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Research

Closing the loop on biomaterials discovery

We combine robotic automation with machine learning to run experiments in a continuous, self-driving cycle — for polymer biomaterials, nanomedicine, and drug delivery. Our work spans four connected themes.

Self-driving labs

We close automation and machine learning into a single loop that designs, makes, tests, and learns on its own — turning discovery into a continuous cycle rather than a sequence of manual experiments.

Automated polymer chemistry

Robotic, high-throughput platforms for controlled polymerization — including automation-assisted photoinduced ATRP — let us synthesize and characterize hundreds of candidate polymers reproducibly.

Machine learning & active learning

Active learning and Bayesian optimization drive the search through vast polymer design spaces, using each round of results to decide which experiments are worth running next.

Nanomedicine & drug delivery

We translate discovered bioactive polymers into nanomedicines and controlled drug-release systems, applying the loop to real therapeutic problems.

Publications

Key publications

A selection of work that best represents the lab's direction. Browse the full archive for everything else.

All publications

The method

Design → Build → Test → Learn

Every project is an instance of the same loop. The faster it turns, the faster we discover.

The Design, Build, Test, Learn closed loopA continuous cycle of four stages — Design, Build, Test, and Learn — connected in a ring to represent closed-loop, self-driving experimentation.DesignBuildTestLearn