Dr. Steven C. Lindo, D.P.S.

Summary

My Background and Experience

I attend Hofstra University where I earned a Bachelors and a Masters Degree in Computer Science. I later earned my Doctor of Professional Studies, Doctorate from Pace University, Seidenberg School of Computer Science. I worked for 14 Year for (Thomson) Reuters and for the past 16 years I have been working for Wolters Kluwer. I started teaching at Hofstra University in 2015. I have taught the following:

Undergraduate Courses

• CSC 015 Fundamentals of Computer Science
• CSC 016 Data Structures / C++
• CSC 145 Social Media Data Mining
• CSC 150 Semantic Web Programming
• CSC 156 Intro to Machine Learning
• CSC 157 Data Mining
• CSC 163 Computer Ethics
• CSC 197 Senior Design
• CSC 197 Senior Seminar

Graduate Courses

• CSC 250 Semantic Web Programming
• CSC 272 Machine Learning
• CSC 273 Data Mining
• CSC 291 Social Media Data Mining
• CSC 291W SP TPC: Adv Neural Networks
• CSC 300 Independent Projects

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Research Interests

My Philosophy: Computer Science is the study of all things computing. It is the study of systems, processes and methods for the purpose of delivering computing solutions to problems. It is a discipline in engineering for analysis, designing, and developing and delivering technology.

• Cognitive Computing
• AI & Machine Learning
• Big Data & Data Analytics
• Quantum Computing

"If we knew what it was we were doing, it would not be called research" - Albert Einstein

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Publications

• “Big Data: A Foundational Explanation”, Student-Faculty Research Day, Seidenberg School of Computer Science and Information Systems, Pace University, White Plains, NY, 2015, Richard J. Barilla, Sam Elnagdy, Steven Lindo, and Michael J. Salé,
• “Big Data Machine Learning Algorithms”, Student-Faculty Research Day, Seidenberg School of Computer Science and Information Systems, Pace University, White Plains, NY, 2015 Gilbert Alipui, Claude Asamoah, Richard Barilla, Leigh Anne Clevenger, Alecia Copeland, Sam Elnagdy, Hugh Eng, Michael Holmes, Saravanan Jayaraman, Kevin Khan, Steven Lindo, Javid Maghsoudi, Mantie Reid, Michael Salé, and Charles C. Tappert,

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Dissertation Abstract

Link to full dissertation

A Comparative Study of Collaborative Filtering Recommendation Systems Using Algorithms to Impute Large Sparse Matrices

In the modern era of computing, recommendation systems are a key component for enterprise systems and consumer applications including e-commerce and web applications. The challenge for these systems is the accuracy and quality of the calculations especially when dealing with sparse amounts of data. This research provides an empirical study of the problems associated with a sparse matrix when encountered by collaborative filtering recommendation systems. The research conducts a comparative analysis of different algorithms used to address the issue of sparse data while trying to predict and prescribe (recommend) the optimal choice to users. The research will compare statistical techniques used to impute missing data with other estimations techniques for predicting missing values. The research will show why Matrix Factorization, Maximum Likelihood Estimation (MLE) or Gradient Optimization methods work better for large sparse matrices, over simple mean, sub-group mean or regression methods.