1. Shaaban Mahran Abbady
PhD Student, Data Mining and Dig Data
Software development experience
shaabanmahran@hotmail.com
smr5476@louisiana.edu
(337) 852-3265
Executive
Summary
PhD Student University of Louisiana at Lafayette
Research Assistant Center of Visualization and Decision Informatics
(CVDI)
Research Interests Anomaly Detection, Big Data, Link Prediction,
Stream Mining, Graph Data Mining
Industry Database Design & Development, Business Intelligence,
Experiences Web Development
Education Ph.D Student, Computer Science, University of Louisiana at Lafayette, USA,
(expected to graduate in 2016)
M.Sc in Computer Science, University of Louisiana at Lafayette, USA, 2013,
(GPA 3.92)
M.Sc in Computer Engineering, Arab Academy for Science & Technology,
Egypt, 2009 (Thesis: Accelerating Density-Based Clustering)
B.Sc in Computer Science & Engineering, Alexandria University, Egypt
Points of
Interest
LinkedIn
Databases, OLAP, Data Warehousing, Business Intelligence, Data Mining,
Machine Learning, Big Data, Visualization, and Data Science
www.linkedin.com/pub/shaaban-mahran/35/590/990/
Skills
Programming
Languages
Data Tools
Visualization
Java, C#, Scala, Sybase PowerBuilder, Functional Programming (Scheme),
ASP, JavaScript, HTML5, CSS
R, SQL, Hadoop, Spark, Matlab, Shell Scripting, Excel, Python, GraphLab
D3.js, jQuery, OpenLayers.js, Leaflet.js
Databases IBM Informix DS, Microsoft SQL Server, MySQL, Hbase
BI
OS
Version Control
Languages
MS SQL Server Analysis Services (SSAS)
Windows, Unix/Linux (Ubuntu)
Git
English (Professional proficiency), Arabic (Native)

2. Work Experience:
Jan 2012 - Now: Center of Visualization and Data Informatics, CVDI
(An NSF research institute specialized in big data, Louisiana, USA)
ResearchAssistant (Graduate Student) in the following four projects:
 [August 2015 – Now]:
"Online Mining for Association Rules and Collective Anomalies in Data Streams"
Develop a framework for online association rules discovery and detecting sequence
based anomalies. The framework has a multi-layer stack of technologies that support
distributed processing for historical data, and stream processing for incoming fast
streams. End goal is to deliver near real-time response with satisfactory accurate results.
A lot of big data technologies are involved such as Hadoop, HBase and Spark.
 [August 2014 – June 2015]:
"Hotspot Detection and Prediction in Spatiotemporal Data" Develop a predictive
analytic for detecting emerging hotspots; areas of space and time with unusually high
incidences of events; and further to predict the evolution of those hotspots. Case studies
include disease outbreaks and crime outspread. For scalability, we use MapReduce.
 [Sep 2013 – June 2014]:
"A Spatiotemporal Data Mining Approach for Fraud Detection" Adapt a scalable
spatial-temporal unsupervised techniques into anomaly detection framework. New
parallel algorithms, adapted for the spatiotemporal context has been developed. Case
studies included weather data, medical claims and tax data. Assigned tasks included:
research, developing new algorithms, data preprocessing, synthetic data generation,
geo-coding, visualization, documentation and publication
 [Sep 2012 – June 2013]:
"Real-Time Analysis and Visualization of Multi-dimensional Sensor Data Streams"
Develop high-performance prediction techniques over data streams. Modern
technologies like Hadoop, MapReduce, and In-Memory Databases were studied to be
applied to achieve real-time response against very large data streams. (Twitter’s) Storm
was chosen as the framework for streaming big data along with Java language and R
statistical libraries for time series. MySQL with in-memory storage was used.
Oct 2014 – Dec 2014: Civil Eng. Department, University of Louisiana at Lafayette
Programmer (Visualization)
 Web development for a web site to visualize hydrological data involving maps and
front-end JavaScript frameworks including OpenLayers.js, JQuery, and CSS.
2006 – 2011: Information Center, Arab Academy for Science and Technology, AAST ,
(A multinational educational organization based in EGYPT)
Programmer / Analyst/ Business Intelligence Specialist
Helped to build, maintain, and develop a successful ERP system for AAST handling all
rapidly increasing requirements for students and staff.
 Database Applications:
-Academic Registration
-Asset Tracking
-Stock and Purchases
-Budget Control
-HR and Payrolls
IBM Informix
Dynamic Server
for Unix/Linux,
Sybase Power
Builder
Collect requirements. Design and
implement schemas. Develop client/server
applications. Test and deploy. Users
training and supporting. Documentation.
Database Tuning.
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3.  Web Applications:
Students Portal
(Registration,
Tuitions,
Student Reports)
DHTML,
JavaScript,
ASP
Migrate client/server applications to web
based, and create new ones. Design and
implement front and back ends, focus on
back end. Test, optimize, and secure sites.
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 Business Intelligence Applications:
-Students Registration
& Education
-Health Care
-Budget Control
SQL server
Analysis
Services
+ASP.net
Collect requirements, design warehouse,
resolve issues in legacy data, develop ETL
scripts, design and build cubes, MDX
queries, design dashboard and write
documentation
Courses
MOOC
Information Visualization, Data Mining, Algorithms, Advanced Programming
languages, Databases, Advanced Operating Systems, Statistics, Pattern
Recognition, Computer Networks, Computer Architecture.
 Mining of Massive Datasets (Stanford)
 Data Science Specialization (Johns Hopkins)
 Big Data Specialization (University of California, San Diego)
 Machine Learning Specialization (University of Washington)
Publications  Using Grid for Accelerating Density-Based Clustering. 8th IEEE
International Conference in Computer Technology, CIT2008, Sydney, July
2008.
 Spatio-Temporal Outlier Detection: Did Buoys Tell Where the Hurricanes
Were?, (Accepted in Papers in Applied Geography, 2016)
 A Parallelized Framework for Outliers Detection in Spatiotemporal Data.
(Submitted to Transactions on Knowledge Discovery from Data, TKDD)
 Spatio-Temporal Hotspots Detection Using Polygon Propagation.
(Submitted to International Journal of Geographical Information Science)