Dr. Sohail Ahmad | Applied Statistics | Best Researcher Award
Dr. Sohail Ahmad | Quaid e Azam University Islamabad | Pakistan
Dr. Sohail Ahmad is an emerging researcher specializing in causal inference and the application of deep learning techniques to statistical modeling. His work focuses on directed acyclic graphs (DAGs) for uncovering causal relationships in observational data, with applications in epidemiology, public health, and healthcare policy evaluation. He integrates transformer-based models and variational autoencoders for counterfactual reasoning, treatment effect estimation, and personalized medicine using electronic health records. Dr. Ahmad earned his Ph.D. in Applied Statistics from Central South University, China, and holds an MPhil and Bachelor’s degree in Statistics from Quaid-i-Azam University, Pakistan. He has published extensively in high-impact journals, including Applied Intelligence, Scientific Reports, and AIMS Mathematics. His professional experience includes roles as a visiting lecturer and data analyst, alongside participation in international conferences and workshops. He has earned multiple professional certifications in causal inference and data science and received scholarships and national awards for academic excellence, reflecting his dedication, innovation, and growing impact in statistical research.
Publication Profile
Scopus
Google Scholar
Education
Dr. Sohail Ahmad has pursued a comprehensive academic journey in the field of statistics, demonstrating a strong foundation in both theoretical and applied aspects of the discipline. He earned his Ph.D. in Applied Statistics from Central South University, China, where his research focused on integrating deep learning and statistical approaches for causal inference, exploring innovative methods to model complex relationships in observational data. Prior to his doctoral studies, he completed an MPhil in Statistics at Quaid-i-Azam University, Islamabad, Pakistan, where his thesis addressed the estimation of distribution functions under various sampling schemes, contributing to methodological advancements in survey analysis. Dr. Ahmad began his academic career with a Bachelor’s degree in Statistics from the same university, developing a generalized exponential-type estimator for population mean using auxiliary attributes, which laid the groundwork for his expertise in sampling theory and statistical estimation. His education reflects a consistent trajectory of rigorous research training, analytical skills, and specialization in modern statistical and machine learning techniques.
Experience
Dr. Sohail Ahmad has gained valuable academic and professional experience in the field of statistics through roles in teaching, research, and data analysis. He served as a visiting and contract lecturer at the Statistics Department of Government Post Graduate Jahanzeb College in Swat, Pakistan, where he was involved in delivering lectures, guiding students, and supporting curriculum development, fostering both theoretical understanding and practical application of statistical concepts. Prior to this, he worked as a teaching assistant and data analyst at the Department of Statistics, Quaid-i-Azam University, Islamabad, under the supervision of Prof. Dr. Javid Shabbir. In this role, Dr. Ahmad contributed to academic research projects, assisted in data management and statistical analyses, and supported faculty in developing research methodologies and practical solutions for real-world problems. His experience demonstrates a combination of teaching excellence, research proficiency, and analytical skills, highlighting his capability to contribute meaningfully to both educational and research-oriented environments in statistics.
Professional Certifications
Dr. Sohail Ahmad has actively enhanced his expertise in statistics, data science, and causal inference through a series of professional certifications and online courses from prestigious institutions. He completed advanced courses in causal inference from Columbia University, equipping him with in-depth knowledge of graphical models, counterfactual reasoning, and modern techniques for analyzing observational data. In addition, he pursued data science certifications from IBM, including foundational and applied courses in data analysis, tools, and methodologies, which strengthened his practical skills in programming, data management, and predictive modeling. Dr. Ahmad also completed a COVID-19 contact tracing course through Johns Hopkins University, reflecting his commitment to applying statistical and analytical skills to real-world public health challenges. Complementing these achievements, he participated in the 90th IKECEST Training Program for Silk Road Engineering Science and Technology Development, and an advanced Excel course focusing on data analysis and dashboard creation. These certifications highlight his dedication to continuous learning and professional growth
Awards
Dr. Sohail Ahmad has been recognized for his academic excellence and active participation in extracurricular activities throughout his educational journey. He received a prestigious scholarship from Quaid-i-Azam University, Islamabad, in recognition of his outstanding academic performance and dedication to statistical research. He was awarded a Prime Minister Laptop on merit, honoring his achievements as a distinguished student. Beyond academics, Dr. Ahmad has actively engaged in extracurricular and leadership activities that demonstrate his broader interests and commitment to personal development. He participated in the National Youth Carnival-17 at Quaid-i-Azam University, which provided a platform for networking, collaboration, and skill enhancement among young leaders. Additionally, he took part in the Peace Model United Nations (MNU) and the Youth Counselling Summit, reflecting his interest in global issues, diplomacy, and youth development. These awards and participations highlight his well-rounded profile, combining academic rigor with social engagement and leadership potential.
Research Focus
Dr. Sohail Ahmad’s research primarily focuses on the intersection of statistical methodology and modern machine learning techniques, with a strong emphasis on causal inference and estimator development. His work on generalized exponential-type and unbiased ratio estimators, as well as population distribution function estimation using auxiliary information, reflects a deep expertise in survey sampling, finite population analysis, and statistical estimation. In parallel, he explores advanced causal inference frameworks by integrating transformer-based deep learning models and variational autoencoders to estimate individual treatment effects and model latent confounders, particularly for applications in health and social sciences. This combination of classical statistical theory and cutting-edge machine learning allows him to address complex real-world problems, including treatment effect estimation, predictive modeling using electronic health records, and methodological advancements for observational studies. Dr. Ahmad’s publications demonstrate a consistent focus on improving the accuracy, interpretability, and applicability of statistical models, positioning him at the forefront of research in applied statistics and causal machine learning.
Publication Top Notes
A generalized exponential-type estimator for population mean using auxiliary attributes
Years: 2021
Citations: 27
Finite population distribution function estimation using auxiliary information under simple random sampling
Years: 2021
Citations: 16
A simulation study: An enhanced generalized class of estimators for estimation of population proportion using twofold auxiliary attribute
Years: 2023
Citations: 11
An improved family of unbiased ratio estimators for a population distribution function
Years: 2025
Citations: 6
Evaluation of agricultural wastes as a sustainable carbon source for the production of β-glucosidase from Bacillus stercoris, its purification and characterization
Years: 2023
Citations: 4
TV-CCANM: a transformer variational inference in confounding cascade additive noise model for causal effect estimation
Years: 2025
Citations: 1
Conclusion
Dr. Sohail Ahmad exhibits significant strengths in both methodological development and real-world application of statistical and machine learning approaches for causal inference. His contributions to the integration of deep learning with causal frameworks demonstrate originality and forward-thinking vision, especially in health-related research. While there is room for growth in terms of leadership visibility and research independence, his trajectory suggests a strong upward path. Given his current achievements, academic promise, and global engagement, Dr. Ahmad is indeed a suitable candidate for the Research for Best Researcher Award, representing both current excellence and future potential in advancing causal inference and applied statistics.