Day 2 :
Keynote Forum
Mikhail Moshkov
King Abdullah University of Science and Technology, Saudi Arabia
Keynote: Multi-stage optimization of decision trees: two applications
Biography:
Mikhail Moshkov is Professor in the CEMSE Division at King Abdullah University of Science and Technology, Saudi Arabia since October 1, 2008. He has earned his Master’s degree from Nizhni Novgorod State University, received his Doctorate from Saratov State University, and habilitation from Moscow State University. From 1977 to 2004, He was with Nizhni Novgorod State University. Since 2003, he has worked in Poland in the Institute of Computer Science, University of Silesia, and since 2006, also in the Katowice Institute of Information Technologies. His main areas of research are complexity of algorithms, combinatorial optimization, and data mining. He is the author or coauthor of five research monographs published by Springer
Abstract:
Multi-stage optimization of decision trees is one of the extensions of dynamic programming. It allows us to optimize decision trees sequentially relative to a number of cost functions. We will discuss two applications of this technique: finding of minimum average depth of a decision tree for sorting eight elements and creation of an algorithm for reduct minimization. The question about minimum average depth of a decision tree for sorting of eight elements was open since 1968 and was considered by D Knuth in his famous book The Art of Computer Programming, Volume 3, Sorting and Searching. Reduct is a minimal set of conditional attributes in a decision table which gives the same information about decision attribute as the whole set of conditional attributes. The problem of reduct minimization is closely connected with the feature selection. The end of the presentation is devoted to the introduction to KAUST
Keynote Forum
Herman Ray
Kennesaw State University, USA
Keynote: Unbiased estimation after phase II clinical trials involving multiple endpoints
Biography:
Herman Ray has received his PhD from the University of Louisville, where he has conducted research at the JG Brown Cancer Center. He currently has several manuscripts published in clinical trial design and bioinformatics as well as STEM education policy in the secondary education system. He is now an Associate Professor of Statistics at Kennesaw State University as well as the Director of the Center for Statistics and Analytical Research which is housed within the new Analytics and Data Science Institute
Abstract:
The single arm, two-stage clinical trial design is a popular methodology to evaluate oncology treatments in the phase II setting. The designs are typically augmented with an ad hoc toxicity monitoring rule which is imposed outside of the formal two-stage design but there are also several designs that formally incorporate both endpoints simultaneously. There are many problems that prevent the designs from being used in practice which includes point estimation after the execution of the study. We will examine an unbiased estimator that accounts for both endpoints simultaneously along with the correlation between the endpoints. The behavior of the estimate is examined through simulation studies. It is compared to the maximum likelihood estimator