Teaching Experience

1972 - 1979

November 1972-March 1976: As a Research Scholar at the Indian Statistical Institute, Calcutta, I taught various types of courses such as Statistical Inference, Design of Experiments, Linear Algebra.

March 1976-September 1977: At Monash University, Melbourne, Australia, I taught Mathematical Statistics, Sequential Analysis, Applied Statistics at both the undergraduate and graduate levels.

September 1977-August 1978: At the University of Minnesota, Minneapolis, I taught Statistics and Probability for Engineers at the undergraduate level. Also taught a graduate seminar course on Sequential Estimation.

August 1978-August 1979: At the University of Missouri, Columbia, I taught sections of Business Statistics. Also taught graduate courses on Mathematical Statistics, Linear Models-I, Linear Models-II, and Sequential Analysis.

Oklahoma State University: 1979 - 1985

At Oklahoma State University, Stillwater, I taught large sections (exceeding 150 students in a class) of Business Statistics as well as graduate courses on Multivariate Analysis, Sequential Analysis, Large Sample Theory, Decision Theory, Applied Regression Analysis, Theory of Sampling, Mathematical Statistics. I had supervised seven Ph.D. students and one Master's (by Thesis) student during my tenure at Oklahoma State. During the last three years of stay at Oklahoma State, I became the Chair of the Graduate Studies and Research Committee.

University of Connecticut: Since 1985

September 1987 - October 1990

I assumed the responsibilities of the Headship of the

Department of Statistics at the University of Connecticut, Storrs.

September 1985 - Present

I have been teaching Mathematical Statistics courses at the University of Connecticut, for example, Statistical Inference, Theory of Estimation, Decision Theory, Sequential Analysis, Sampling Theory, Selection and Ranking, Linear Models I & II, and Advanced Inference for the Masters and Ph.D. students in statistics and other disciplines.

• I have routinely offered graduate level service courses such as Applied Multivariate Methods, Applied Linear Regression, and Sampling Methods.

• I have taught extremely large sections (exceeding 350 students in a class) of Freshman/Sophomore level Business Statistics courses as well as moderately large sections (with enrollment around 35-40) of undergraduate courses such as Mathematical Statistics, Engineering Statistics, Statistical Methods, Sampling Methods, and Applied Regression Analysis, for the statistics majors, math-stat majors and non-majors.  

Teaching Innovation  

At the undergraduate level, I have organized two special courses. They are:

• Statistical modeling and diagnostics are emphasized with the help of hands-on and interactive SAS programming.

• Stat 252: Sampling Methods

• Data collection techniques are emphasized. A novel part of the course includes an individualized final project (equivalent to 50% of the final exam) where each student selects and formulates one specific statistical problem in his/her area of interest. This is followed by real data collection from the field. The focus is on collecting good quality data through one=s own effort. Existing data available from the internet or a nearby hospital, for example, is outright rejected. Instead a student is encouraged more to design a survey and collect relevant survey data to study some interesting problem arising in health care or insurance, for example. Each student determines the required sample size after taking both the time factor and actual cost into consideration, physically goes out to collect data, and then follows through relevant statistical analysis. Everyone submits a substantial written report.

At the first-year graduate level, I voluntarily hold a Aproblem session@ meeting once a week when I teach the mathematical statistics courses. Neither students nor I earn any credit for participation.

It is a one-semester graduate level course covering both probability and statistical inference for the Ph.D. students from graduate programs other than statistics. I designed the course and taught it the first three times it was offered (2003, 2004, and 2005) during the spring semesters. My textbook, Introductory Statistical Inference (Chapman&Hall/CRC: 2006), evolved from the lecture notes used in this course. 

It is a two-semester sequence consisting of probability topics (first semester) followed by statistical inference (second semester). This first-year graduate level sequence happens to be a core offering for our beginning Masters and Ph.D. level students as well as mathematically serious students from other programs. In the weekly held Aproblem session,@ I solve Aproblems@ and reinforce important concepts with additional Aexamples@ C or I may generalize an approach or give alternative ways to solve some previously tackled problem. On some days, I may ask the class to work on a problem and then the whole discussion may revolve around the pros and cons of all sorts of different ways to attack that problem. These are Alive sessions.@ I encourage all students to think Aloudly.@ If a mistake is made, I explain the nature of the mistake and discuss ways to avoid making similar mistakes in the fiuture. The weekly Aproblem session@ is purely voluntary, but students rarely miss a Aproblem session.@ Through this involvement, students learn to appreciate the value of rigorous mathematical and statistical Athinking.@ My textbook, Probability and Statistical Inference (Marcel Dekker/Taylor & Francis: 2000), evolved from the lecture notes used in this course. 

 

Last Revised: September 19, 2007

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