Computational Biology and Bioinformatics

Build a Bayesian model that links social media sentiment cascades to county vaccine uptake gaps and tests diffusion patterns.

Build a species distribution model to predict where spotted lanternfly could spread by 2050 using occurrence data, climate layers, and validation.

Build an ODE model of menstrual hormone cycles, compare PCOS patterns, and find the best sampling windows for at-home hormone strips.

Model zebrafish stripe variants with reaction-diffusion code, fit public images, and infer morphogen parameters from pattern data.

Use EEG signals and a transformer model to predict attention drift during homework, then test how well your classifier spots off-task moments.

Use public transcriptome and metabolome data to build a variational autoencoder, compare cohorts, and find shared metabolic-stress patterns.

Build a metagenomic analysis project that profiles household dust, compares microbial diversity, and tests links to allergy and asthma severity.

Build and test a transformer model that predicts HIV drug-resistance mutations from sequence and treatment history using real benchmark data.

Train a graph model to predict blood-brain-barrier permeability and rank drug leads, while learning data cleaning, validation, and model evaluation.

Compare dopamine-style reward signals with deep Q-learning, and test whether biological constraints improve sample efficiency in sparse-reward tasks.