Nino Demetrashvili is an Associate Professor at the School of Computer Science, Kutaisi International University (KIU). Her academic background and research interests broadly encompass statistics, biostatistics, and data science.
She earned her doctoral degree specializing in Biostatistics from the University of Groningen, University Medical Center Groningen (the Netherlands) in 2015. Her dissertation focused on statistical inference in variance components models and statistical learning methods for biomedical applications. She also worked as a Postdoctoral Scientist in Biostatistics at Roche Diagnostics in Penzberg (Germany), where she developed a statistical framework for the quality assurance of diagnostic assays across various measurement instruments.
Her most influential scientific contribution lies in the construction of confidence intervals for functions of variance components. In connection with this work, she won a proposal and organized an invited session at International Biometric Conference in 2018, entitled “Functions of Variance Components in Mixed Effects Models: Estimation, Confidence Intervals, Hypothesis Test, and Boundary Issues.”
Dr. Demetrashvili has authored and co-authored peer-reviewed publications with collaborators from institutions in Canada, the Netherlands, Georgia, and Germany. Her interdisciplinary approach has led to close collaborations with professionals outside the field of statistics, including biologists, clinicians, health researchers, and computer scientists.
With over 20 years of experience spanning academia, industry, and the public sector, Dr. Demetrashvili has developed expertise and competence in the methodologies and applications of statistics, biostatistics, and data science. Prior to joining KIU, Nino served as a senior (regulatory) biostatistician at Health Canada, where she was responsible for the critical evaluation of statistical components of drug submissions for Canadian market authorization and for the development of regulatory guidelines. As a result, she acquired in-depth knowledge of the drug development lifecycle.
She has taught students from diverse academic backgrounds, including statistics, computer science, and medicine.
Her research interests include statistics, biostatistics, and data science (in general); advanced statistical modeling; statistical and machine learning; clinical trial design (including superiority, non-inferiority, and equivalence testing) and related analysis methods; methodologies in pharmacometrics, epidemiology, and diagnostic assay validation; as well as statistical bioinformatics, among others. Her current research focuses on applying and developing statistical and machine learning methods to address challenges in public health and other areas of medicine.
