Encrypted peptides
I investigate bioactive peptides released through protein degradation, particularly antimicrobial peptides and their role in host defense.
I’m a PhD student in computational biology at Lund University, currently a visiting research scholar in César de la Fuente’s Machine Biology Group at the University of Pennsylvania.
My current work focuses on encrypted peptide discovery, particularly antimicrobial peptides, and computational methods for protein and peptide design.

I study how proteins give rise to bioactive peptides, and build computational methods to discover and design them.
I investigate bioactive peptides released through protein degradation, particularly antimicrobial peptides and their role in host defense.
I develop methods for protein and peptide design using machine learning, Bayesian optimization, and biological information.
I use peptidomics, graph models, and interpretable machine learning to study protein degradation and molecular changes during infection.
Bayesian optimization for exploring protein and peptide sequence space.
Sequence designModel and analyze sequential protein degradation from peptide measurements.
PeptidomicsBuild and interpret neural networks informed by biological pathways.
Interpretable MLCluster related peptides to reveal patterns in large peptidomics datasets.
PeptidomicsGuide protein generation toward structural and biochemical objectives.
Protein designReconstruct proteomic disease trajectories with conditional flow matching.
ProteomicsA background to my PhD projects
the flawed academic system through the lens of game theory
a short tutorial