Yasaman Jamalipour Soofi, a doctoral candidate in systems engineering, will defend their dissertation titled “Complex Systems Approach in Data Analysis: Unraveling Interactions and Emergent Behavior in Material Science and Human Health.” Their advisor, is Jinling Liu, was a prior associate professor in the engineering management and systems engineering department. The dissertation abstract is provided below.

The dissertation embodies a complex system approach to explore the intricate relationships within material science and human genomics. By adopting a systems engineering perspective, the research aims to unravel the behaviors and properties of these domains as interconnected complex systems. The research encompasses two main projects that serve as examples of the complex system approach. In the field of material science, the focus lies on the prediction of alloy properties. This project develops predictive models that leverage machine learning algorithms and statistical methods to forecast alloy properties based on composition and processing parameters. By integrating physics-based feature engineering with machine learning techniques, the project captures the complex relationships within the alloy system, bridging the gap between fundamental physics principles and data-driven modeling. This endeavor enhances material design and optimization through an understanding of the emergent properties and interactions within alloys. In the realm of human genomics, the research delves into the impact of genomic variants on disease traits, with a specific emphasis on cardiovascular diseases and Parkinson's disease. This project integrates Genome-Wide Association Study (GWAS) and Individualized Bayesian Inference (IBI) methodologies to gain valuable insights into the genetic basis of complex diseases. By considering the intricate interactions between genetic variations and disease traits at an individual level, this project enhances our understanding of the underlying complexities of diseases. It enables personalized risk assessment and targeted interventions, contributing to the advancement of precision medicine.

Both projects exemplify the complex system approach by recognizing the holistic perspective and interconnections within the studied domains. The predictive models and integrated methodologies developed in these projects serve as manifestations of the systems engineering principles employed. The projects involve pattern recognition, capturing interactions, and predicting emergent properties within the studied systems. Furthermore, the research explores system-level optimization, aiming to improve outcomes and performance in material science and human genomics. In conclusion, the dissertation's complex system approach integrates material science and human genomics, utilizing projects as examples to unravel the intricate relationships within these domains. By applying systems engineering principles, such as systems integration, pattern recognition, interactions, emergent properties, predictive modeling, and system-level optimization, the research contributes to the understanding and advancements in complex systems analysis. This work opens doors for further exploration of complex systems, extending from alloys to human genomes and beyond.