Java Sql Server

Guichong Li

Software developer, Java, Ruby, Researcher in Artificial Intelligence, Data Mining, Machine Learning,

Big Data Analyst

******@****.*******.**

Summary

• Current 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.

• Previous postdoctoral research in DRDC, CORA, Canada, for Complex Dynamic Network Analysis;

Simulation of Autonomous Underwater Vehicles; using MatLab and VB, etc.

• 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 anomaly detection, 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 education 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, EJB, AJAX, SOAP; HTML5, NoSQL;

Experience

Independent Study at IT workshop

July 2013 - November 2013 (5 months)

Summer vacation;

IT workshop from Aug. 20 to Nov. 9:

Cross Culture; Social Media; Business Analysis;

Postdoctoral Researcher at University of Ottawa

June 2012 - June 2013 (1 year 1 month)

UNIVERSITY OF OTTAWA

Postdoctoral Researcher, 2012/6-2013/06

• As a sole researcher, my research task was to develop a new algorithm for uniformly and unbiased sampling

online social networks instead of traditional methods such as Random Walk (RW) and Metropolis-Hastings

Random Walk (MHRW) in applied statistics; the new algorithm overcomes the drawbacks such as a slow

mixing time and biased results in traditional methods.

• The research was supported by NSERC Engage Grant and SME4SME Grant; I was responsible of unfolding

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successful development which eventually leads to a significant research result; thus I developed a novel

algorithm which applies advanced Markov Chain Monte Carlo methods such as coupling techniques for

sampling large graph networks;

• I achieved the goal by developing a new coupling technique called conditional independent coupling for

perfect sampling and extending traditional coupling algorithms; conducted overall 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. Research results has 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, and the product was transferred

to the local company.

TECHNICAL AND THEORETICAL ASPECTS

• Advanced Markov Chain Monte Carlo (MCMC) methods and Coupling technique in applied statistics;

Random Walk and Metropolis-Hastings algorithms; various convergence diagnosis methods such as Geweke

Diagnostic; uniformly and unbiased sampling; ...

Postdoctoral Researcher at DRDC, CORA, canada

December 2010 - December 2011 (1 year 1 month)

DRDC, CORA, CANADA

Postdoctoral Researcher, 2010/12- 2011/12

• 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 anomaly detection algorithm using Java (J#/Eclipse); it is achieved by developed a new

kernel method called ensemble kernel; the algorithm is typically one class classifier for machine learning; this

improves the traditional One-Class SVM algorithm implemented in LibSVM in Weka (a Java package for

various data mining and machine learning algorithms). This research work was published in Canadian

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Artificial Intelligence (AI) in 2012.

• Used anomaly detection techniques for information security by analyzing web log files and data transfer on

WebSphere/WebLogic; EJB, AJAX, SOAP, HTML5, NoSQL.

TECHNICAL AND THEORETICAL ASPECTS

• 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;

• MatLab, VB, social network tools and package: SNAP and Pajek, Java(J#/Eclipse), SVM, Weka; EJB,

WebSphere/Weblogic, AJAX, SOAP, HTML5, NoSQL.

Research Assistant at University of Ottawa

January 2006 - December 2010 (5 years)

UNIVERSITY OF OTTAWA

Research Assistant, 1/2006-12/2010

• Researched and developed anomaly detection techniques as a research assistance in University of Ottawa,

and cooperated with Health Canada for monitoring nuclear pollution, using Weka(data mining and machine

learning Java open source);

• 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 training datasets by

applying Markov Chain Monte Carlo methods; designed a border identification algorithm to identify borders

from training 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.

TECHNIQUES:

• Data Mining and Machine Learning algorithms, Naive Bayes, Bayesian Networks, k-Nearest Neighbors,

k-Mean, Generalized Linear Model, SVM, OCSVM; using Java/Eclipse, Weka.

• J2SE, J2EE, J2ME, WebSphere/Weblogic, AJAX, and SOAP, Jini, XML/XSLT, JAXB, SAX/DOM,

PersonalJava, JavaSpaces, HTML5, NoSQL.

Research assistant at University of Regina

February 2001 - December 2005 (4 years 11 months)

UNIVERSITY OF REGINA

Research Assistant, 2/2001-12/2005

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• 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.

TECHNICAL AND THEORETICAL ASPECTS

• 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.

Software Engineer at Institude of Computer Research and Application

January 1993 - January 2001 (8 years 1 month)

# 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, 1999 – 2001;

# As a sole developer, I developed Accounting System for future trade, mainly using PowerBuilder and SQL

server, 1994 - 1998; 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.

Technical aspects

• TCP/IP, C/C++, SQL server, PowerBuilder

Instructor at zhengzhou university

September 1987 - January 2001 (13 years 5 months)

# Employed in Department of Computer Science, Zhengzhou/HuangHe University;

# Teaching courses including RDBMS; C/C++;

Education

University of Ottawa

Doctor of Philosophy (PhD), Computer Science, 2006 - 2010

Activities and Societies: Tamale Seminar

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University of Regina

Master's degree, Computer Science, 2001 - 2004

Activities and Societies: Tamale Seminar

Southwest China Normal University

Bachelor's degree, Mathematics, 1981 - 1985

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Guichong Li

Software developer, Java, Ruby, Researcher in Artificial Intelligence, Data Mining, Machine Learning,

Big Data Analyst

******@****.*******.**

Contact Guichong on LinkedIn

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