JOHN ANDREW JASPERSE

***** ********** *****

Raleigh, NC 27614

919-***-**** (h)

adwfhs@r.postjobfree.com (email)

PROFESSIONAL SUMMARY

Software development and software testing experience on HP9000 Unix workstations, Dell Windows 10 personal computers, and HP1000 RTE-A real-time minicomputers. A flexible, adaptable, and self-taught individual with good intrapersonal, good interpersonal, and broad multidisciplinary skills. Knowledge of C, Java, JavaScript, Python, Visual Basic, HTML, CSS, Fortran, SAS Language, SAS Macro Language, SAS/OPTMODEL, SAS/SQL, SAS/IML, SAS/OR, ORACLE 18c Express, ORACLE/SQL, ORACLE PL/SQL, Excel 2019, Excel Optimization Solver, Excel Analysis ToolPak, and Windows subsystem for Linux. Some knowledge of SAS/STAT and SAS/ETS for basic univariate, multivariate, econometric, and time series data analysis. Some knowledge of cloud computing basics.

SEARCHING FOR FULL-TIME OR CONTRACT WORK

Software Solutions Developer

6/2013 – Present

Runge-Kutta Numerical Analysis Solution for Systems of Differential Equations

Solution Architecture: Web browser thin client is written in HTML and CSS with enhanced UI functionality provided by JavaScript client-side; client communicates via Ajax with a JavaScript server-side server running under Node using JSON data exchange; server listens for requests submitted by the client and executes various Java programs to prepare responses; user has the option to perform the back-end calculations to solve the inputted differential equations numerically using a custom programmatically generated, compiled, and executed C, Java, or Python program; graphical output specified by the user is provided by a custom programmatically generated, compiled, and executed Python program and is surfaced to the client browser in two different styles of HTML output; numerical solution raw output file, graphics files, and HTML files are transferred back to the client using Java socket protocols; numerical solution raw output is formatted as a comma-delimited file for further data analysis and visualization with SAS, Python, and Excel; client request configurations can be optionally stored in an Oracle data base for later retrieval using JDBC and JSON data exchange. The solution is used to solve problems in chemical engineering reaction kinetics and fluid flow, epidemiological models, movement through space, and population dynamics.

Data Analysis and Data Visualization of the Numerical Analysis Solution for Systems of Differential Equations

Raw data output for various systems of differential equations is further analyzed and visualized using SAS Language, SAS Macro Language, and SAS System procedures; Python and the NumPy, Pandas, and Matplotlib modules; and Excel formulas, Excel Analysis ToolPak, and Excel plots. Each method performs custom calculations using the raw data, custom statistical analyses, and custom series plots and phase portraits. SAS products used include Base SAS, and the IMPORT, MEANS, SQL, and SGPLOT procedures.

Mathematical Optimization Solution in Visual Basic and Excel

Solution Architecture: Visual Basic form specifies the location of a workbook that contains worksheets with the input data or the location of the folder that contains the comma-delimited files, the number of decision variables, the number of constraints, the number of report options, the objective function type, and the types of constraints; the form invokes a Visual Basic subroutine that populates an empty Excel spreadsheet with a linear, nonlinear, or non-smooth objective function to maximize or minimize, decision variables and the decision variable types (continuous, integer, or binary), the linear, nonlinear, or non-smooth constraints and constraint types (less than or equal to, equal to, or greater than or equal to), and the report types; the Simplex LP or the GRG Nonlinear solver is invoked programmatically and the value of the objective function, the values of the decision variables, and the reports are displayed automatically in the solver optimization output worksheets. The Evolutionary solver is invoked through the Excel interface manually. The solution can also solve a problem with an unconstrained nonlinear objective function, can allow negative values of the decision variables, and can set initial values of the decision variables to serve as a starting point for nonlinear or non-smooth problems before the Excel solver is invoked. The solution is used to solve problems in industrial engineering manufacturing planning, transportation models, blending problems, nonlinear inventory models, microeconomic consumer demand utility maximization and production cost minimization problems, investment portfolio optimization, and nonlinear least-squares parameter estimation.

Output Verification of the Visual Basic and Excel Mathematical Programming Solution Using Python

Implemented the Primal-Dual Predictor-Corrector Interior Point algorithm to verify the output of the linear programs, the Steepest Ascent algorithm to verify the output of the nonlinear unconstrained programs, the Newton-Raphson algorithm to verify the output of the unconstrained and constrained nonlinear programs, the Gauss-Newton and Levenberg-Marquardt algorithms to verify the output of the nonlinear least-squares programs, and the Golden section search to verify the output of the non-smooth programs. Also verified that the solutions to the constrained problems satisfied the Karush-Kuhn-Tucker conditions by checking that, at the optimal point, the gradient of the objective function lies in the cone formed by the gradients of the binding constraints. All of the verification programs were written in Python using the Math and NumPy modules.

Time Series ARIMA Forecasting Solution Using Python for Excel

Solution Architecture: Python UI written using Tkinter controls four modes of invocation. Mode 1 displays the original time series read from an Excel workbook or a comma-delimited file, displays an autocorrelation plot, displays a decomposition plot to visually isolate the trend and seasonality components, and performs a test for stationarity; mode 2 performs trend and seasonal differencing to determine the I (integrated) order of the series, produces an autocorrelation plot and a partial autocorrelation plot to determine the MA order and the AR order of the series respectively, and performs a test for stationarity; mode 3 performs the ARIMA or a seasonal-ARIMA on the training portion of the series, produces the diagnostic plots, performs a validation step, and calculates the mean absolute percent error (MAPE) and root mean squared error (RMSE) of the validation period; and mode 4 does the final forecasting of the series over the requested time horizon, and plots the prediction intervals of the forecast. The user has the option to display the output interactively as it is produced, or to direct the output to Excel workbooks and text files. The graphical output from each of the four modes is directed to four separate worksheets in one Excel workbook, the output from the Python ARIMA module is directed to a text file, and the predicted mean and prediction intervals are directed to a second Excel workbook. The solution is partitioned so that the Python forecasting program can be invoked through the UI as a subprocess or in batch mode from a Windows command prompt using a runstring without having to go through the UI.

WORK HISTORY

SAS INSTITUTE, INC., Cary, NC

Senior Development Tester, SAS Solutions OnDemand Department, Advanced Analytics Lab

4/2008 – 5/2013

Tested SAS Solutions OnDemand services and solutions during the quality assurance testing cycle; supported SAS Analytic Engineers on various application service provider consulting projects; tested end-to-end data integration processes for the initial loading and incremental updating of data warehouses and related data marts for various business intelligence applications; designed and developed the alert routing and alert suppression back-end modules for the SAS Social Network Analysis Server using SAS Language, SAS Macro Language, Base SAS procedures including the SQL procedure, and SAS stored processes and Web services; optimized the performance of the SAS code by using indexed, sequential, and random access methods and sort-merge, index, and hash joins; developed a set of SAS macros to verify independently the output produced by the alert detection and management process; developed, executed, and reviewed batch and interactive tests with respect to functionality, error handling, verification, and stress testing; performed automated load testing of Web-based applications using the e-TEST Suite; developed Perl scripts to automate unit testing; conducted code reviews of implemented features and technical reviews of testing work tickets.

SAS INSTITUTE, INC., Cary, NC

Senior Software Developer, Merchandise Intelligence Solutions Research and Development

9/2006 – 3/2008

Designed, developed, and enhanced SAS Pack Optimization back-end modules using SAS Language, SAS Macro Language, and Base SAS procedures including the SQL procedure; enhanced the disaggregation logic to consider on-hand and on-order inventory when applying a size profile to calculate store needs for a set of SKUs; implemented the near match size set match feature to allow partial matches of size profiles; enhanced the profile lookup logic to match the store category of the profile with the corresponding geography attribute; implemented the store to distribution center mapping capability to correct situations in which the mapping was not specified in the request; added functionality to update the profile lookup statistics table after performing a profile lookup; implemented code to detect infeasible cases before the optimizer is invoked; modified existing functionality to support changes to the underlying data model; improved system performance by analyzing the SAS logs to identify CPU-bound and I/O-bound steps and making the necessary code changes; improved optimization performance by enabling by-group processing; developed test data generation macros to create large requests for performance testing and for deriving CPU, memory, and disk space requirements for production environments; developed unit tests to exercise implemented features.

SAS INSTITUTE, INC., Cary, NC

Senior Quality Assurance Analyst, Analytical Solutions Testing Department

Quality Assurance Analyst, Statistical Products Department

7/1997 – 8/2006

Tested the Merchandise Intelligence Revenue Optimization and the SAS/OR mathematical optimization, discrete event simulation, project/resource scheduling, and decision analysis products during the development and quality assurance testing cycles; developed a mixed-integer linear program using the OPTMODEL procedure to verify

independently the pricing schedule produced by the deterministic markdown pricing optimization CLPRS procedure; developed a set of SAS macros to verify independently the output produced by the earned value management analysis macros; developed an original test design specification and set of accompanying tests for the INTPOINT procedure that utilized the Primal-Dual Predictor-Corrector Interior Point algorithm; developed an original set of test models for the discrete event simulation product QSIM; developed a set of test data generation macros to populate large data marts for testing the revenue optimization product line; tested a custom interactive Gantt chart Java applet; modified existing test design specifications to test enhancements; developed, executed, and reviewed batch and interactive tests with respect to functionality, error handling, verification, and stress testing; resolved test output differences and updated benchmarks when necessary; used the coverage analyzer to redesign tests to cover source code that was not originally executed; participated in simulation consulting projects that involved modeling systems using QSIM, designing experiments to measure the effects of the model parameters, and analyzing the multivariate simulation output using SAS Language and SAS System procedures; delivered project presentations at the SAS internal operations research seminar; implemented linear, nonlinear, and dynamic programming, matrix factorization, and critical path method algorithms using the IML matrix language procedure to enhance the understanding of the optimization techniques; reviewed software documentation and recommended changes; tracked defects and documented resolutions. Knowledge of the LP, NLP, INTPOINT, NETFLOW, CPM, GANTT, NETDRAW, and DTREE procedures. Knowledge of the QSIM and PROJMAN applications.

SAS INSTITUTE, INC., Cary, NC

Systems Developer, Internal Data Base Research and Development

Associate Systems Developer, Internal Data Base Research and Development

1/1993 – 6/1997

Designed, developed, and enhanced SAS data set and catalog I/O and data compression modules in C on HP9000 workstations; implemented generation data sets, resource environment block support, and protection of critical regions using synchronization primitives to support an intra-session pseudo-multitasking environment and asynchronous I/O for the portable data set supervisor; designed and implemented the version 7 catalog file format that utilized a record concatenation compression technique that significantly reduced catalog disk storage requirements while minimizing internal data movement; enhanced the ability of the new catalog code to support random access with variable length records; designed and implemented the version 7 catalog compatibility engine to handle version 6 catalogs; conducted technical reviews to examine implemented features; used the symbolic debugger to identify code problems and to step through error-handling code; reviewed Purify reports to resolve memory related issues; wrote test scripts using the supervisor test bed procedure to test I/O subsystem functionality and analyzed code coverage; developed a test driver to measure the effectiveness of various data compression strategies for SAS catalogs; executed and analyzed tests to measure catalog I/O performance.

FRIEDRICH AND COMPANY, Leinfelden, Germany

Systems Engineer

8/1989 – 10/1991, 5/1984 – 7/1986 (US office in Malvern, PA)

Designed, developed, enhanced, and supported Fortran computer programs for the laboratory information management system LABSAM that utilized soft real-time exclusive-writer and program-to-program communication protocols on HP1000 minicomputers; assisted in the research and development for the audit trail version of LABSAM; developed the programmatic read/write data base access product LABTOOLS that provided routines for sample login, test scheduling, result entry, result validation, data item metadata access, and auxiliary file access; devised a technique to allow multiple LABSAM systems to reside on one processor; supported Hewlett-Packard analytical data systems field organization; conducted systems analysis to define customer specific software enhancements for LABSAM and LAS, and developed cost efficient solutions. Knowledge of RTE-A system generation, NS1000 ARPA, DS1000, X.25, Datapair, LABSAM, LAS, and IMAGE.

GLAXO INC., Research Triangle Park, NC

Project Leader, Scientific Computing Department, Management Information Services

2/1988 – 7/1989

Managed a team responsible for implementing and supporting laboratory data acquisition and data management packages on HP1000 computer systems; designed HP1000 computer systems for LABSAM and LAS including hardware and software requirements for LAN, WAN, and disk-mirroring subsystems; designed and installed a

LABSAM data base for aerosol stability studies; modified a serial instrument interface to collect data and store it in LABSAM; wrote specifications for a system suitability calculations package, a front end to load stability

protocol information into LABSAM, and an LAS sequence generator; supervised the work of contractors

who developed custom software for requirements specific to Glaxo; conducted analytical chemistry methods in order to enhance the understanding of the application environment; developed Fortran computer programs to perform scientific calculations from chemical and chromatographic data.

HEWLETT-PACKARD COMPANY, Palo Alto, CA

Product Support Engineer, Scientific Instruments Division

8/1986 – 1/1988

Interfaced with research and development, marketing, quality assurance, and manufacturing departments in preparing software releases for LABSAM; coordinated on-line support to ensure proper operation and integrity of LABSAM by analyzing field reports of software problems and developing solutions; developed and taught

LABSAM courses to external customers and internal theory of operation courses to Hewlett-Packard analytical data systems engineers.

PROFESSIONAL CERTIFICATIONS

2008 Sun Certified Programmer for the Java 2 Platform 1.4

2008 Sun Certified Associate for the Java Platform, Standard Edition

2005 SAS Certified Advanced Programmer

2004 SAS Certified Base Programmer

SAS SOFTWARE TRAINING COURSES

Forecasting Using SAS Software: A Programming Approach

Multivariate Statistical Methods: Practical Research Applications

Introduction to Data Warehousing, SAS 9 Data Integration

Data Mining Techniques: Theory and Practice, SQL Processing with SAS

SAS Macro Language, SAS Macro Programming: Advanced Topics

SAS Color Graphics, SAS Report Writing: A Programming Approach

Basic Statistics Using SAS Software, ANOVA and Regression Using SAS Software

Advanced SAS Programming Techniques and Efficiencies

Fundamentals of the SAS System, SAS Programming

SAS Institute Training Center, Cary, NC

EDUCATION

1998 Master of Computer Science

North Carolina State University, Raleigh, NC

1984 Master of Engineering, Operations Research and Industrial Engineering

Cornell University, Ithaca, NY

1982 Bachelor of Science, Chemical Engineering

University of Massachusetts, Amherst, MA

GRADUATE AND ADVANCED UNDERGRADUATE

COURSES IN ECONOMICS AND STATISTICS

Price Theory, Intermediate Microeconomics, Intermediate Macroeconomics

Applied Multivariate Statistical Analysis, Statistics for Engineers I, Statistics for Engineers II

North Carolina State University, Raleigh, NC

HONORS AND ACTIVITIES

Cornell University Master of Engineering Fellowship

Cornell Manufacturing Engineering and Productivity Program Certificate

Tau Beta Pi National Engineering Honor Society, Massachusetts Zeta Student Chapter Vice President

Upsilon Pi Epsilon Honorary Computer Society

Competent Toastmaster Certificate

American Sailing Association Bareboat Chartering Standard, Berkeley, CA

American Sailing Association Basic Coastal Cruising, Annapolis, MD

PUBLICATIONS READ WEEKLY AND SEMI-MONTHLY

The Economist

New Scientist

Foreign Affairs

INTERESTS

Sailing, baseball, seashell collecting

Reading books on economics and business, science and technology, and current affairs

Developing software solutions on home computers

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