Dr Haris Khurram | Computational Statistical Modeling

Dr Haris Khurram | Leading Researcher in Computational Statistical Modeling

Congratulations,Dr Haris Khurram  on winning the esteemed Women Researcher Award  from ResearchW! Your dedication, innovative research, and scholarly contributions have truly made a significant impact in your field. Your commitment to advancing knowledge and pushing the boundaries of research is commendable. Here’s to your continued success in shaping the future of academia and making invaluable contributions to your field. Well done!

Dr Haris Khurram Sebastin is a distinguished academic and researcher in the field of renewable energy, holds a PhD in Biosystems Engineering from Kangwon National University, South Korea. His academic journey has been marked by a profound dedication to advancing solar energy technologies, specifically in solar thermal harvesting and its integration into agricultural and architectural applications.

Professional Profiles:

Academics:

PhD in Statistics, Bahauddin Zakariya University, Multan, 2016-2021, Thesis: Bayesian Non-Parametric Modelling.

M.Phil. in Statistics, Bahauddin Zakariya University, Multan, 2014-2016, Thesis: Identifying Continuous Probability Distributions.

Work:

Assistant Professor (Statistics), National University of Computer and Emerging Sciences, Chiniot-Faisalabad Campus, Pakistan (2021-present).

Lecturer (Statistics) at various institutions.

Taught in diverse departments: Information Technology, Commerce, Agriculture Technology, Soil Science, Forestry.

Admin and committee roles at National University.

Membership in Pakistan Institute of Statistical Training and Research.

International Engagements:

Presented papers at global conferences on Statistical Sciences, Neonatology, Disaster Risk Reduction, and more.

Editorial roles in various academic journals.

Reviewed papers for esteemed journals in statistics, public health, and medical sciences.

Awarded for Best Paper and Achievement in Disaster Risk Reduction.

Community Engagement:

Conducted workshops on Faculty Development, Outcome-Based Education, and Pedagogical Techniques.

Contributed to disaster risk reduction expos, survey statistic conferences, and workshops on curriculum design.

Computer Skills:

Proficient in MS-Office Suite, LaTeX, and various statistical software: R, SPSS, Minitab, E-Views, Stata, Mathematica, etc.

Interests:

Expertise and interest in computational statistical methods, survey sampling, Bayesian modeling, econometric analysis.

Application domains: Bio-Medical Science, Pharmaceutical Sciences, Genetics, Social Sciences.

Publications Top Note :

Predatory functional response and fitness parameters of Orius strigicollis Poppius when fed Bemisia tabaci and Trialeurodes vaporariorum as determined by age-stage, two-sex … paper published in 2015 cite by 20

Why Banks Need Adequate Capital Adequacy Ratio? A Study of Lending & Deposit Behaviors of Banking Sector of Pakistan  paper published in 2018 cite by 14

Stock Market Forecasting Using the Random Forest and Deep Neural Network Models Before and During the COVID-19 Period paper published in 2022 cite by 6

Nutritional status and growth centiles using anthropometric measures of school-aged children and adolescents from Multan district  paper published in 2022 cite by 5

Impact of Terrorism on Stock Market: A Case of South Asian Stock Markets  paper published in 20196 cite by 4

Assessing Patient Satisfaction with Community Pharmacy Services: A Large Regional Study at Punjab, Pakistan paper published in 2023 cite by 3

Knowledge, attitudes and practices of pharmacogenomics among senior pharmacy students: a cross sectional study from Punjab, Pakistan   paper published in 2022 cite by 3

Assessment of health-related quality of life in hypertensive hemodialysis patients  paper published in 2022 cite by 3

Disposal practices of expired and unused medications among households in Punjab, Pakistan   paper published in 2023 cite by 2

Antibiotic use: A cross-sectional survey assessing the knowledge, attitudes, and practices amongst students of Punjab, Pakistan paper published in 2022 cite by 2

 

📊 Citation Metrics (Google Scholar):

  • Citations by: All – 61, Since 2018 – 61
  • h-index: All – 4, Since 2018 – 4
  • i10 index: All – 2, Since 2018 – 2

 

 

Introduction to statistical methods and data analysis

Introduction to statistical methods and data analysis

The International Conference on Statistical Methods for Analyzing Engineering Data stands as a prominent nexus for the convergence of statistical methodologies and data analysis techniques within the realm of engineering. This esteemed conference brings together experts, researchers, and practitioners to explore innovative statistical approaches that drive advancements in engineering systems and data analysis. At the heart of this gathering is a shared commitment to harnessing the power of statistics to optimize, refine, and revolutionize engineering practices.

Multivariate Statistical Analysis in Engineering

Dive into the world of multivariate statistical methods to uncover hidden patterns, relationships, and correlations within complex engineering data sets, allowing for informed decision-making and process optimization.

Quality Control and Process Monitoring

Examine statistical techniques for monitoring and improving the quality of manufacturing processes, ensuring consistency, minimizing defects, and enhancing overall product performance.

Statistical Reliability and Risk Assessment

Explore the application of statistical tools to assess the reliability and risk associated with engineering systems, guiding maintenance strategies and mitigating potential failures.

Design and Analysis of Experiments (DoE)

Delve into the principles of experimental design and statistical analysis to optimize product designs, enhance manufacturing processes, and identify factors influencing engineering outcomes.

Big Data Analytics in Engineering

Investigate how statistical methods and data analysis can be tailored to analyze and extract valuable insights from large-scale engineering datasets, enabling data-driven decision-making and predictive modeling.