About Us

About Us

Exploring the world of data mining and the triumphs and challenges experienced first hand from our in-house data scientists.

See the Authors:

  • Dan Steinberg, Ph.D.
    CEO and founder of Salford Systems, the developer of the CART® decision tree, MARS® spline regression, TreeNet® gradient boosting, Breiman's RandomForests®, Friedman’s regularized regression/elasticnet package Generalized PathSeeker™️, and other influential data mining technology. After earning a PhD in Econometrics at Harvard in 1982 Dan began his professional career as a Member of the Technical Staff at Bell Labs, Murray Hill, and then as Assistant Professor of Economics at the University of California, San Diego. In 1990 he began working with Leo Breiman, Jerome Friedman, Richard Olshen, and Charles Stone on commercializing advanced machine learning in 1990 which led to both software development and consulting services. These services spanned complex modeling projects for major banks worldwide, such as CitiBank, Chase, American Express, Capital One, and Credit Suisse, and included financial services projects in Europe, Australia, New Zealand, Singapore, Malaysia, and Korea.
    Dan also led teams developing advanced predictive modeling solutions for optimal internet ad placement driving more than 1 billion impressions per day, and for grocery store demand prediction for a retail network of 160 large format stores handling more than 122,000 products. Dan led the teams that won two first place awards in the KDDCup 2000, four first place awards in the 2002 Duke/TeraData Churn modeling competition, and the teams that won awards in the PAKDD competitions of 2006 and 2007, and provided the software used by two winners of the KDDCup 2009 competition. Dan has published papers in economics, econometrics, and computer science journals, and contributes actively to the ongoing R&D at Salford.
  • Charles Harrison, Marketing Statistician
    Charles Harrison is a Marketing Statistician at Salford Systems. In this role he uses his statistical modeling and computer programming background to illustrate the practical techniques and uses of Salford Software in real-world applications in tandem with our in-house marketing team. 

    Charles is a recent graduate from the University of Central Florida, where he earned his Masters of Science in Statistical Computing with an emphasis in Data Mining. His enthusiasm for software programming started at an early age and developed into an interest in business planning and automation, with an emphasis in network traffic and forecasting. Charles worked as an intern at Sprint Nextel in their Network Planning Division and later was a Business Intelligence Intern, taking his expertise to a new level by creating an infrastructure designed to analyze M2M data using SQL. He later improved upon his own software to map key geospatial traffic components to optimize the Sprint network. This work carried over to The J.M. Smucker Company where Charles was an internal consultant to marketing teams, providing them statistical analysis for time series forecasting, geospatial, and demographic analysis.

    With advanced knowledge in machine learning, logistic regression, experimental design, and marketing, Charles provides actionable insights and valuable results to the Salford Team and translates them to an array of educational, promotional, and consulting projects. Outside of the office, Charles enjoys watching and doing sports, whether it be going to a Yankees game, his favorite team, or running in a 5k.
  • Christian Kendall, Data Scientist 
    Christian Kendall is a Data Scientist at Salford Systems. He brings more than 4 years of research expertise, with a background in physical and life science emphasizing informatics and software development. Christian graduated with a Bachelor’s degree in Chemistry from Occidental College in Eagle Rock, CA, starting with a focus on biochemistry and bioinformatics that later turned into a passion for statistical data analytics and data science. As a researcher, Christian first saw and understood the need for practical modeling applications while working on automatic target recognition at NASA and then developing code for identifying proteins in high-throughput experiments later that same year in the Yates Laboratory at The Scripps Research Institute. At NASA, Christian fixed and optimized instruments while developing analytical methods for detecting bio-interest molecules. Christian also helped to design nanometer-scale structures for the study of photovoltaics while at the California Institute of Technology using 3D modeling and finite-difference time-domain solutions to simulate light absorption. His research continued at both the Mason Laboratory at Weill Cornell Medical College in New York, and The Scripps Research Institute in California, both with a focus on analysis and preparation of DNA sequencing libraries for genomics and metagenomics. Christian’s continued interest in data, automation, software development led him to Salford Systems as a Data Scientist, where he implements machine learning and data mining techniques with our proprietary software to create practical applications for real-world problems. When he’s not crunching numbers, Christian enjoys cooking and baking, brewing kombucha, and trying to keep a lot of cacti and flowers alive.

 

A Brief History

Founded in 1983, Salford Systems specializes in providing new generation data mining and choice modeling software and consultation services. Applications in both software and consulting span market research segmentation, direct marketing, fraud detection, credit scoring, risk management, bio-medical research and manufacturing quality control. Industries using Salford Systems products and consultation services include telecommunications, transportation, banking, financial services, insurance, health care, manufacturing, retail and catalog sales, and education. Salford Systems software is installed at more than 3,500 sites worldwide, including 300 major Universities.

Exploring the world of data mining and the triumphs and challenges experienced first hand from our in-house data scientists.

About Us

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