Xingquan Zhu

Florida Atlantic University, Boca Raton, Florida, United States

Are you Xingquan Zhu?

Claim your profile

Publications (169)70.37 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: Naive Bayes (NB) is a popular machine learning tool for classification, due to its simplicity, high computational efficiency, and good classification accuracy, especially for high dimensional data such as texts. In reality, the pronounced advantage of NB is often challenged by the strong conditional independence assumption between attributes, which may deteriorate the classification performance. Accordingly, numerous efforts have been made to improve NB, by using approaches such as structure extension, attribute selection, attribute weighting, instance weighting, local learning and so on. In this paper, we propose a new Artificial Immune System (AIS) based self-adaptive attribute weighting method for Naive Bayes classification. The proposed method, namely AISWNB, uses immunity theory in Artificial Immune Systems to search optimal attribute weight values, where self-adjusted weight values will alleviate the conditional independence assumption and help calculate the conditional probability in an accurate way. One noticeable advantage of AISWNB is that the unique immune system based evolutionary computation process, including initialization, clone, section, and mutation, ensures that AISWNB can adjust itself to the data without explicit specification of functional or distributional forms of the underlying model. As a result, AISWNB can obtain good attribute weight values during the learning process. Experiments and comparisons on 36 machine learning benchmark data sets and six image classification data sets demonstrate that AISWNB significantly outperforms its peers in classification accuracy, class probability estimation, and class ranking performance.
    Expert Systems with Applications 02/2015; 42(3):1487–1502. · 1.85 Impact Factor
  • Shirui Pan, Jia Wu, Xingquan Zhu, Chengqi Zhang
    [Show abstract] [Hide abstract]
    ABSTRACT: Many applications involve stream data with structural dependency, graph representations, and continuously increasing volumes. For these applications, it is very common that their class distributions are imbalanced with minority (or positive) samples being only a small portion of the \hbox{population}, which imposes significant challenges for learning models to accurately identify minority samples. This problem is further complicated with the presence of noise, because they are similar to minority samples and any treatment for the class imbalance may falsely focus on the noise and result in deterioration of accuracy. In this paper, we propose a classification model to tackle imbalanced graph streams with noise. Our method, graph ensemble boosting, employs an ensemble-based framework to partition graph stream into chunks each containing a number of noisy graphs with imbalanced class distributions. For each individual chunk, we propose a boosting algorithm to combine discriminative subgraph pattern selection and model learning as a unified framework for graph classification. To tackle concept drifting in graph streams, an instance level weighting mechanism is used to dynamically adjust the instance weight, through which the boosting framework can emphasize on difficult graph \hbox{samples}. The classifiers built from different graph chunks form an ensemble for graph stream classification. Experiments on real-life imbalanced graph streams demonstrate clear benefits of our boosting design for handling imbalanced noisy graph stream.
    IEEE transactions on cybernetics. 08/2014;
  • Bin Li, Xingquan Zhu, Ruijiang Li, Chengqi Zhang
    [Show abstract] [Hide abstract]
    ABSTRACT: Cross-domain collaborative filtering (CF) aims to share common rating knowledge across multiple related CF domains to boost the CF performance. In this paper, we view CF domains as a 2-D site-time coordinate system, on which multiple related domains, such as similar recommender sites or successive time-slices, can share group-level rating patterns. We propose a unified framework for cross-domain CF over the site-time coordinate system by sharing group-level rating patterns and imposing user/item dependence across domains. A generative model, say ratings over site-time (ROST), which can generate and predict ratings for multiple related CF domains, is developed as the basic model for the framework. We further introduce cross-domain user/item dependence into ROST and extend it to two real-world cross-domain CF scenarios: 1) ROST (sites) for alleviating rating sparsity in the target domain, where multiple similar sites are viewed as related CF domains and some items in the target domain depend on their correspondences in the related ones; and 2) ROST (time) for modeling user-interest drift over time, where a series of time-slices are viewed as related CF domains and a user at current time-slice depends on herself in the previous time-slice. All these ROST models are instances of the proposed unified framework. The experimental results show that ROST (sites) can effectively alleviate the sparsity problem to improve rating prediction performance and ROST (time) can clearly track and visualize user-interest drift over time.
    IEEE transactions on cybernetics. 08/2014;
  • Jia Wu, Shirui Pan, Xingquan Zhu, Zhihua Cai
    [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, we formulate a novel graph-based learning problem, multi-graph classification (MGC), which aims to learn a classifier from a set of labeled bags each containing a number of graphs inside the bag. A bag is labeled positive, if at least one graph in the bag is positive, and negative otherwise. Such a multi-graph representation can be used for many real-world applications, such as webpage classification, where a webpage can be regarded as a bag with texts and images inside the webpage being represented as graphs. This problem is a generalization of multi-instance learning (MIL) but with vital differences, mainly because instances in MIL share a common feature space whereas no feature is available to represent graphs in a multi-graph bag. To solve the problem, we propose a boosting based multi-graph classification framework (bMGC). Given a set of labeled multi-graph bags, bMGC employs dynamic weight adjustment at both bag- and graph-levels to select one subgraph in each iteration as a weak classifier. In each iteration, bag and graph weights are adjusted such that an incorrectly classified bag will receive a higher weight because its predicted bag label conflicts to the genuine label, whereas an incorrectly classified graph will receive a lower weight value if the graph is in a positive bag (or a higher weight if the graph is in a negative bag). Accordingly, bMGC is able to differentiate graphs in positive and negative bags to derive effective classifiers to form a boosting model for MGC. Experiments and comparisons on real-world multi-graph learning tasks demonstrate the algorithm performance.
    IEEE transactions on cybernetics. 07/2014;
  • Meng Fang, Xingquan Zhu
    [Show abstract] [Hide abstract]
    ABSTRACT: Traditional active learning assumes that the labeler is capable of providing ground truth label for each queried instance. In reality, a labeler might not have sufficient knowledge to label a queried instance but can only guess the label with his/her best knowledge. As a result, the label provided by the labeler, who is regarded to have uncertain labeling knowledge, might be incorrect. In this paper, we formulate this problem as a new “uncertain labeling knowledge” based active learning paradigm, and our key is to characterize the knowledge set of each labeler for active learning. By taking each unlabeled instance’s information and its likelihood of belonging to the uncertain knowledge set as a whole, we define an objective function to ensure that each queried instance is the most informative one for labeling and the labeler should also have sufficient knowledge to label the instance. To ensure label quality, we propose to use diversity density to characterize a labeler’s uncertain knowledge and further employ an error-reduction-based mechanism to either accept or decline a labeler’s label on uncertain instances. Experiments demonstrate the effectiveness of the proposed algorithm for real-world active learning tasks with uncertain labeling knowledge.
    Pattern Recognition Letters 07/2014; 43:98–108. · 1.27 Impact Factor
  • Source
    Meng Fang, Jie Yin, Xingquan Zhu
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper addresses the problem of transferring useful knowledge from a source network to predict node labels in a newly formed target network. While existing transfer learning research has primarily focused on vector-based data, in which the instances are assumed to be independent and identically distributed, how to effectively transfer knowledge across different information networks has not been well studied, mainly because networks may have their distinct node features and link relationships between nodes. In this paper, we propose a new transfer learning algorithm that attempts to transfer common latent structure features across the source and target networks. The proposed algorithm discovers these latent features by constructing label propagation matrices in the source and target networks, and mapping them into a shared latent feature space. The latent features capture common structure patterns shared by two networks, and serve as domain-independent features to be transferred between networks. Together with domain-dependent node features, we thereafter propose an iterative classification algorithm that leverages label correlations to predict node labels in the target network. Experiments on real-world networks demonstrate that our proposed algorithm can successfully achieve knowledge transfer between networks to help improve the accuracy of classifying nodes in the target network.
    03/2014;
  • Xindong Wu, Xingquan Zhu, Gong-Qing Wu, Wei Ding
    [Show abstract] [Hide abstract]
    ABSTRACT: Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution.
    IEEE Transactions on Knowledge and Data Engineering 01/2014; 26(1):97-107. · 1.89 Impact Factor
  • Guohua Liang, Xingquan Zhu, Chengqi Zhang
    [Show abstract] [Hide abstract]
    ABSTRACT: Many real world applications involve highly imbalanced class distribution. Research into learning from imbalanced class distribution is considered to be one of ten challenging problems in data mining research, and it has increasingly captured the attention of both academia and industry. In this work, we study the effects of different levels of imbalanced class distribution on bagging predictors by using under-sampling techniques. Despite the popularity of bagging in many real-world applications, some questions have not been clearly answered in the existing research, such as the effect of varying the levels of class distribution on different bagging predictors, e.g., whether bagging is superior to single learners when the levels of class distribution change. Most classification learning algorithms are designed to maximize the overall accuracy rate and assume that training instances are uniformly distributed; however, the overall accuracy does not represent correct prediction on the minority class, which is the class of interest to users. The overall accuracy metric is therefore ineffective for evaluating the performance of classifiers in extremely imbalanced data. This study investigates the effect of varying levels of class distribution on different bagging predictors based on the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) as a performance metric, using an under-sampling technique on 14 data-sets with imbalanced class distributions. Our experimental results indicate that Decision Table (DTable) and RepTree are the learning algorithms with the best bagging AUC performance. The AUC performances of bagging predictors are statistically superior to single learners, with the exception of Support Vector Machines (SVM) and Decision Stump (DStump).
    International Journal of Machine Learning and Cybernetics, IJMLC. 01/2014; 5(1):63-71.
  • Bin Li, Ling Chen, Xingquan Zhu, Chengqi Zhang
    [Show abstract] [Hide abstract]
    ABSTRACT: Social recommender systems largely rely on user-contributed data to infer users’ preference. While this feature has enabled many interesting applications in social networking services, it also introduces unreliability to recommenders as users are allowed to insert data freely. Although detecting malicious attacks from social spammers has been studied for years, little work was done for detecting Noisy but Non-Malicious Users (NNMUs), which refers to those genuine users who may provide some untruthful data due to their imperfect behaviors. Unlike colluded malicious attacks that can be detected by finding similarly-behaved user profiles, NNMUs are more difficult to identify since their profiles are neither similar nor correlated from one another. In this article, we study how to detect NNMUs in social recommender systems. Based on the assumption that the ratings provided by a same user on closely correlated items should have similar scores, we propose an effective method for NNMU detection by capturing and accumulating user’s “self-contradictions”, i.e., the cases that a user provides very different rating scores on closely correlated items. We show that self-contradiction capturing can be formulated as a constrained quadratic optimization problem w.r.t. a set of slack variables, which can be further used to quantify the underlying noise in each test user profile. We adopt three real-world data sets to empirically test the proposed method. The experimental results show that our method (i) is effective in real-world NNMU detection scenarios, (ii) can significantly outperform other noisy-user detection methods, and (iii) can improve recommendation performance for other users after removing detected NNMUs from the recommender system.
    World Wide Web 11/2013; · 1.20 Impact Factor
  • Ting Guo, Xingquan Zhu
    [Show abstract] [Hide abstract]
    ABSTRACT: Graph classification concerns the learning of discriminative models, from structured training data, to classify previously unseen graph samples into specific categories, where the main challenge is to explore structural information in the training data to build classifiers. One of the most common graph classification approaches is to use sub-graph features to convert graphs into instance-feature representations, so generic learning algorithms can be applied to derive learning models. Finding good sub-graph features is regarded as an important task for this type of learning approaches, despite that there is no comprehensive understanding on (1) how effective sub-graph features can be used for graph classification? (2) how many sub-graph features are sufficient for good classification results? (3) does the length of the sub-graph features play major roles for classification? and (4) whether some random sub-graphs can be used for graph representation and classification? Motivated by the above concerns, we carry out empirical studies on four real-world graph classification tasks, by using three types of sub-graph features, including frequent sub-graphs, frequent sub-graph selected by using information gain, and random sub-graphs, and by using two types of learning algorithms including Support Vector Machines and Nearest Neighbour. Our experiments show that (1) the discriminative power of sub-graphs varies by their sizes; (2) random sub-graphs have a reasonably good performance; (3) number of sub-graphs is important to ensure good performance; and (4) increasing number of sub-graphs reduces the difference between classifiers built from different sub-graphs. Our studies provide a practical guidance for designing effective sub-graph based graph classification methods.
    10/2013;
  • Ting Guo, Lianhua Chi, Xingquan Zhu
    [Show abstract] [Hide abstract]
    ABSTRACT: Graph stream classification concerns building learning models from continuously growing graph data, in which an essential step is to explore subgraph features to represent graphs for effective learning and classification. When representing a graph using subgraph features, all existing methods employ coarse-grained feature representation, which only considers whether or not a subgraph feature appears in the graph. In this paper, we propose a fine-grained graph factorization approach for Fast Graph Stream Classification (FGSC). Our main idea is to find a set of cliques as feature base to represent each graph as a linear combination of the base cliques. To achieve this goal, we decompose each graph into a number of cliques and select discriminative cliques to generate a transfer matrix called Clique Set Matrix (M). By using M as the base for formulating graph factorization, each graph is represented in a vector space with each element denoting the degree of the corresponding subgraph feature related to the graph, so existing supervised learning algorithms can be applied to derive learning models for graph classification.
    Proceedings of the 22nd ACM international conference on Conference on information & knowledge management; 10/2013
  • Meng Fang, Jie Yin, Xingquan Zhu
    [Show abstract] [Hide abstract]
    ABSTRACT: Modern information networks, such as social networks, are often characterized with large sizes and dynamic changing structures. To analyze these networks, existing solutions commonly rely on graph sampling techniques to reduce network sizes, and then carry out succeeding mining processes, such as labeling network nodes to build classification models. Such a sampling-then-labeling paradigm assumes that the whole network is available for sampling and the sampled network is useful for all subsequent tasks (such as network classification). Yet real-world networks are rarely immediately available unless the sampling process progressively crawls every single node and its connections. Meanwhile, without knowing the underlying analytic objective, the sampled network can hardly produce quality results. In this paper, we propose an Active Exploration framework for large graphs where the goal is to carry out network sampling and node labeling at the same time. To achieve this goal, we consider a network as a Markov chain and compute its stationary distribution by using supervised random walks. The stationary distribution of the sampled network help identify important nodes to be explored in the next step, and the labeling process labels the most informative node which in turn strengthens the sampling process. The mutually and simultaneously enhanced sampling and labeling processes ensure that the final network contains a maximum number of nodes directly related to the underlying mining tasks.
    Proceedings of the 22nd ACM international conference on Conference on information & knowledge management; 10/2013
  • Shirui Pan, Xingquan Zhu
    [Show abstract] [Hide abstract]
    ABSTRACT: Recent years have witnessed an increasing number of applications involving data with structural dependency and graph representations. For these applications, it is very common that their class distribution is imbalanced with minority samples being only a small portion of the population. Such imbalanced class distributions impose significant challenges to the learning algorithms. This problem is further complicated with the presence of noise or outliers in the graph data. In this paper, we propose an imbalanced graph boosting algorithm, igBoost, that progressively selects informative subgraph patterns from imbalanced graph data for learning. To handle class imbalance, we take class distributions into consideration to assign different weight values to graphs. The distance of each graph to its class center is also considered to adjust the weight to reduce the impact of noisy graph data. The weight values are integrated into the iterative subgraph feature selection and margin learning process to achieve maximum benefits. Experiments on real-world graph data with different degrees of class imbalance and noise demonstrate the algorithm performance.
    Proceedings of the Twenty-Third international joint conference on Artificial Intelligence; 08/2013
  • [Show abstract] [Hide abstract]
    ABSTRACT: Patterns/subsequences frequently appearing in sequences provide essential knowledge for domain experts, such as molecular biologists, to discover rules or patterns hidden behind the data. Due to the inherent complex nature of the biological data, patterns rarely exactly reproduce and repeat themselves, but rather appear with a slightly different form in each of its appearances. A gap constraint (In this paper, a gap constraint (also referred to as a wildcard) is a character that can be substituted for any character predefined in an alphabet.) provides flexibility for users to capture useful patterns even if their appearances vary in the sequences. In order to find patterns, existing tools require users to explicitly specify gap constraints beforehand. In reality, it is often nontrivial or time-consuming for users to provide proper gap constraint values. In addition, a change made to the gap values may give completely different results, and require a separate time-consuming re-mining procedure. Therefore, it is desirable to automatically and efficiently find patterns without involving user-specified gap requirements. In this paper, we study the problem of frequent pattern mining without user-specified gap constraints and propose PMBC (namely P̲atternM̲ining from B̲iological sequences with wildcard C onstraints) to solve the problem. Given a sequence and a support threshold value (i.e. pattern frequency threshold), PMBC intends to discover all subsequences with their support values equal to or greater than the given threshold value. The frequent subsequences then form patterns later on. Two heuristic methods (one-way vs. two-way scans) are proposed to discover frequent subsequences and estimate their frequency in the sequences. Experimental results on both synthetic and real-world DNA sequences demonstrate the performance of both methods for frequent pattern mining and pattern frequency estimation.
    Computers in biology and medicine 06/2013; 43(5):481-92. · 1.27 Impact Factor
  • Xindong Wu, Kui Yu, Wei Ding, Hao Wang, Xingquan Zhu
    [Show abstract] [Hide abstract]
    ABSTRACT: We propose a new online feature selection framework for applications with streaming features where the knowledge of the full feature space is unknown in advance. We define streaming features as features that flow in one by one over time whereas the number of training examples remains fixed. This is in contrast with traditional online learning methods that only deal with sequentially added observations, with little attention being paid to streaming features. The critical challenges for Online Streaming Feature Selection (OSFS) include 1) the continuous growth of feature volumes over time, 2) a large feature space, possibly of unknown or infinite size, and 3) the unavailability of the entire feature set before learning starts. In the paper, we present a novel Online Streaming Feature Selection method to select strongly relevant and nonredundant features on the fly. An efficient Fast-OSFS algorithm is proposed to improve feature selection performance. The proposed algorithms are evaluated extensively on high-dimensional datasets and also with a real-world case study on impact crater detection. Experimental results demonstrate that the algorithms achieve better compactness and higher prediction accuracy than existing streaming feature selection algorithms.
    IEEE Transactions on Software Engineering 05/2013; 35(5):1178-92. · 2.59 Impact Factor
  • Hanning Yuan, Meng Fang, Xingquan Zhu
    [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, we propose a Hierarchical Sampling-based Multi-Instance ensemble LEarning (HSMILE) method. Due to the unique multi-instance learning nature, a positive bag contains at least one positive instance whereas samples (instance and sample are interchangeable terms in this paper) in a negative bag are all negative, simply applying bootstrap sampling to individual bags may severely damage a positive bag because a sampled positive bag may not contain any positive sample at all. To solve the problem, we propose to calculate probable positive sample distributions in each positive bag and use the distributions to preserve at least one positive instance in a sampled bag. The hierarchical sampling involves inter- and intrabag sampling to adequately perturb bootstrap sample sets for multi-instance ensemble learning. Theoretical analysis and experiments confirm that HSMILE outperforms existing multi-instance ensemble learning methods.
    IEEE Transactions on Knowledge and Data Engineering 01/2013; 25(12):2900-2905. · 1.89 Impact Factor
  • Jia Wu, Zhihua Cai, Xingquan Zhu
    [Show abstract] [Hide abstract]
    ABSTRACT: Probability estimation from a given set of training examples is crucial for learning Naive Bayes (NB) Classifiers. For an insufficient number of training examples, the estimation will suffer from the zero-frequency problem which does not allow NB classifiers to classify instances whose conditional probabilities are zero. Laplace-estimate and M-estimate are two common methods which alleviate the zero-frequency problem by adding some fixed terms to the probability estimation to avoid zero conditional probability. A major issue with this type of design is that the fixed terms are pre-specified without considering the uniqueness of the underlying training data. In this paper, we propose an Artificial Immune System (AIS) based self-adaptive probability estimation method, namely AISENB, which uses AIS to automatically and self-adaptively select the optimal terms and values for probability estimation. The unique immune system based evolutionary computation process, including initialization, clone, mutation, and crossover, ensure that AISENB can adjust itself to the data without explicit specification of functional or distributional forms for the underlying model. Experimental results and comparisons on 36 benchmark datasets demonstrate that AISENB significantly outperforms traditional probability estimation based Naive Bayes classification approaches.
    Neural Networks (IJCNN), The 2013 International Joint Conference on; 01/2013
  • Jia Wu, Zhihua Cai, Sanyou Zeng, Xingquan Zhu
    [Show abstract] [Hide abstract]
    ABSTRACT: Naive Bayes (NB) is a popularly used classification method. One potential weakness of NB is the strong conditional independence assumption between attributes, which may deteriorate the classification accuracy. In this paper, we propose a new Artificial Immune System based Weighted Naive Bayes (AISWNB) classifier. AISWNB uses immunity theory in artificial immune systems to find optimal weight values for each attribute. The adjusted weight values will alleviate the conditional independence assumption and help calculate the conditional probability in an accurate way. Because AISWNB uses artificial immune system search mechanism to find optimal weights, it does not need to know the importance of individual attributes nor the relevance among attributes. As a result, it can obtain optimal weight value for each attribute during the learning process. Experiments and comparisons on 36 benchmark data sets demonstrate that AISWNB outperforms other state-of-the-art attribute weighted NB algorithms.
    Neural Networks (IJCNN), The 2013 International Joint Conference on; 01/2013
  • Shirui Pan, Xingquan Zhu, Chengqi Zhang, P.S. Yu
    [Show abstract] [Hide abstract]
    ABSTRACT: Graph classification is becoming increasingly popular due to the rapidly rising applications involving data with structural dependency. The wide spread of the graph applications and the inherent complex relationships between graph objects have made the labels of the graph data expensive and/or difficult to obtain, especially for applications involving dynamic changing graph records. While labeled graphs are limited, the copious amounts of unlabeled graphs are often easy to obtain with trivial efforts. In this paper, we propose a framework to build a stream based graph classification model by combining both labeled and unlabeled graphs. Our method, called gSLU, employs an ensemble based framework to partition graph streams into a number of graph chunks each containing some labeled and unlabeled graphs. For each individual chunk, we propose a minimum-redundancy subgraph feature selection module to select a set of informative subgraph features to build a classifier. To tackle the concept drifting in graph streams, an instance level weighting mechanism is used to dynamically adjust the instance weight, through which the subgraph feature selection can emphasize on difficult graph samples. The classifiers built from different graph chunks form an ensemble for graph stream classification. Experiments on real-world graph streams demonstrate clear benefits of using minimum-redundancy subgraph features to build accurate classifiers. By employing instance level weighting, our graph ensemble model can effectively adapt to the concept drifting in the graph stream for classification.
    Data Engineering (ICDE), 2013 IEEE 29th International Conference on; 01/2013
  • [Show abstract] [Hide abstract]
    ABSTRACT: Short & sparse text is becoming more prevalent on the web, such as search snippets, micro-blogs and product reviews. Accurately classifying short & sparse text has emerged as an important while challenging task. Existing work has considered utilizing external data (e.g. Wikipedia) to alleviate data sparseness, by appending topics detected from external data as new features. However, training a classifier on features concatenated from different spaces is not easy considering the features have different physical meanings and different significance to the classification task. Moreover, it exacerbates the "curse of dimensionality" problem. In this study, we propose a transfer classification method, TCSST, to exploit the external data to tackle the data sparsity issue. The transfer classifier will be learned in the original feature space. Considering that the labels of the external data may not be readily available or sufficiently enough, TCSST further exploits the unlabeled external data to aid the transfer classification. We develop novel strategies to allow TCSST to iteratively select high quality unlabeled external data to help with the classification. We evaluate the performance of TCSST on both benchmark as well as real-world data sets. Our experimental results demonstrate that the proposed method is effective in classifying very short & sparse text, consistently outperforming existing and baseline methods.
    Proceedings of the 21st ACM international conference on Information and knowledge management; 10/2012

Publication Stats

2k Citations
70.37 Total Impact Points

Institutions

  • 2006–2014
    • Florida Atlantic University
      • Department of Computer and Electrical Engineering and Computer Science
      Boca Raton, Florida, United States
  • 2009–2013
    • University of Technology Sydney 
      • • Centre for Quantum Computation and Intelligent Systems (QCIS)
      • • Faculty of Engineering and Information Technology
      Sydney, New South Wales, Australia
  • 2008–2013
    • Hefei University of Technology
      Luchow, Anhui Sheng, China
  • 2011
    • UTS:Insearch
      Sydney, New South Wales, Australia
  • 2000–2010
    • Fudan University
      • School of Computer Science
      Shanghai, Shanghai Shi, China
  • 2003–2009
    • University of Vermont
      • • Department of Computer Science
      • • Department of Electrical Engineering
      Burlington, VT, United States
  • 2007
    • Chinese Academy of Sciences
      • Research Center for Cyber Economy and Knowledge Management
      Peping, Beijing, China
  • 2001–2004
    • University of North Carolina at Charlotte
      • Department of Computer Science
      Charlotte, NC, United States
  • 2001–2003
    • Purdue University
      • Department of Computer Science
      West Lafayette, IN, United States