Introduction
Dr. Haris Khurram is an Assistant Professor of Analytics at the National University of Computer and Emerging Sciences (NUCES), Chiniot-Faisalabad Campus. He joined NUCES in 2018 as a Lecturer of Statistics and subsequently served as an Assistant Professor of Statistics before moving to his current role in Analytics. He was a Visiting Assistant Professor at Faculty of Science and Technology, Prince of Songkla University (PSU), Thailand. He worked as a Postdoctoral Fellow in Research Methodology and Data Analytics Program, Department of Mathematics and Computer Sciences, Prince of Songkla University (PSU), Thailand. Prior to this, he worked at Bahauddin Zakariya University and at Multan Postgraduate College, Multan, in a visiting and a permanent capacity, respectively. He received his Ph.D. in Statistics from Bahauddin Zakariya University, Pakistan, and has more than nine years of experience in teaching and research.
Dr. Khurram is an active researcher in the areas of Data Science, Analytics, and Predictive Modeling. His research focuses on the development and application of statistical and machine-learning methods for health, environmental, epidemiological, survey, and forecasting problems. His recent work includes machine-learning-based prediction, public health and epidemiological modeling, environmental and air-pollution analysis, spatio-temporal modeling, anthropometric and growth modeling, survey sampling and calibration estimation, and econometric and forecasting models. He also has extensive experience in academic publishing and scholarly service, serving as an Academic Editor for PLOS ONE. He has authored more than 50 research publications in international journals and has served as a reviewer and editor for various international scholarly journals.
Fields of Interest
- Data Science and Analytics
- Machine Learning and Predictive Modeling
- Applied Statistics and Econometrics
- Public Health and Epidemiology
- Environmental and Climate Modeling
- Survey Sampling and Estimation
- Bayesian Nonparametric Models