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Select Paper: ChainSpace: Sequence-Aware Design for Machine Learning-Driven Polymer Engineering
I'm a chemical engineer working where polymer science, lab automation, and machine learning meet. Most recently I was a Principal Scientist and the first employee at Nanite, which specialized in AI-driven, high-throughput workflows to design polymers for gene delivery. Before that I built materials informatics capabilities at 3M in the Corporate Research Materials Lab and was a NIST-CHiMaD Postdoctoral Fellow at the University of Chicago working on Materials Genome Initiative (MGI)-related projects in soft matter.
Open to R&D leadership roles in AI-driven materials, polymers, and delivery. Let's talk.
Selected Works
Selected Works
ChainSpace: Sequence Statistics for AI Models
ChainSpace: Sequence Statistics for AI Models
A computational framework for mapping synthetically accessible polymer sequence space for ML-driven design.
V2 preprint on ChemRxiv.
Polymer Nanoparticle Design
Polymer Nanoparticle Design
AI-driven design with automated synthesis and screening for gene delivery, supporting $10M+ in venture financing.
Awarded 2023 Emerging Investigator in Chem Comm.
Materials Informatics for Adhesives
Materials Informatics for Adhesives
Platform design to structure libraries of formulation and process data around pressure sensitive adhesives across product lines.
Overview in Pure and Applied Chemistry and in Molecular Systems Design & Engineering.
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