SUMMARY:

• Developed advanced and creative machine learning regression/classification algorithm for food component analysis using chemometrics and spectroscopy;
• Recent postdoctoral research on uniformly and unbiased sampling/crawling online social networks using advanced Markov Chain Monte Carlo techniques; developed a new sampling algorithm called conditional independent coupling using Ruby and Rails and Twitter API, DataMapper; Unix/Linux, Amazon EC2; social media analysis using Python, NLTK, SKLearn.
• Previous postdoctoral research in Confidential, for Complex Dynamic Network Analysis; Simulation of Autonomous Underwater Vehicles; using MatLab and VB; research and development for big data analysis using mapreduce/hadoop.
• Two year research contract in Health Canada for nuclear explosion and pollution monitoring, and environmental anomaly detection; using J# and Weka (Java) software package, Eclipse.
• The main research interest focus on Machine Learning and Data Mining algorithms and technology; in particular, one - class learning using kernel methods for fraud/anomaly detection and its application on big data using MapReduce/Hadoop with Pig/Hive/HBase, and advanced Markov Chain Monte Carlo techniques for fast and unbiased sampling/crawling online social networks such as Twitter and Facebook.
• Having both Mathematics and Computer Science backgrounds; 10 year professional experience for software development, various artificial intelligence algorithm design, and leadership for transaction and database applications using SQL Server, ORACLE, C/C++, Java/J#, VB, JDBC, .Net., PowerBuilder, TCP/IP, OpenGL;
• Research work on information security using anomaly detection techniques on Web server such as WebSphere/Weblogic with Spring, Swing, AJAX, SOAP; XML, HTML5, NoSQL;

WORK EXPERIENCE:

Confidential

Postdoc researcher

Responsibilities:

• Big data analytics and supply chain management; web crawling for large supply chain networks; graphic and network analysis; Statistical Markov Chain Monte Carlo; parallel MCMC for web crawling; develop advanced techniques for community detection/security;
• Research on advanced regression/classification algorithms; deep learning; stacked regression; forecasting techniques for supply chain analysis.

  Innovative regression/classification; deep learning; stacked regression

Confidential

Senior Machine Learning Specialist

Responsibilities:

• Food component analysis: macronutrients (calories, carbo, proteins) and sugars( fructose, maltose, sucrose) using machine learning for chemometrics;
• Developed advanced and creative and innovative regression/classification machine learning algorithm for precisely predicting/detecting food ingredients;
• Linear regression/generalized linear regression; PCA/PLS; Stacked Regression/Stacked PCA/PLS; Lasso L1/L2; feature extraction, using weka and Python, scikit-learning;
• Model selection; new ensemble learning algorithm for nonlinear regression; Semi-supervised/supervised learning for food analysis with missing labeled data
• Learning without overfitting and curse of dimensionality
• New cutting edge machine learning techniques by overcoming overfitting, curse of dimensionality, class imbalanced problem;
• Advanced Markov Chain Monte Carlo for scalable parallel computation for big data analysis; comparison between MCMC and MapReduce/Hadoop; MLE and MapReduce/Hadoop;
• New anomaly detection techniques using the new learning framework: kernel decomposition and Maximum Likelihood Estimation using the statistical package, R., Java, C++;

Confidential

Researcher

Responsibilities:

• Supported by NSERC Engage Grant and SME4SME Grant as a sole researcher; developed an innovative and unique algorithm which applies advanced Markov Chain Monte Carlo methods such as coupling techniques for sampling large graph networks;
• Extended traditional coupling algorithms such as perfect sampling; conducted experiments by sampling online social networks such as Twitter and small social networks; results show that the algorithm is extremely efficient to produces unbiased samples.
• The algorithm was implemented using Ruby, Twitter API, DataMapper, SQLite, MySQL, PostGres; running environment: Unix/Linux, Amazon EC2; designed a web application using Rails with MVC pattern for demonstration;
• Performed social network analysis such as degree distribution, Centrality, Clustering coefficient, community detection. Initial research results have been published in IEEE ICDM International Workshop on Data Mining in Network, 2012.
• Obtained a US patent for the initial research result as the original inventor.
• Applied to Community detection and social media analysis using Python for NLP such as NLTK, SKLearn; RapidMiner, C++, R; tasks for text classification, sentiment analysis, term extraction.

Confidential

Postdoctoral Researcher

Responsibilities:

• Engaged in the development of the software tool for simulation of Autonomous Underwater Vehicles using MatLab and VB; Involved in research on Complex Dynamic Network Analysis; simulation using MatLab;
• Utilized various artificial intelligence algorithms such as Genetic Algorithm (GA) in MatLab for simulating and computing the shorted path with the lowest cost in complex networks.
• Implemented the Box-Muller algorithm to simulate mine distributions on the seabed as normal distributions; implemented algorithms to dynamically demonstrate the manipulation of Autonomous Underwater Vehicle (AUV).
• Performed complex dynamic network analysis using various tools such as SNAP and Pajek; knowledge of the small world effect, degree distribution, degree correlation, centrality, clustering coefficient, community detection.
• Developed a new fraud/anomaly detection algorithm using Java (J#/Eclipse), weka for machine lerning, kernel methods, ensemble learning, one class learning; improved the traditional One-Class SVM algorithm implemented in LibSVM in Weka (a Java package for data mining and machine learning algorithms). This research work was published in Canadian Artificial Intelligence (AI) in 2012.
• Designed one-class Naive Bayes algorithm for anomaly detection in big data using MapReduce/Hadoop, with Pig/Hive/HBase;
• Also used anomaly detection techniques for information security by analyzing web log files and data transfer on WebSphere/WebLogic; EJB, AJAX, SOAP, XML, HTML5, NoSQL.
• Anomaly detection techniques, one-class learning algorithm, kernel methods, support vector machine algorithm;
• Social networks; social network analysis; small world effect; degree distribution, community detection;
• Big data; MapReduce/Hadoop, Pig/Hive/HBase, MatLab, VB, social network tools and package: SNAP and Pajek, Java(J#/Eclipse), SVM, Weka; EJB, WebSphere/Weblogic, AJAX, SOAP, XML, HTML5, NoSQL.

Confidential

Research Assistant

Responsibilities:

• Researched and developed anomaly detection techniques as a research assistance in Confidential, and cooperated with Health Canada for monitoring nuclear pollution, using Weka(data mining and machine learning Java open source), RapidMiner, C++, R;
• Built the architecture consisting of severs and databases using J2SE, J2EE, J2ME for information retrieval and management of samples and data synthesized in laboratory or collected from natural environment for nuclear exploration and pollution; designed and developed the messaging framework for communication between researchers over XML.
• Developed a new instance selection algorithm for supervised learning from large datasets by applying Markov Chain Monte Carlo methods; designed a border identification algorithm to identify borders from datasets; research results were published in International Conference on Data Mining (ICDM) in 2008;
• Developed various anomaly detection algorithms by re-designing one-class learning algorithms based on traditional supervised learning algorithms such as Naive Bayes, Bayesian Networks, k-Nearest Neighbor, k-mean, Parzen density estimation; algorithms were implemented using Java/J#; Weka (data mining and machine learning Java open source); this work was published in Conference on Intelligent Data Understanding(CIDU) in 2010.
• Data Mining and Machine Learning algorithms, Naive Bayes, Bayesian Networks, k-Nearest Neighbors, k-Mean, Generalized Linear Model, SVM, OCSVM; using Java/Eclipse, Weka, RapidMiner, C++, R.
• J2SE, J2EE, J2ME, WebSphere/Weblogic, AJAX, and SOAP, Jini, XML/XSLT, JAXB, SAX/DOM, PersonalJava, JavaSpaces, HTML5, NoSQL.

Confidential

Research Assistant

Responsibilities:

• Researched on association analysis large transaction datasets; developed new knowledge discovery Algorithms; analysis of Web log files for user behaviors, using Java and python;
• Researched on animation simulation and crowd behavior manipulation using C++ for autonomy and intelligence using artificial intelligence technology such as neural networks, etc;
• Published two research papers: basic association rule, searching for pattern rules, using Java;
• Teaching experience of Java programming course.
• Various similarity and interestingness measures: Gini Index, Support, Confidence, Conviction, Cosine, Laplace, Interest, Jaccard, Shannon entropy, Piatetsky-Shapiro, Kullback-Leiber diversity, Goodman and Kruskal, Pearson correlation coefficient, J-measure, Euclidean distance, etc;
• Knowledge discover and data mining algorithms; scalable and heuristic search algorithms such as mining association and interesting rules on large transaction datasets;
• Java, C++, Weka: data mining and machine learning package.

Confidential

Instructor

Responsibilities:

• Employed in Department of Computer Science, Zhengzhou/HuangHe University;
• Teaching courses including RDBMS; C/C++;

Confidential

Software Engineer

Responsibilities:

• A part-time position; I was responsible of software development for practical applications;
• As a project leader, I was involved in developing Remote Exchange System for future trade, mainly using TCP/IP, C/C++, SQL server, Unix/Linux ;
• As a sole developer, I developed Accounting System for future trade, mainly using PowerBuilder and SQL server ; I designed and implemented and tested all accounting computational procedures; easy and friendly manipulation and maintenance;
• Obtained all experience for software development, test, documents, maintenance, and sale;Both software products obtained great success in market; the products gained the highest market share.

Confidential

Research assistant

Responsibilities:

• PhD student, Computer Science in Confidential, Analyzing and designing intelligent system for Health Canada for monitoring nuclear explosion and pollution.
• Using Machine Learning and Data Mining methods for designing and implementing this intelligent system;
• The research was done with two year research contract with Government Health Canada using C/C++, C#, J#, .Net, and Weka Machine Learning Package, and Eclipse, knowledge of EJB, WebSphere, Weblogic, AJAX, and SOAP.

Confidential

Research assistant

Responsibilities:

• The research focuses on efficient methods for searching for non-redundant association rules from large transaction databases using Java and C++;
• 3D graphics and dynamics using OpenGL and C++; simulation of spacecraft dodging meteorites with artificial intelligence technology;