ming and Hu, Jianjun


• A survey on machine reading comprehension—tasks, evaluation metrics and benchmark datasets
  Applied Sciences 10, 7640, 2020.
  Zeng, Changchang, Li, Shaobo, Li, Qin, Hu, Jie and Hu, Jianjun


• Global attention based graph convolutional neural networks for improved materials property prediction
  arXiv preprint arXiv:2003.13379, 2020.
  Louis, Steph-Yves, Zhao, Yong, Nasiri, Alireza, Wong, Xiran, Song, Yuqi, Liu, Fei and Hu, Jianjun


• Node-select: a flexible graph neural network based on realistic propagation scheme
  , 2020.
  Louis, Steph-Yves, Nasiri, Alireza, Rolland, Fatima Christina, Mitro, Cameron and Hu, Jianjun


• Generative adversarial networks (gan) based efficient sampling of chemical composition space for inverse design of inorganic materials
  npj Computational Materials 6, 1-7, 2020.
  Dan, Yabo, Zhao, Yong, Li, Xiang, Li, Shaobo, Hu, Ming and Hu, Jianjun


• Critical temperature prediction of superconductors based on atomic vectors and deep learning
  Symmetry 12, 262, 2020.
  Li, Shaobo, Dan, Yabo, Li, Xiang, Hu, Tiantian, Dong, Rongzhi, Cao, Zhuo and Hu, Jianjun


• A novel method of bearing fault diagnosis in time-frequency graphs using inceptionresnet and deformable convolution networks
  IEEE Access 8, 92743-92753, 2020.
  Li, Shaobo, Yang, Wanli, Zhang, Ansi, Liu, Huibin, Huang, Jinyuan, Li, Chuanjiang and Hu, Jianjun


• Dynamical analysis of the fractional-order centrifugal flywheel governor system and its accelerated adaptive stabilization with the optimality
  International Journal of Electrical Power \& Energy Systems 118, 105792, 2020.
  Luo, Shaohua, Li, Junyang, Li, Shaobo and Hu, Jianjun

2019

• Deepseqpan, a novel deep convolutional neural network model for pan-specific class i hla-peptide binding affinity prediction
  Scientific Reports 9, 794, 2019.
  Liu, Zhonghao, Cui, Yuxin, Xiong, Zheng, Nasiri, Alierza, Zhang, Ansi and Hu, Jianjun


• Multiple pedestrian tracking by combining particle filter and network flow model
  Neurocomputing 351, 217-227, 2019.
  Cui, Yuxin, Zhang, Jingru, He, Zhenyu and Hu, Jianjun


• Deep autoencoder neural networks for short-term traffic congestion prediction of transportation networks
  Sensors 19, 2229, 2019.
  Zhang, Sen, Yao, Yong, Hu, Jie, Zhao, Yong, Li, Shaobo and Hu, Jianjun


• Using big data analytics to improve hiv medical care utilisation in south carolina: a study protocol
  BMJ Open 9, e027688, 2019.
  Olatosi, Bankole, Zhang, Jiajia, Weissman, Sharon, Hu, Jianjun, Haider, Mohammad Rifat and Li, Xiaoming


• Limited data rolling bearing fault diagnosis with few-shot learning
  IEEE Access 7, 110895-110904, 2019.
  Zhang, Ansi, Li, Shaobo, Cui, Yuxin, Yang, Wanli, Dong, Rongzhi and Hu, Jianjun


• A deep learning algorithm for one-step contour aware nuclei segmentation of histopathology images
  Medical \& biological engineering \& computing 57, 2027-2043, 2019.
  Cui, Yuxin, Zhang, Guiying, Liu, Zhonghao, Xiong, Zheng and Hu, Jianjun


• Complexface: a multi-representation approach for image classification with small dataset
  arXiv preprint arXiv:1902.07902, 2019.
  Zhang, Guiying, Cui, Yuxin, Zhao, Yong and Hu, Jianjun


• Online damage monitoring of sic f-sic m composite materials using acoustic emission and deep learning
  IEEE Access 7, 140534-140541, 2019.
  Nasiri, Alireza, Bao, Jingjing, Mccleeary, Donald, Louis, Steph-Yves M, Huang, Xinyu and Hu, Jianjun


• Anomalous thermal transport behavior in graphene-like carbon nitride (c $ \_3 $ n)
  arXiv preprint arXiv:1904.00316, 2019.
  Qin, Guangzhao, Qin, Zhenzhen, Wang, Huimin, Hu, Jianjun and Hu, Ming


• Attention mechanism-based deep learning pan-specific model for interpretable mhc-i peptide binding prediction
  bioRxiv, 830737, 2019.
  Jin, Jing, Liu, Zhonghao, Nasiri, Alireza, Cui, Yuxin, Louis, Stephen, Zhang, Ansi, Zhao, Yong and Hu, Jianjun


• Deep convolutional neural network and attention mechanism based pan-specific model for interpretable mhc-i peptide binding prediction
  bioRxiv, 830737, 2019.
  Jin, Jing, Liu, Zhonghao, Nasiri, Alireza, Cui, Yuxin, Louis, Stephen, Zhang, Ansi, Zhao, Yong and Hu, Jianjun


• Machine learning based ultra high carbon steel image segmentation
  , 2019.
  Suresh, Sumith Kuttiyil


• Convolutional neural networks for crystal material property prediction using hybrid orbital-field matrix and magpie descriptors
  Crystals 9, 191, 2019.
  Cao, Zhuo, Dan, Yabo, Xiong, Zheng, Niu, Chengcheng, Li, Xiang, Qian, Songrong and Hu, Jianjun


• Generative adversarial networks (gan) based efficient sampling of chemical space for inverse design of inorganic materials
  arXiv preprint arXiv:1911.05020, 2019.
  Dan, Yabo, Zhao, Yong, Li, Xiang, Li, Shaobo, Hu, Ming and Hu, Jianjun


• Computational screening of new perovskite materials using transfer learning and deep learning
  Applied Sciences 9, 5510, 2019.
  Li, Xiang, Dan, Yabo, Dong, Rongzhi, Cao, Zhuo, Niu, Chengcheng, Song, Yuqi, Li, Shaobo and Hu, Jianjun


• A review of text corpus-based tourism big data mining
  Applied Sciences 9, 3300, 2019.
  Li, Qin, Li, Shaobo, Zhang, Sen, Hu, Jie and Hu, Jianjun


• Personalized product evaluation based on gra-topsis and kansei engineering
  Symmetry 11, 867, 2019.
  Quan, Huafeng, Li, Shaobo, Wei, Hongjing and Hu, Jianjun

2018

1	Jianjun Hu, Zhonghao Liu, DeepMHC: Deep Convolutional Neural Networks for High-performance peptide-MHC Binding Affinity Prediction, bioRxiv 239236; 2017 doi: https://doi.org/10.1101/239236.
2	S Li, G Liu, X Tang, J Lu, J Hu, An Ensemble Deep Convolutional Neural Network Model with Improved DS Evidence Fusion for Bearing Fault Diagnosis, Sensors 2017, 17(8), 1729; doi:10.3390/s17081729.
3	Zheng Xiong, Yinyan He, Jason Hattric-Simpers, Jianjun Hu (2017) Automated Phase Segmentation for large-Scale X-ray Diffration Data using Graph-based Phase Segmentation (GPhase) Algorithm, ACS Combinatorial Sciences. DOI: 10.1021/acscombsci.6b00121
4	H. Luo, R. Benner, R. A. Long, J. Hu, "Subcellular Localization of Marine Bacterial Alkaline Phosphatases " Proceeding of National Academy of Science (PNAS), November 19, 2009
5	J. Hu, E. D. Goodman, and R. C. Rosenberg, " Automated Synthesis of Mechanical Vibration Absorbers Using Genetic Programming", Journal of Artificial Intelligence for Engineering Design, Analysis and Manufacturing. 22(3), 2008 , Special Issue on Genetic programming for human-competitive design
6	Shaoboli, Zheng Xiong, Jianjun Hu (2017) Inferring Phase Diagrams from X-ray Diffraction data with large background signals using Graph Segmentation Algorithm (BGPhase), Materials Science and Technology, (accepted). 2017
7	J. Hu, Yifeng David Yang and Daisuke Kihara, "EMD: an Ensemble Algorithm for discovering regulatory motifs in DNA sequences", BMC Bioinformatics, 7:342. 2006.
8	J. Hu, Bin Li, and Daisuke Kihara, "Limitations and Potentials of Current Motif Discovery Algorithms", Nucleic Acid Research, 33: 4899-4913, 2005
9	J. Hu, E. D. Goodman, and R. C. Rosenberg, " Automated Synthesis of Mechanical Vibration Absorbers Using Genetic Programming", Journal of Artificial Intelligence for Engineering Design, Analysis and Manufacturing. 22(3), 2008 , Special Issue on Genetic programming for human-competitive design
10	J. Hu, E. Goodman, K.Seo, Z. Fan, R. Rosenberg, "The Hierarchical Fair Competition (HFC) Framework for Sustainable Evolutionary Algorithms", Evolutionary Computation, 13 (2), MIT Press, 2005.

Conference Publications

1	A. Mondal and J. Hu, "Protein Localization by Integrating Multiple Protein Correlation Networks", Proceeding of 2012 International Conference on Bioinformatics and Computational Biology (BIOCOMP12), 2012
2	A. Mondal and J. Hu, "NetLoc: Network Based Protein Localization Prediction Using Protein-Protein Interaction and Co-expression Networks", Proceeding of IEEE International Conference on Bioinformatics & Biomedicine (BIBM2010)
3	F. Zhang and J. Hu, "Bayesian Classifier for Anchored Protein Sorting Discovery", Proceeding of 2009 IEEE International Conference on Bioinformatics & Biomedicine (BIBM09: Nov1-4 2009, USA).
4	J. Hu and F. Zhang (2009), Improving Protein Localization Prediction Using Amino Acid Group Based Physichemical Encoding”. Lecture Notes in Computer Science. Bioinformatics and Computational Biology BiCoB2009. 5462/2009. Springer Berlin / Heidelberg
5	J. Hu, X. Zhong, E. Goodman, “Open-ended Robust Design of Analog filters Using Genetic Programming”, Proc. Genetic and Evolutionary Computation Conference. (Best paper nominee), 2005
6	J. Hu, E. Goodman, “Robust and Efficient Genetic Algorithms with Hierarchical Niching and Sustainable Evolutionary Computation Model”, Proc.Genetic and Evolutionary Computation Conference.2004
7	J. Hu, E. Goodman, “Wireless Access Point Configuration by Genetic Programming”, Proc. IEEE Congress on Evolutionary Computation (CEC) 2004.
8	J. Hu, E. Goodman, and R. Rosenberg, “Topological search in automated mechatronic system synthesis using bond graphs and genetic programming”, Proc. of American Control Conference (ACC), 2004.
9	J. Hu, K. Seo, Z. Fan, R. Rosenberg, and E. Goodman, " HEMO: A Sustainable Multi-Objective Evolutionary Optimization Framework", Proc. 2003 Genetic and Evolutionary Computation Conference, Chicago, Springer, Lecture Notes in Computer Science, July, pp. 1029-1040, 2003

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