Jiajia Xie

Jiajia Xie

AI, Machine Learning, and Computational Scientist.

I am a machine learning engineer in Energy and Charging’s Reliability Engineering at Tesla, where I build and evaluate LLM-agent systems for reliability engineering and physics-informed machine learning models for prognostics of Tesla’s products.

I obtained my Ph.D. from the interdisciplinary Computational Science and Engineering program at Georgia Tech, advised by Prof. Cassie S. Mitchell in the Laboratory for Pathology Dynamics. I previously earned my M.S. in the same program under the joint supervision of Prof. B. Aditya Prakash and Prof. Lauren N. Steimle, focusing on data-driven modeling and controls of pathology dynamics, epidemics and human behavior. I hold a B.S. in Economics and Mathematics from Presbyterian College (South Carolina, USA). During my Ph.D., I interned at Amazon, Tesla, and Berkeley Lab.

Current Work at Tesla

My work centers on making LLM-agent workflows trustworthy enough to support formal reliability engineering processes.

  • LLM-agent evaluation for FMEA. I design and optimize AI-agentic workflows that draft failure mode and effects analyses (FMEAs), and I build the evaluation layer that keeps them aligned with expert judgment and preference.
  • Knowledge-graph-driven fault tree generation. I design, evaluate, and produce ai-agentic workflow for fault tree analyses.

Research Interests

A central theme of my PhD research is bridging modern generative and language models with structured or physical systems under perturbations — from the pathology dynamics of diseases to industrial reliability processes and human mobility. Recently, I have been exploring generative diffusion/flow models, LLM-agent evaluation, stochastic optimal control, and network science.

Bio-mechanistic Generative Models

  • Variational autoencoder–style approaches for generating brain connectomes under neurological diseases (IJMS 2025).
  • Dynamic Brain Connectome Vulnerability in Neurodegeneration via Score-based Network Diffusion (NPJ DEMENTIA).
  • Diffusion Bridge Sampler and Stochastic Optimal Control (title removed for review anonymization).

Additional Work

  • Source-robust non-parametric reconstruction of epidemic-like event-based network diffusion processes with online data (BDCC 2025)
  • Augmenting Bayesian topic models using online confirmations from community-driven apps (BuildSys 2023)
  • Representative deep-gray thermodynamic models of residential buildings (Energy & Buildings 2024)
  • Empirical WiFi datasets for localizing COVID-19 interventions (Frontiers in Digital Health 2023)

Outside of research, I am a dreamer, reader, and enthusiastic audience member of musicals, sports, and concerts (🖤🩷).

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