Jiajia Xie

I am a Ph.D. candidate in 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 WiFi networks, mobility data, and COVID-19 modeling.

I hold a B.S. in Economics and Mathematics from Presbyterian College (South Carolina, USA).

Experience

• Applied Scientist Intern – Amazon AMXL (Summer 2025)
• Data Scientist Intern – Tesla (Fall 2023)
• Research Intern – Lawrence Berkeley National Laboratory (Summer 2023)

Research Interests

A central theme of my research is understanding social, physical, and pathological dynamics as networked systems subject to perturbations. My work asks:

Can we reverse-engineer, fine-tune, or control base systems to recover missing connections, reconstruct their evolution, or generate plausible new data?

To address this, my Ph.D. work integrates optimal control, generative modeling, and physics-informed learning to model and intervene in complex dynamical systems.

Selected Research Areas & Projects

• Stochastic Optimal Control for Fine-tuning Diffusion Models in Pathophysiology
  • Modeling neurodegenerative disease progression via stochastic optimal control of misfolded protein dynamics.
• Bio-mechanistic Generative Models
  • Variational autoencoder–style approaches for generating brain connectomes under neurological diseases (International Journal of Molecular Sciences 2025).
  • Score-based stochastic network diffusion models for connectome dynamics under neurodegeneration.
  • Score-based diffusional event-based modeling for high-dimensional, monotonic disease progression.
• Online Decision Making and Optimization
  • Source-robust, non-parametric reconstruction of epidemic-like event-based network diffusion processes with online data (Submitted to PLOS Computational Biology).
  • Augmenting Bayesian topic models using online confirmations from community-driven apps (BuildSys 2023).
• Physics-Constrained Machine Learning
  • Representative deep-gray thermodynamic models of residential buildings (Energy & Buildings 2024).
• Additional Work
  • 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 (🖤🩷).

Contact

• Email: jxie@gatech.edu
• Resume: Download PDF (Updated Aug 2025)