Statistical design of experiments, originated from agricultural applications, is used extensively in a wide range of scientific and industrial investigations. Experiments need to be properly designed so that valid information can be extracted at a lower cost. I am interested in efficient experimental designs and the related construction and combinatorial problems. Currently, I work mostly on design of experiments in the situation where the response depends on a large number of factors (variables), the so called factorial design. When a large number of factors have to be studied, but the experimental runs are expensive, it is not feasible to observe all possible combinations of the factors. For example, with just two settings for each factor, an experiment with 10 factors requires 2^10 runs to observe all the combinations. One aspect of my research deals with how to choose a "good" small subset of the factor combinations. There are interesting connections with combinatorics, coding theory and finite geometry.