(4ai) Advancing Sustainability and Health through Multiscale Computational Modeling of Soft Materials

Research Interests

Striving to advance environmental sustainability and human health, my long-term research vision revolves around exploiting computational modeling to investigate two crucial areas in soft materials: (1) recyclable high-performance polymer materials, and (2) cellular basis of human diseases. In both areas, my group will leverage multiscale modeling and spatiotemporal characterization approaches, bridging the gap between atomic scale information and macroscopic rheological and mechanical properties, thereby informing industrial and clinical applications. In terms of sustainable polymers, my aim is to elucidate the relationship from molecular structure to polymer network characterization, and from industrial processing to final product properties. This would help to position newly designed polymer materials as cost-effective, recyclable options within the industry. As for diseases, my goal is to build a coherent link from lipids and proteins to cell membranes and cytoskeletons, extending to individual cells and tissues. This research aims to enhance disease understanding at the molecular level and improve disease diagnoses through the analysis of cellular behaviors. While both research areas draw on my previous work in drug delivery, scattering analysis, and theoretical modeling of traditional polymers, my interdisciplinary training enables me to introduce novel perspectives and techniques to each topic. This helps bridge the gaps that often arise due to domain-specific knowledge barriers.

As a step towards this goal, I have devoted my career to computer simulations of soft materials intending to create novel multiscale molecular models, characterization tools and theoretical methods to study polymer and cellular systems. My doctoral research focused on multiscale simulations to address complex challenges related to nano-bio interfaces in drug delivery. During my time at ORNL, I developed innovative scattering analysis frameworks, revealing new aspects of polymer physics. At UM, I am collaborating with the Dow Chemical Company on polymer processing, where I am developing a multistage machine-learning modeling protocol that integrates polymerization, rheology, crystallization, and end-product properties. These efforts have resulted in 40 peer-reviewed journal articles and have fostered robust collaborations across academia, national laboratories, and industry.

1: Multistage and Multiscale Modeling of ``Synthesis - Characterization - Processing" for Sustainable Polymers: Paving the Way from Theory to Practice.

Developing renewable, safe, and recyclable polymer materials within a closed lifecycle is of paramount importance to address the issues stemming from single-use plastics of nonrenewable origins. Significant progress has been achieved in the domain of sustainable polymer systems, encompassing polymers derived from biomass renewables, repurposing plastic waste, and innovative recyclable crosslinked thermosets. Despite notable advancements in design of polymer chemistry, a notable gap remains between the successful design of small molecules and the realization of functional polymer materials capable of delivering the desired product performance. To address this challenge, my group adopts an integrated approach, combining multiscale modeling, scattering characterizations, and machine learning techniques. My primary focus is on advancing two key areas: rheology and crystallization. Through the establishment of a computational workflow that integrates molecular simulations, structural and dynamical characterizations, constitutive modeling, and optimization of processing conditions, I aim to develop a multistage modeling framework that effectively links molecular composition design to product properties.

2: Multiscale modeling of ``Subcellular Structure - Single Cell - Multicellular Tissue" for Enhanced Understanding and Clinical Diagnosis of Diseases.

Numerous cellular-based diseases, such as cancer, are characterized by pathological alterations in lipid and protein compositions, leading to significant changes in mechanical and rheological behaviors at the cellular and tissue levels. To revolutionize medical treatments and clinical diagnoses of these diseases, it is paramount to correlate molecular variations with single-cell behaviors and the properties of tissues formed by multiple coordinated cells. Currently, such understanding is hindered by challenges including the intricate influence of molecular compositions on cell membranes and cytoskeletons, as well as the complex structures and dynamics of cells and their interactions with their surroundings. My group will employ multiscale approaches to fill these research gaps, enabling a comprehensive understanding of subcellular structures, single-cell behaviors, and collective cellular performances. Through the utilization of this bottom-up strategy, I aim to establish connections between microscale molecular pathological variations and macroscale tissue rheology.

Teaching Interests

My aspiration to become a professor in engineering is driven not only by my passion for cutting-edge research but also by my deep enthusiasm for teaching. Having personally benefited from the guidance and support of my teachers and mentors, which enabled me to overcome the challenges of growing up in a low-income family and become a first-generation graduate, I am determined to pass on this spirit of empowerment to my students. Through teaching, I aim to make a meaningful impact on their lives and equip them with the necessary skills for successful careers. I have had the opportunity to gain valuable teaching experience in various roles, including as a teaching assistant (TA), guest lecturer, and mentor for undergraduates. As an assistant professor in the future, I eagerly anticipate teaching a diverse range of undergraduate courses, with a particular focus on subjects such as polymers, mechanics, and computational modeling.

My initial teaching experience dates to 2012 when I volunteered as a lecturer for a group of high school students. During this time, I taught both math and physics, and it was inspiring to witness the students' keen interest when I emphasized the connections between physical and mathematical concepts. As a TA for the Computational Mechanics course, which had approximately 100 undergraduate students, my responsibilities included grading assignments, holding weekly office hours, and delivering lectures when the professor was unavailable. Additionally, I had the privilege of serving as the coordinator for REU students. One aspect of this role that I thoroughly enjoyed was hosting weekly group meetings, where I would address topics based on their specific needs, such as efficient literature review techniques, and lead engaging group discussions. During my graduate studies, I had the opportunity to mentor a small research team comprising undergraduate students, namely Jason Yang, Alessandro Fisher, William Baker, and Jeffery Ge. Through our collaboration, each of them published at least one paper alongside me, and I am proud to see them embark on successful career paths. For instance, Jason is currently pursuing his Ph.D. degree at Caltech with an NSF Graduate Research Fellowship, while Jeffery has found success as an engineer at Amazon.

In summary, my teaching interests stem from a genuine desire to impart knowledge, inspire curiosity, and support the personal and professional growth of my students. I am committed to creating an engaging learning environment and fostering a collaborative spirit that enables students to thrive academically and pursue rewarding careers.