I am a Research Assistant Professor at the University of Pennsylvania in the Machine Biology Group, working where peptide chemistry, microbiology, and artificial intelligence meet. My work designs and discovers new antibiotics to confront the global rise of antimicrobial resistance.
Trained as a synthetic and organic chemist, I pair solid-phase peptide synthesis and structure–activity-relationship-guided design with deep learning and generative AI to mine the proteomes of living, extinct, and microbial life for encrypted antimicrobial peptides. I also engineer peptide chemical modifications for stability and selectivity, and build low-cost biosensors for rapid infectious-disease diagnostics.
Dissecting how physicochemical and structural descriptors — charge, hydrophobicity, amphipathicity, helicity — govern the activity, selectivity, and stability of cationic antimicrobial peptides, and using those rules to redesign natural toxins (decoralin, polybia-CP, VmCT1, mastoparans) into potent, non-toxic synthetic therapeutics.
Building computational and generative-AI pipelines — pattern recognition, genetic algorithms, deep learning, and diffusion/Bayesian models — to uncover encrypted antibiotics across the tree of life: the human proteome, extinct organisms (molecular de-extinction), the archaeome, and the global microbiome.
Expanding peptide chemical space with cyclization, disulfide and lactam bridges, non-canonical residues, backbone and terminal modifications, and computationally designed pore-forming scaffolds — tuning proteolytic stability, membrane selectivity, and function.
Designing inexpensive electrochemical and colorimetric biosensors that detect bacterial and viral pathogens in minutes at the point of care — from pencil-graphite SARS-CoV-2 tests to paper-based devices for antibiotic-resistant bacteria.