Developed implementations of modular platform components that operate on distributed datasets using Ray (by Anyscale).

Increased (by over 100x) the size of datasets that can be feasibly supported on the platform from <10GB to >1TB.

Parallelized workloads by formulating a series of computations as nodes in a dependency graph.

Used Argo Workflows on Kubernetes to orchestrate jobs in Docker containers.

Yielded over 3x speedup on some workflows.

Made computational chemistry tasks more robust to node errors or AWS spot instance termination.

Designed a new interface and implemented 2 new molecule generation methods for manipulating and composing different combinations of substructures (fragments) when generating molecules.

Integrated Unigen into a data pipeline that would automatically filter and use ML models to predict certain desired properties (e.g. ADMET).

Devised a unified, common interface for disparate generation methods. This facilitated the parallelization of molecule generation tasks across multiple tools and their integration into data pipelines.

Implemented a suite of enumeration tools based on molecule generation algorithms from academic papers, open-source packages, etc.

Created a Django web application allowing chemists to configure a selection of tools to run on a list of molecules.