Georgia Tech’s QBioS PhD arms biologists with stochastic-process toolkits and GPU clusters. Cohorts model viral dynamics in single cells, visualize developmental genetics in light-sheet microscopes, and mine terabytes of microbiome metagenomes. Rotations range from CRISPR lineage tracing in zebrafish embryos to agent-based tumor eco-evolution, producing alumni who publish in Cell and debug CUDA kernels with equal ease.
Agent-based model of tumor–immune eco-evolutionary dynamics
Bayesian inference of ribosome-profiling translation rates
Topology-guided analysis of protein-folding energy landscapes
CRISPR lineage-tracing reconstruction algorithms in zebrafish
Neural-ODE surrogate for intracellular viral replication cycles
Single-cell multi-omic integration via graph variational auto-encoders
Biome-scale metagenomic assembly of coastal nitrogen cycling
Optogenetic control of synthetic toggle-switch circuits in yeast
Stochastic modeling of antibiotic persistence in biofilms
Phase-field simulation of bacterial colony pattern formation
Evolutionary game theory of microbiome metabolic cross-feeding
Deep-learning enhancer-promoter interaction predictor
Kinetic Monte-Carlo of actin network polymerization forces
Spatial transcriptomics data-viz pipeline for brain slices
Techno-economic analysis of cell-free biomanufacturing platforms
Marry theory and data to reveal life’s hidden algorithms.
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