Dr. Idika E. Okorie
Dr. Idika E. Okorie Senior Lecturer
Teaching Areas
Research Interests

Senior Lecturer, Department of Mathematics

Dr. Idika E. Okorie earned his Ph.D. in Statistics from the University of Manchester, United Kingdom (UK) in 2020, following his M.Sc in Statistics (Financial Statistics) from the University of Manchester, UK in 2014, and B.Sc. in Statistics from the Abia State University, Nigeria in 2009. Between 2012 and 2015, he served as a Graduate Assistant and later Assistant Lecturer in the Department of Statistics at the Abia State University, Nigeria.

Before moving to Khalifa University in 2020, Dr. Okorie worked as a Research Associate in the Division of Infection, Immunity and Respiratory Medicine, School of Biological Sciences, Faculty of Biology, Medicine, and Health at the University of Manchester, UK where he developed a novel Statistical Classification (Machine Learning) algorithm for predicting patients' outcomes for chronic respiratory diseases including chronic cough, chronic obstructive pulmonary disease (COPD), asthma, and idiopathic pulmonary fibrosis (IPF).

His research area includes distribution theory and applications, applied statistics, and statistical modeling in finance. He enjoys playing table tennis. Dr. Okorie is a Certified Member of the Institute of Mathematics and its Applications (IMA) and a Member of the Institute of Mathematical Statistics (IMS). He has peer-reviewed many research articles for many reputable statistics and related journals.

  • Ph.D., Statistics, University of Manchester¬† (Manchester, UK), 2020
  • M.Sc., Statistics (Financial Statistics), University of Manchester (Manchester, UK), 2014
  • B.Sc., Abia State University (Uturu, Nigeria), 2009
Teaching Areas
  • Statistical Computing with R
  • Sampling Theory and Survey Methods
  • Time Series Analysis and Forecasting
  • Generalized Linear Models & Survival Analysis
  • Statistical Modelling in Finance
  • Quantitative Risk Management
  • General Insurance
  • Distribution Theory
  • Statistical Methods
  • Linear Models
  • Extreme Values and Financial Risk
  • Calculus I
  • Linear Algebra I
  • Probability
  • Statistical Inference
Research Interests
  • Applied statistics
  • Statistical modeling in finance
  • Distribution theory