Hi, I'm Jiale Shi.

I am a Postdoctoral Associate at the Department of Chemistry, Boston University, working with Prof. Qiang Cui. My current research focuses on developing graph models to quantify protein dynamics, modeling pH and buffer effects on lipid nanoparticle phase transitions, and developing conditional generative models and diffusion models for lipid molecule design.

I was a Postdoctoral Associate at the Department of Chemical Engineering, Massachusetts Institute of Technology (MIT), under the supervision of Prof. Bradley D. Olsen (MIT) and Dr. Debra J. Audus (NIST) from 2022 to 2025. My postdoctoral research at MIT focused on developing polymer informatics and data-driven polymer property predictions for the CRIPT (Community Resource for Innovation in Polymer Technology) project, a $5 million NSF-funded initiative. I developed chemistry-informed functions to precisely quantify polymer similarity, MacroSimGNN to accelerate macromolecule similarity calculations, landmark distance embedding for polymers, BigFingerprint (a topology-aware fingerprint for polymers), and LLM-based Knowledge Graph RAG for polymer simulation.

My Ph.D. work at the University of Notre Dame with Prof. Jonathan K. Whitmer focused on computing free energy landscapes for materials design. I developed AI models to predict polymer-surface adhesion free energy, developed deep transfer learning to overcome data-scarcity challenges, modeled temperature-dependence of anisotropic elastic responses of liquid crystals, and modeled temperature-dependence of dynamics and stability of cluster isomerization. I received my B.S. at Peking University where I finished my undergraduate thesis under the supervision of Prof. Hong Jiang.