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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. 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 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. 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 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 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. 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 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 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 LANGUAGES Jump statistical discover, NCSS MS Excel, Word, PowerPoint MS Access, MySQL, Oracle Intermediate R, AERNA Simulation, Python. Beginning SAS, C++, and VB. CPLEX Optimizer MATLAB, Mathematica Windows, Linux, Mac Arabic: Native language English: fluent PROFESSIONA L SOCIETY MEMBERSHIPS Prognostics and Health Management Society Society for Maintenance & Reliability Professionals International Association for Quantitative Finance International Society for Bayesian Analysis AWARDS AND HONORS • Awarded Full Scholarship from Saudi Government