Zexin Wan is a Master's student in computer science at Rensselaer Polytechnic Institute expected to graduate in May 2016. He has relevant coursework in parallel programming, networks, machine learning, databases, AI, and cryptography. His projects include developing a cryptocurrency system, analyzing space colonization data, designing a secure online banking system, and creating an open source card game framework. His skills include C, C++, Python, Java, JavaScript, databases, Linux, Eclipse, and Git. He has received academic honors and leads the Rensselaer Center for Open Source Software.
Profile Summary
14 years of Total Experience in Python Development
10 Years in Leading Teams, Scrum Master and Management
8 Years of experience as Solution Architect in multiple projects.
Open source Contributor in Python Software Foundation
Research & Development, Proof of Concepts, SDLC process
Gathering information from Clients directly and Reporting
Agile Methodology and Cloud Technology SME
Corporate Trainer for Python, Flask and Agile
Conducting Interviews for Python, Linux, C++
Domain Exposure: Banking, Finance, Digital, Network Security, Energy, CFD,
HPSA, Server Automation
Presentation of the Semantic Knowledge Graph research paper at the 2016 IEEE 3rd International Conference on Data Science and Advanced Analytics (Montreal, Canada - October 18th, 2016)
Abstract—This paper describes a new kind of knowledge representation and mining system which we are calling the Semantic Knowledge Graph. At its heart, the Semantic Knowledge Graph leverages an inverted index, along with a complementary uninverted index, to represent nodes (terms) and edges (the documents within intersecting postings lists for multiple terms/nodes). This provides a layer of indirection between each pair of nodes and their corresponding edge, enabling edges to materialize dynamically from underlying corpus statistics. As a result, any combination of nodes can have edges to any other nodes materialize and be scored to reveal latent relationships between the nodes. This provides numerous benefits: the knowledge graph can be built automatically from a real-world corpus of data, new nodes - along with their combined edges - can be instantly materialized from any arbitrary combination of preexisting nodes (using set operations), and a full model of the semantic relationships between all entities within a domain can be represented and dynamically traversed using a highly compact representation of the graph. Such a system has widespread applications in areas as diverse as knowledge modeling and reasoning, natural language processing, anomaly detection, data cleansing, semantic search, analytics, data classification, root cause analysis, and recommendations systems. The main contribution of this paper is the introduction of a novel system - the Semantic Knowledge Graph - which is able to dynamically discover and score interesting relationships between any arbitrary combination of entities (words, phrases, or extracted concepts) through dynamically materializing nodes and edges from a compact graphical representation built automatically from a corpus of data representative of a knowledge domain.
Profile Summary
14 years of Total Experience in Python Development
10 Years in Leading Teams, Scrum Master and Management
8 Years of experience as Solution Architect in multiple projects.
Open source Contributor in Python Software Foundation
Research & Development, Proof of Concepts, SDLC process
Gathering information from Clients directly and Reporting
Agile Methodology and Cloud Technology SME
Corporate Trainer for Python, Flask and Agile
Conducting Interviews for Python, Linux, C++
Domain Exposure: Banking, Finance, Digital, Network Security, Energy, CFD,
HPSA, Server Automation
Presentation of the Semantic Knowledge Graph research paper at the 2016 IEEE 3rd International Conference on Data Science and Advanced Analytics (Montreal, Canada - October 18th, 2016)
Abstract—This paper describes a new kind of knowledge representation and mining system which we are calling the Semantic Knowledge Graph. At its heart, the Semantic Knowledge Graph leverages an inverted index, along with a complementary uninverted index, to represent nodes (terms) and edges (the documents within intersecting postings lists for multiple terms/nodes). This provides a layer of indirection between each pair of nodes and their corresponding edge, enabling edges to materialize dynamically from underlying corpus statistics. As a result, any combination of nodes can have edges to any other nodes materialize and be scored to reveal latent relationships between the nodes. This provides numerous benefits: the knowledge graph can be built automatically from a real-world corpus of data, new nodes - along with their combined edges - can be instantly materialized from any arbitrary combination of preexisting nodes (using set operations), and a full model of the semantic relationships between all entities within a domain can be represented and dynamically traversed using a highly compact representation of the graph. Such a system has widespread applications in areas as diverse as knowledge modeling and reasoning, natural language processing, anomaly detection, data cleansing, semantic search, analytics, data classification, root cause analysis, and recommendations systems. The main contribution of this paper is the introduction of a novel system - the Semantic Knowledge Graph - which is able to dynamically discover and score interesting relationships between any arbitrary combination of entities (words, phrases, or extracted concepts) through dynamically materializing nodes and edges from a compact graphical representation built automatically from a corpus of data representative of a knowledge domain.
1. Zexin Wan
1906 River View Road, Troy, New York 12183
zexin.wan@gmail.com 518-380-4817
Education:
Rensselaer Polytechnic Institute (RPI), Troy, NY Expected: May 2016
Master of Science in Computer Science GPA: 4.0/4.0
Bachelor of Science in Computer Science GPA: 3.24/4.0
Relevant Courses: Parallel Programming, Network Programming, Machine learning, Database System, Intro to Artificial
Intelligence, Crypt & Network Security, and Graph Theory.
Project Experience:
Cryptocurrency Maintenance and Malleability spring 2015
• Researched and performed an attack on a cryptocurrency system (an easy version of bit coin but with a center authority in
the system). Used the meet in middle attack to obtain other groups’ signatures for implementing fake coins to fool the CA.
Placed first in class.
• Skills/programming languages: C++ with crypto++, bitcoin technology, hard core bit, Ideal Lattices.
Space Colonization and Galaxy Research spring 2015
• Demonstrated and innovated a Salsa program (A language invented by Professor C. Varela) to analyze space colonization
problems using three-dimensional data from Sloan Digital Sky Survey. In order to achieve max efficiency, an actor-oriented
program was built to support multi-threads computing within single machine and distributed tasks to groups of computers
over Internet.
• Skills/programming languages: Java, Salsa, distributed system, cloud computation, concurrent programming.
Secured Online Banking System fall 2014
• Designed and implemented a secured online banking system that supports up to 32 ATMs which prevents users from
plain text attack, man in middle attack, cipher text attack and etc. Survived all opponents’ attempts of attack. Placed first in
class.
• Skills/programming languages: C++, padding, RSA public key encryption, Diffie Hellman exchange.
Open Source Card Game Framework spring 2014
• Developed and demonstrated an Open Source card game framework that help people to design and customize their own
version of Yu-Gi-Oh or similar style of trading card games. Helped on project management for the purpose of assessing the
needs of end users and breaking complex problems into addressable parts.
• Skills/programming languages: Java, p2p connection, MySQL, Java graphic GUI.
Parallel Programming and Scalability Analysis fall 2013
• Prototyped and documented a parallel string matching program that could run on IBM Blue-Gene Q server and performed
100 times better than a regular string matching program.
•Skills/programming languages: C, Open MPI, Pthread, scaling multi-threads program.
Online Chat Room fall 2013
• Designed and implemented a web built-in chat room server & client based on TCP that supported at most 64 people for
group or individual texting & picture sending.
• Skills/programming languages: C, JavaScript with jQuery library, CSS, HTML5.
Skills:
• Programming Languages: C, C++, Python, Java, JavaScript and relevant libraries, HTML5, CSS and Delphi.
• Platform: Working knowledge of environments including Windows and Linux.
• Database Practical exposure to MySQL.
• Software: Functional knowledge of Eclipse, Visual Studio, VMware and LaTex. Familiarity with Git and Photoshop.
Leadership and Awards
(RCOS) Rensselaer Center for Open Source Software, group leader January 2014-Present
RPISEC (Cyber Security Group), Member January 2015-Present
• Dean’s List of Distinguished Students January 2013-Present
• AMC 12B (American Mathematics Competition) first place in Maine. May 2011