Skip to main content

Contact

Proprietor:

Jeffrey S Strickland, Ph.D.
jeff@Humalytica.com
jeff@simulation-educators.com
Connect with Jeff on LinkedIn
Connect with Jeff on Google+


Work history


Clarity Insights
Senior Analytics Consultant, 2017 - Present
Senior Predictive Modeler for an undisclosed client in the Financial and Insurance Industry. Performs modeling projects from cradle to grave, including business case and modeling objective development, requirements collection and analysis, model construction, performance evaluation, economic evaluation, model deployment and model validation support. Construct logistic regression model, neural network models, ensembles and uplift (net-lift) models. Programs in SAS and R, uses SAS Enterprise Miner and IBM SPSS Modeler.
Clarity Solution Group
Senior Analytics Consultant, 2012 - 2017
Senior Predictive Modeler for an undisclosed client in the Financial and Insurance Industry. Performs modeling projects from cradle to grave, including business case and modeling objective development, requirements collection and analysis, model construction, performance evaluation, economic evaluation, model deployment and model validation support. Construct logistic regression model, neural network models, ensembles and uplift (net-lift) models. Programs in SAS and R, uses SAS Enterprise Miner and IBM SPSS Modeler.
Northrop Grumman
Senior Operations Research Analyst, 2008 - 2012
Technical Director for the Threat Modeling Center (TMC) at the Missile Defense Agency (MDA). The TMC constructs all authentic threat models for every MDA simulation; Technical Director for Verification and Validation (V&V) of Modeling and Simulation (M&S) at the MDA. The V&V Directorate performed V&V oversight for all MDA models and simulations.
University of Alabama in Huntsville
Principal Research Scientist, 2007 - 2008
Technical Consultant for the Modeling and Simulation of NASA's Aries I Crew Launch Vehicle's Reliability and Launch Availability. Ares I was part of the Constellation Program before it was cancelled by the 201 NASA Authorization Bill. Ares I performed a successful launch at Cape Canaveral before the program was canceled.
SPARTA, Inc.
Chief Scientist, 2005 - 2007
Provides Scientific oversight for all modeling and simulation projects. Performed Reliability, Availability and Maintainability (RAM) design modeling simulation for Warrior Extended Range Multi-Purpose (ERMP) Unmanned Aerial Vehicle( UAV), the Hunter Class III UAV, the CLASS II UAV. Also preforms studies and analysis of space surveillance strategies for the US Army Space and Missile Defense Command.
US Army Logistic Management University
Course Director, 2002 - 2005
Course Director for the Operations Research and Systems Analysis (ORSA) Military Application Course (MAC) I resident course. Developed an ACE accredited curriculum for joint service ORSA training. Instituted SAMRT Technologies and classrooms.
United States Military Academy
Assistant Professor, 1991 - 2002
Mathematics and Operations Research Instructor

Popular posts from this blog

Time Series Analysis using iPython

Time Series Analysis using iPython
In this example, we will examine ARMA and ARIMA models with Python using the Statsmodels package. This package can be downloaded at http://statsmodels.sourceforge.net/stable/index.html. Autogressive Moving-Average Processes (ARMA) and Auto-Regressive Integrated Moving Average (ARIMA) can be called from the tsa (Time Series) module from the Statamodels package.
Note: I am not as expert in time-series analysis as I am in other areas of Analytics, so if you find errors I would be happy to know about them and correct them. Introduction ARIMA models are, in theory, the most general class of models for forecasting a time series, which can be made to be “stationary” by differencing (if necessary), perhaps in conjunction with nonlinear transformations such as logging or deflating (if necessary). A random variable that is a time series is stationary if its statistical properties are all constant over time. A stationary series has no trend, its variations aro…

Neural Networks using R

Neural Networks using RByonMay 13, 2015
The intent of this article is not to tell you everything you wanted to know about artificial neural networks (ANN) and were afraid to ask. For that you’ll have to ask someone else. Here I only intend to tell you how you might use R to implement an ANN model. One thing I will say is that I rarely use an ANN. I have found them to work best in an ensemble model (using averaging) with logistics regression models. Using neuralnetneuralnet depends on two other packages: grid and MASS (Venables and Ripley, 2002). It is used is primarily with functions dealing with regression analyses like linear models (lm) and general linear models (glm). As essential arguments, we must specify a formula in terms of response variables ~ sum of covariates and a data set containing covariates and response variables. Default values are defined for all other parameters (see next subsection). We use the data set infert that is provided by the package dat…

Clouds, clouds, and more clouds

Clouds, clouds, and more clouds There are at least eleven kinds of clouds: cirrus, cirrocumulus, cirrostratus, altocumulus, altostratus, cumulonimbus, cumulus, nimbostratus, stratocumulus, small Cu, and stratus. But this article is not about those kinds of clouds. Of course there are other kinds of clouds, like iCloud, Google Cloud, Azure Cloud, Amazon Cloud, and the list goes on. But this article is not about those clouds either. This article is about text analytics.
Clouds and Text Analytics
The picture above was generated by R as a Word Cloud. It is equivalent to a frequency distribution (see Figure 1), where the size of the characters comprising a word corresponds to its frequency count, so that the word “icloud” occurs many times in the text, while the word “people” (inside the “d” in “icloud”) occurs less frequently. In Figure 1, “speed” occurs most frequently and is a key concept in flight dynamics, and in corresponding models.