Dr. Arman Sabbaghi
- Email: sabbaghipurdue.edu
- Website
- Biosketch PDF
- Purdue University
- Status: Faculty
- Department: Stat,
- Will Mentor: Doctoral students, Pre-doctoral students
Areas of Expertise
Applied Statistics,Research Interests
Bayesian data analysis, Causal inference, Experimental design, Missing data, Modeling and model selection, Statistical process and quality control, Data Science
Bio
The current dramatic growth and trajectory of the modern field of statistics is driven by complex issues in data collection and analysis that accompany groundbreaking developments in science and engineering, and that cannot be resolved by traditional means. I seek to contribute to this new era in statistics by pioneering statistical methodologies and theories that possess a broad scope of application to cutting-edge problems across disciplines. My research interests include
- statistical modeling for improved control of complex engineering systems,
- Bayesian data analysis (e.g., Bayesian model building, posterior predictive checks, MCMC methods),
- experimental design (e.g., the design and analysis of fractional factorials, optimum designs, computer experiments),
- causal inference (e.g., transportability, interference, principal stratification, observational studies), and
- missing data.
Specific major objectives of my current research are the development of efficient and powerful Bayesian methodologies for statistical model building in additive manufacturing (AM) systems, and the creation of mathematical tools that facilitate the characterization of large classes of experimental designs for the study and improvement of complex physical processes. My research interest in statistical methodology and theory for addressing important real-life problems in science and engineering has ultimately led to the development of new frameworks and techniques for AM and experimental design that can positively impact the continued growth and trajectory of statistics.
Please feel free to contact me if you would like to collaborate on new and interesting real-life problems.