Assistant Professor in the Area of Applied Statistics
Associate Director, Statistical Consulting Service
Department of Statistics
Purdue University Department of Statistics
150 North University Street
West Lafayette, IN 47907-2066
Office: MATH 204
Phone: (765) 496-0234
Fax: (765) 494-0558
| Curriculum Vitæ
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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
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. A brief overview of my research on AM systems follows below, and additional information can be found in the Foundations of Accuracy Control for Additive Manufacturing (FACAM) blog. 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.
- 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.
Please feel free to contact me if you would like to collaborate on new and interesting real-life problems.
Geometric Shape Deviation Modeling and Control for Additive Manufacturing
Additive manufacturing (AM) has been recognized as a disruptive technology with the potential to revolutionize manufacturing. It holds the promise of direct digital manufacturing of shapes from computer-aided design (CAD) models that possess highly complex geometries, materials, and functionalities such that part-specific tooling and fixturing, and the bulk and waste, associated with traditional manufacturing are eliminated. However, a major issue with current AM processes is that they yield shapes whose dimensions are discrepant with those specified in the CAD models. These discrepancies, referred to as geometric shape deviations, occur due to rapid phase changes inherent in AM. Shape deviations adversely affect the performance and utility of AM, and thus must be controlled in order to ensure its successful adoption in practice.
In 2012, I commenced a research collaboration with Dr. Qiang Huang from the University of Southern California's Daniel J. Epstein Department of Industrial and Systems Engineering and Dr. Tirthankar Dasgupta from Rutgers University's Department of Statistics and Biostatistics to develop statistical methodologies for shape deviation control in AM. Our first set of accomplishments include
A major trajectory of AM, which constitutes a rapidly evolving domain of cyber-physical systems, is the development of AM systems that seamlessly integrate multiple disparate CAD models and connected AM processes for a large community of additive manufacturers. These systems possess great potential to fundamentally transform the manner in which people interact with manufacturing. Indeed, by reducing fabrication complexity and liberating product design processes for online communities of users, AM systems can inaugurate an exciting new era of cybermanufacturing with positive effects that far exceed those of current manufacturing systems. However, the specification of deviation models for comprehensive control of an AM system is made difficult by the nature and capability of AM for one-of-a-kind manufacturing. Shape deviation control in an AM system requires a methodology that can leverage previously developed deviation models for different shapes under distinct processes to automate and expedite the specification of deviation models for new shapes and processes using only a small sample of products.
- a new functional modeling and control methodology for in-plane deviations (Huang et al., 2015), and
- the design of an experiment and the derivation of new Bayesian discrepancy measures for the identification and modeling of interference in AM (Sabbaghi et al., 2014).
Starting in 2015, I worked with Dr. Huang and Dr. Dasgupta, and my Ph.D. student Raquel De Souza Borges Ferreira, to develop efficient and powerful Bayesian methodologies to address these challenges for AM systems. My research for this objective is supported by the U.S. National Science Foundation under Grant Nos. CMMI-1544841, as part of the NSF/DHS/DOT/NASA/NIH Cyber-Physical Systems program, and CMMI-1744123. The specific methodologies that we created are
These methodologies can contribute to the new era of AM systems by addressing the challenge of deviation modeling and control of the vast variety of shapes manufactured under distinct processes with severe time and resource constraints. Our methods have been able to produce effective deviation models in a rapid and efficient manner without requiring the use of specialized domain knowledge on specific AM processes. The corresponding significant implication is that these methods can abstract from particular shapes and processes to underpin cross-cutting deviation model building in AM systems more broadly. In this respect, our research can enable smarter deviation control for AM systems, with the potential of immediate practical application, and thereby help to advance their future growth and adoption for a large community of AM users.
- an adaptive Bayesian method for deviation modeling of different shapes from small samples of disparate shapes under an AM process (Sabbaghi et al., 2018),
- model transfer across distinct AM processes via mean effect equivalence of lurking variables (Sabbaghi and Huang, 2018), and
- deviation model building across both different shapes and processes in AM systems (Sabbaghi and Huang, 2016; Ferreira et al., 2018).
Please feel free to contact me if you wish to learn more about these methodologies for geometric shape deviation modeling and control of AM systems.