Download Data-Science-Predictive

MOHAMMED ALI H. LSKAAFI
357 E 27th St , Erie City, PA 16504
[email protected]
269-873-3540
OBJECTIVE
To obtain Applying for Advanced Analytics position that will challenge me
and allow me to use my education, skills and past experiences in a way that
is mutually beneficial to both myself and my employer and allow for future
growth and advancement.
SUMMARY OF
QUALIFICATIO
NS
My experience over 5 years is in the design, implementation predictive
modeling frameworks and data mining for develop software, and algorithms
with applications to complex dynamic business problem, life insurance,
supply chain modeling, healthcare operations, and finance. My fields of
expertise include statistical process monitoring, parametric/non-parametric
modeling, and system identification. Previous works experiences have
provided me with the skills, market preparation, and entrepreneurial spirit
necessary for a successful career.
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Utilizes advanced statistical analysis and machine learning
techniques to create predictive models to support objectives of
business units. Performs model validation and measures the impacts
of proposed models.
Developed next generation technologies for on-line fault diagnosis,
failure prognosis, and fault tolerant controls for primary.
Provide forecasting and statistical subject matter expertise and use
state of the art forecasting techniques along with traditional methods
to provide best in class solutions. Determine the appropriate
forecasting method and statistical modeling techniques for varying
businesses.
Mine massive amounts of data and perform large-scale data analysis
to derive useful business insights into product, customer, and market
behavior
Understand business problems, translate them into quantitative
models, and choose the best applicable statistical and analytical tools
to drive a solution.
Develop software, algorithms and applications to apply mathematics
to data, perform large scale experimentation and build data driven
apps to translate data into intelligence, solve a variety of business
problems and enable business strategy Inform, influence, support,
and execute our business decisions and product design.
Works with large and multiple data sets to process data in the
required format for the analysis.
Analyze and explore data to help discover hidden business insights in
the data
Develop a conscience of linear optimization, network modeling,
forecasting and queuing theory.
Develop next-generation analytic approaches where current
generation approaches are inadequate.
Use predictive modeling, statistics, Machine Learning, Data Mining,
and other data analysis techniques to collect, explore, and extract
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PROJECT
SECTION
insights from very large scale structured (mainly) and unstructured
data.
Proficient user of SAS JMP,Teradata, NCSS and MATLAB and R
analytical software for big data analytics.
High level of autonomy and sense of personal responsibility in
achieving success. Strong desire for expansion of current skillset in a
fast-paced work environment and willingness to learn a new Software
such as SPSS, StatPlus, , SAS and SAS Enterprise Miner, T-SQL,
HTML, CSS, XML, ASP.NET, C++. C.
Experience with articulating the overall story derived from data and
analysis and explaining complex analyses and themes to both nontechnical and technical audiences
• Forecasting of Visitors Staying Overnight at Grand Canyon National
Park
• Using an Analysis of Support Vector Machines for Credit Risk
Modeling.
• Uncertainty Analysis for the Auto Associate Kernel Regression
(AAKR) method model for Credit Risk estimation.
• Applications of Least of Support Vector Machines in the Evaluation of
Client Credibility.
• The General Path Model with Bayesian updating for estimation price
home.
• Geographic weighted regression to modeling location business
problem
• Market Basket Analysis of Grocery Store Data and
• Call Center Scheduling Problem and Solution
• K-means Clustering on a Classifier-Induced Representation Space:
Application to Customer
Contact Personalization
Tools : MATLAB ,R SAS Enterprise
Fault Diagnosis and Failure Prognostics of Lithium-ion Battery based
on Least Squares Support Vector Machine and Memory Particle Filter
Framework August 2015.
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The main purpose of the research was to develop novel data driven
framework for fault diagnosis and remaining useful life (RUL)
prognostics for lithium-ion batteries.
The feasibility of the framework is validated using Li-ion battery
prognostic data obtained from the National Aeronautic and Space
Administration (NASA) Ames Prognostic Center of Excellence
(PCoE).
The aim of this novel framework is to improve the accuracy of
incipient and abrupt faults diagnosis and failure prognosis using Least
Square Support Vector Machine (LS-SVM) and Memory-Particle
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Filter (M-PF).
The experimental results show the following: the proposed diagnostic
approach provides an effective way of diagnosing Li-ion battery fault;
(2) the proposed prognostic approach can predict the RUL of Li-ion
batteries with small error, and has high prediction accuracy; and, (4)
the proposed prognostic approach shows that historical failure data
can be used instead of a physical failure model in the particle filter.
Tools/Hardware: SAS JMP,Teradata, NCSS and MATLAB and R
Social Proof Marketing and Advertising: empirical modeling for and
marketing and analysis diagnostics January 2016
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The objective in this study is to built-in understanding of
business problem, increasing customer value by integrating data
mining and campaign management software.
Answers include typical questions:1- Which customers are most
likely to leave your company or service?; 2- Identify which
prospects should be included to obtain a high respond to a
particular offer-3 Identify the common characteristics of customers
who buy the same products from your company.
An AutoRegressive Integrated Moving Average (ARIMA) model
predicts future values;(2) The k-means algorithm;(3) Regression
method.
Data analytics project from SocialProof Marketing and Advertising
company.
The result implies that discount price may not increase customers’
switching. May be the company should offer similar types of benefits
(discounts, coupons, etc.) through their own membership card
programs;(2) there are some areas that warrant further study. First,
data for some variables, such as account tenure and each company’s
size, were not available; and customers perceived values on service
satisfaction were not included in the data either Therefore, a better
model can be developed by including these variables;(3) The
company must to decide to focus on developed and improving feature
that retain customer and attractive new one.
Tools: SAS JMP,Teradata, NCSS and MATLAB and R
Online Support Vector Regression Approach for the Monitoring of
complex system April 2013
 The objective in this study is to presents an application of accurate
online support vector regression (AOSVR) approach that efficiently
updates a trained predictor whenever a new sample is added to the
training.
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The feasibility of the framework is validated using Li-ion battery
prognostic data obtained from the National Aeronautic and Space
Administration (NASA) Ames Prognostic Center of Excellence
(PCoE).
The results show that the approach is effective for online machine
condition monitoring where it is usually difficult to obtain sufficient
training data prior to the installation of the online systems.
Tools/Hardware: SAS JMP,Teradata, NCSS and MATLAB and R
The General Path Model with Bayesian updating for Remaining
Useful Life estimation May 2014
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The objective in this study is to implement a dynamic Bayesian
updating methodology is introduced to incorporate prior
information
in
the Bayes General Path Model ( BGPM)
methodology.
The feasibility of the framework is validated the simulated turbofan
engine data provided in the 2008 Prognostics and Health
Management Conference Prognostics Challenge.
A comparison of the application of the traditional GPM method
and the proposed GPM/Bayes method showed that the latter was
able to make prognostic estimates in simulated turbofan the former
method was often unable to make RUL estimates very early in
life. In addition, the GPM/Bayes model had smaller Mean Absolute
Percent Error (MAPE) for predictions made early in system life; as
more information became available, the error of the two
methods converged because in both cases prognostic estimates
were based primarily on the available prognostic parameter values
with little emphasis on the prior information.
Tools/Hardware: SAS JMP,Teradata, NCSS and MATLAB and R
Uncertainty Analysis for the Auto Associate Kernel Regression (AAKR)
method model - March 2013
1- Forecasting of Visitors Staying Overnight at Grand Canyon
National Park
2- Data Anomaly Detection Using Principal Component Regression
and Auto Associative Kernel Regression.
3- Development and Application of Instrument data anomaly
detectability for Pressure Swing Adsorption system (PSA) in the
nuclear industry.
Tools/Hardware: SAS JMP,Teradata, NCSS , CPLEX Optimizer MATLAB
and ,MS Access, MySQL, Python, and R
WORK
EXPERIENCE
Office Manager
05/ 2005 – 12/2007
General Construction and Oil AL- Abbad Company, Saudi Arabia
 Transform tabular data into meaningful executive-level charts such as
price waterfalls, scatter plots, box plots, heat maps and other graphic
types.
 Produces reports and tools for automation report generation using
appropriate business metrics or indicators and provides to
management as requested.
 Managed financial resources for projects.
 Scheduled the project in logical steps and budget time required to
meet deadlines.
 Interpreted and explained plans and contract terms to administrative
staff, workers, and clients.
 Prepared contracts and negotiated revisions, changes and additions
to contractual agreements with architects, consultants, clients,
suppliers and subcontractors.
 Studied job specifications to determine appropriate design or
construction methods.
 Prepared and submitted budget estimates and progress and cost
tracking reports.
 Developed and implemented quality control programs.
Assistant Director of Administrative and Financial
01/ 2001- 08/ 2005
General Construction and Oil AL- AL –Dubaisi Est., Saudi Arabia
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Prepared a draft budget bureau in cooperation and coordination with
other departments.
Provided the department of follow-up in the court data and
documents needed to audit the accounts of the Bureau.
Took the necessary measures to prepare tenders and contracts for
the Bureau of Procurement.
Prepared periodic reports for management activities, achievements
and proposals for the development of work and referred to the
Director General of Administrative and Financial Affairs.
Supervised the preparation and issuance of reports and periodic
financial statements of the enterprise
Developed a data topology based on the data storage, various
replications, and movements of data.
Prepared business flow diagrams and defined the workflows.
Prepared periodic reports for management activities, achievements
and proposals for the development of work and referred to the
Director General of Administrative and Financial Affairs.
Successfully created and managed a conversion testing effort which
included a data quality review, two system test cycles, and user
acceptance testing.
Perform specific analyses to support business decisions.
Provided the department of follow-up in the court data and
documents needed to audit the accounts of the Bureau.
EDUCATION
PhD. Industrial and Systems Engineering, University of Tennessee,
Knoxville
Doctorate in mathematics and statistics, with a concentration in multivariate
statistics and Bayesian modeling. The research interests include developing
hybrid-modeling techniques, which combine traditional statistics and machine
learning.
August 2011 – December
2015
Major GPA: 3.61/4.0
M.S. Engineering Management, Gannon University, Erie, PA
2010
Major GPA: 3.30/4.0
December
M.S. Statistics Program, University of Tennessee, Knoxville, M.S. in
Statistics
December 2015
G.C. Reliability and Maintainability Engineering Program, University of
Tennessee, Knoxville, Graduate Certificate
August 2015
COMPUTER
SKILLS
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LANGUAGES
Jump statistical discover, NCSS
MS Excel, Word, PowerPoint
MS Access, MySQL, Oracle
Intermediate R, AERNA
Simulation, Python.
Beginning SAS, C++, and VB.
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CPLEX Optimizer
MATLAB, Mathematica
Windows, Linux, Mac
Arabic: Native language
English: fluent
PROFESSIONA
L SOCIETY
MEMBERSHIPS
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Prognostics and Health Management Society
Society for Maintenance & Reliability Professionals
International Association for Quantitative Finance
International Society for Bayesian Analysis
AWARDS AND
HONORS
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Awarded Full Scholarship from Saudi Government