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Dr. Joseph Caruso

Faculty Fellow

Bisgrove Hall, Second Floor, Room 254J

Office Hours:
M: 10 a.m. - 12 p.m.
W: 10 - 12 p.m.
T: 9:30 - 10:30 a.m.
TH: 9:30 - 10:30 a.m.

Phone: 716.286.8143


Dr. Joseph Caruso


I am currently in my 8th year of teaching at Niagara University.  As a Faculty Fellow at Niagara University, my IBM experience allows me to give students a “hands on” approach to solving business problems.   In 2015 I was hired into the College of Business and am currently in the Finance/Economics Department.

My career at IBM began in 1978.  At that time Poughkeepsie was the mainframe capital of the work and I began my journey as a systems analyst in the production control area.  During my career at IBM I took advantage of all opportunities that were offered to me.  I worked as a systems analyst, materials manager, statistician, market researcher, quality analyst and forecaster. Along the way I compiled an impressive list of credentials:

  • PhD in Engineering Systems at Union College, NY.
  • President of the Mid Hudson Valley Chapter of the American Statistical Association.
  • published several external articles in the areas of software reuse and reliability.
  • US patent in the area of survey analysis.

I ultimately attained a rank of senior systems analyst at IBM which was one step below director level.  I was generally known as the “quantitative” analyst and often assisted other staff people with quantitative analysis.

Focus of Teaching

My focus of teaching at NU is Business Analytics.  I have developed unique syllabus for Business Analytics where lecture is supplemented by three team presentations.   I act as a consultant to students on project teams for a richer learning experience.

  • Students are challenged with a descriptive statistics project with simulated data sets that resemble actual business data.  Students analyze the data and generate graphics, tables and confidence intervals in Excel and/or R Studio.
  • Students present chi-square analysis of contingency tables created by me where tables of data are generated using interactions and white noise so that all teams are on a level playing field.
  • Student teams present stepwise regression and a regression tree analysis at the end of the semester using R Studio to introduce them to machine learning.

Current Involvement

  • Member QAAC Committee
  • Advisor for Manhattan College Business Analytics (MCBAC) Competition

Educational Background

  • Ph.D. in Administrative and Engineering Systems, Union College 1992
  • MS in Agricultural Economics and Operations Research,
  • Penn State University 1978
  • BA in Economics, SUNY at Stony Brook, 1974