Publications
Three peer-reviewed papers and one manuscript in preparation,
spanning machine learning for biomaterials discovery, automated polymer synthesis, and automated structural
analysis. Every record below links to the version of record.
First authorReview202425 citations
Eman Ahmed, Prajakatta Mulay, Cesar Ramirez, Gabriela Tirado-Mansilla, Eugene Cheong, Adam J. Gormley
Tissue Engineering Part A 30(19-20), 662-680 · 2024
Biomaterials rarely fail or succeed for one obvious reason. Performance usually comes from a combination of small structural details interacting at once. Testing those combinations one experiment at a time does not scale. This review sets out how high-throughput experimentation paired with machine learning changes the search: run many conditions in parallel, keep every data point including the failures, and train models that map structure to function across the whole space rather than at a few sampled points.
Co-authorResearch article202610 citations
Cesar Ramirez, Eman Ahmed, Elena Di Mare, Maria Pineiro-Goncalves, Apostolos Maroulis, Prajakatta Mulay, D. Christopher Radford, Adam J. Gormley
ACS Polymers Au 6(1), 181-193 · 2026
ATRP is one of the workhorse reactions for making polymers with controlled length and composition, but it has historically needed inert, oxygen-free conditions, which rules out running it in an open well plate on a robot. Oxygen-tolerant chemistry removes that constraint. This paper puts photo-ATRP onto an automated liquid-handling platform and uses it to screen reaction components at a scale that is impractical by hand, including for methyl methacrylate, a monomer that propagates slowly enough to be genuinely awkward to optimise.
Co-authorResearch article20258 citations
Cesar Ramirez, Elena Di Mare, James Byrnes, Eman Ahmed, Maria Pineiro-Goncalves, Cristian Lopez, N. Sanjeeva Murthy, Adam J. Gormley
Biophysical Journal 124(21), 3772-3786 · 2025
SAXS tells you the size and shape of something in solution, but getting there involves judgement calls: where to set the Guinier range, whether a P(r) fit is trustworthy, what maximum dimension to believe. Those calls are slow and they vary between analysts, which is a problem once you are producing hundreds of profiles. SAXS Assistant automates the pipeline, trains a model on 1,940 experimental profiles from the SASBDB to estimate Dmax, clusters profiles against known biomolecular shapes, and flags results it is not confident about instead of quietly returning them.
First authorIn preparation
High-Throughput Approach for Evaluating the Solubility of Polymer-Stabilized Proteins in Organic Solvent
Eman Ahmed, Adam J. Gormley
Manuscript in preparation · Gormley Lab, Rutgers University
The doctoral project: a plate-based, automated assay for asking which random copolymers keep an enzyme soluble and active once it is moved into a water-miscible organic solvent.
October 2025
High-Throughput Approach for Evaluating Polymer-Enzyme Hybrids
Biomedical Engineering Society (BMES) Annual Meeting, San Diego, California
Oral presentation
December 2024
High-Throughput Approach for Evaluating Polymer-Enzyme Hybrids
Biomedical Engineering Students Society (BESS) Symposium, Rutgers University
Oral presentation
October 2018
Tension-Induced Rupture of Lipid Membranes
Gulf Coast Undergraduate Research Symposium, Rice University, Houston, Texas
Poster
April 2018
Tension-Induced Rupture of Lipid Membranes
Aresty Undergraduate Research Symposium, Rutgers University
Poster · Honourable mention