Photo courtesy of Minghao Liu
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Minghao Liu’s career is a testament to the power of computational science in solving complex, real-world problems. Beginning with his groundbreaking research in computational mechanics, Minghao has consistently demonstrated a passion for pushing the boundaries of understanding dynamic systems, particularly under extreme conditions where traditional experiments fall short. His expertise in developing advanced simulations and computational models not only filled critical gaps in scientific knowledge but also laid the foundation for his seamless transition into the world of FinTech.
His Ph.D research, which resulted in many impactful publications, reflects this ambition to push the boundaries of understanding through simulations.
Peridynamic simulation of brittle-ice crushed by a vertical structure
In this study, Minghao applied peridynamic theory to simulate the fracture mechanics of brittle ice subjected to impact forces, conditions that are difficult to replicate in physical experiments. The work provided crucial insights into crack propagation patterns under extreme cold, helping to improve the design of structures exposed to harsh arctic conditions.
Coarse-grained molecular modeling of the microphase structure of polyurea elastomer
In this paper, Minghao developed a structure – matching coarse-grained model of polyurea elastomers. The model implicitly represented hydrogen atoms, streamlining the complexity of molecular simulations. Trained using iterative Boltzmann inversion and a novel distance–dependent scaling function, the model significantly reduced iteration times. The simulations captured the microphase separation of polyurea, revealing a hard domain spacing of five nm, consistent with x-ray scattering data from similar elastomers. Analyzing two different model systems, the research demonstrated that multiblock systems form large, interconnected hard domains, while diblock systems create smaller, ribbon-shaped domains. The study further revealed that the soft segments formed bridge-like and loop-like structures, contributing to a deeper understanding of the material’s microstructure under various conditions.
Coarse-grained molecular simulation of the role of curing rates on the structure and strength of polyurea
This research explored how the curing process influences the mechanical strength of polyurea, a material that plays a key role in industries requiring high-performance materials, such as defense and automotive. Simulating these effects in silico allowed for a more nuanced understanding of how material properties change under extreme manufacturing conditions.
The use of scientific computing was central to all of Minghao’s research efforts. By employing computational simulations, Minghao was able to model complex physical systems at different scales — ranging from fracture mechanics in ice to molecular dynamics in polyurea. The power of high-performance computing enabled the exploration of system behaviors that are difficult or impossible to observe in real-world experiments. These computational techniques, such as peridynamics, coarse-grained modeling and lattice Boltzmann method, not only reduced the need for physical prototypes but also accelerated the discovery process by providing deeper insights into material properties under extreme conditions.
This expertise in scientific computing seamlessly translated into Minghao’s current role as a data scientist in the FinTech industry. In a field where large-scale data analysis and machine learning drive innovation, Minghao applies similar computational methodologies to solve financial problems. Just as simulations were used to predict material behaviors, now predictive models and algorithms are deployed to optimize financial products, analyze market trends, and enhance customer experiences.
Specifically, Minghao focuses on credit risk modeling and recommendation systems in his data science role. The skills developed in his academic research — modeling dynamic systems, analyzing complex data, and making predictions based on those models — are directly applicable in financial contexts. Credit risk modeling, for example, requires understanding patterns of financial behavior of tradeline data under varying conditions, much like how Minghao once modeled material properties of molecular structures under extreme stresses. Similarly, building recommendation systems involves predictive modeling and pattern recognition, skills honed through his work with molecular and material simulations. In both cases, Minghao brings a unique approach that blends scientific rigor with innovative financial solutions.
By blending scientific precision with innovative financial solutions, Minghao Liu exemplifies how advanced computational methodologies can transcend disciplines, driving both technological and business innovation. His journey from academia to FinTech highlights the transformative power of data science and computational expertise in tackling the most challenging problems across industries.
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