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Faculty

Bharatendra Rai

Professor of Decision and Information Sciences, Charlton College of Business, University of Massachusetts Dartmouth

  • Education

    PhD, Wayne State University

Dr. Bharatendra Rai is professor of business analytics in the Charlton College of Business at the University of Massachusetts, Dartmouth. He teaches courses on applied deep learning, analyzing big data, business analytics and data mining, applied decision techniques, and operations management.

His current research interests include machine learning and deep learning applications. He received PhD in industrial engineering from Wayne State University, Detroit. His two master’s degrees include specializations in quality, reliability, and operations research from Indian Statistical Institute, India.

He has over 20 years of consulting and training experience that includes industries such as automotive, cutting tool, electronics, food, software, chemical, and defense in the areas of data science, machine learning, supply chain, design of experiments, and Six Sigma using technologies such as R/RStudio software.

His work experience includes over five years at Ford in the area of data analytics, quality, reliability, and Six Sigma. His research publications include journals such as the Institute of Electrical and Electronics Engineers (IEEE) Transactions on Reliability, Reliability Engineering and System Safety, Quality Engineering, International Journal of Product Development, and International Journal of Business Excellence.

He has been keynote speaker at conferences and presented his research work at international conferences. Rai received an Employee Recognition Award by the Ford Asian Indian Association (FAIA) for PhD dissertation in support of Ford Motor Co.

He is certified as an International Organization for Standardization (ISO) 9000 and ISO 14000 lead assessor and Six Sigma Black Belt from American Society for Quality (ASQ).

Course

Rai teach STAT 109 Introduction to Statistical Modeling, which covers topics in statistical inference and examines statistics and data analysis.

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