TITLE: Graduate Student (PhD)
PROGRAM: Bioinformatics (2020 – 2025)
EMAIL: markxiang@nullg.ucla.edu
EDUCATION:
B.S., University of Massachusetts, Amherst (2020)
Ph.D., University of California, Los Angeles (2025)

Research Interests

My research explores how dynamic signaling and gene regulation shape immune cell fate decisions, with a particular focus on B cells. Working in the Hoffmann lab at UCLA, I’ve been investigating how molecular signaling circuits—especially the NFκB pathway—control antibody responses during infection and vaccination. By integrating experimental immunology with computational modeling, my work seeks to understand how systems-level regulation drives immune decision-making, from activation to memory.

One major branch of my work develops computational models to understand how stochasticity and epigenetic control influence B-cell fate during vaccine responses. I built a mechanistic, knowledge-based model of the germinal center reaction that integrates immunological insights and accounts for dynamic, non-genetic heterogeneity in B-cell behavior. Our model shows that while B-cell fate decisions appear stochastic, this variability—when combined with clonal epigenetic stability—actually accelerates affinity maturation by allowing rare high-affinity clones to evade early differentiation and undergo further rounds of selection. These insights not only reconcile classical clonal selection theory with modern observations of cellular heterogeneity, but also allow accurate prediction of vaccine responses in mouse models with altered B-cell differentiation programs. The framework provides a basis for interpretable and personalized modeling of immune responses in health and disease.

In parallel, I have conducted live-cell imaging experiments to analyze NF-κB signaling dynamics in primary murine B cells. Using an inverted fluorescent microscope, I performed time-lapse imaging of nuclear cRel and RelA dynamics in response to BCR and CD40 stimulation. I evaluated traditional segmentation methods and implemented modern tools such as Cellpose for mask generation and Fiji for tracking and signal quantification. These pipelines enabled single-cell analysis of nuclear translocation dynamics, supporting two collaborative studies. Across both projects, I contributed to experimental design, microscopy, image analysis, and mentoring undergraduate researchers.

(Last updated: July 28, 2025)