Act as Principal Investigator for United States' National Institutes of Health (NIH)-funded computational projects: $2.2M Awarded, Grant#: 5R44TR002035-03.Devise, implement, and steward novel Data Science research projects on the application of Next Gen Sequencing (NGS) data toward drug discovery.Develop robust AI/ML models for target identification and phenotype prediction.Develop procedures, tools, and workflows for data wrangling and cleaning, quality control, and analyses. Engineer robust data pipelines for NGS data processing, ensuring high-quality inputs for advanced AI/ML modeling in drug discovery projects.

Published machine learning research in Nature: Scientific Reports.Engineered and maintained data infrastructure for NIH BD2K-LINCS Initiative, enhancing accessibility and analysis capabilities for large-scale biomedical datasets.Developed AI/ML models to prioritize and rank bioactive compounds.Co-developed chemical and multi-omics data standards to facilitate data integration and maximize adherence to FAIR data principles.Streamlined data management processes, significantly reducing curation and quality control efforts while improving the efficiency of data integration pipelines.Co-developed the NIH's LINCS Data Portal and LINCS Data Portal 2.0.Co-developed the Smart API, a data standard and associated tools, which aim to maximize the adherence of web-based Application Programming Interfaces to FAIR data principles. Fostered interdisciplinary collaborations, bridging gaps between computational scientists and biomedical researchers to drive innovative drug discovery approaches.

Developed computational models that led to the discovery of the first ever reported correlation between biological activity and electronic sub-structure.Engineered innovative regression analysis descriptors, enhancing drug activity predictions and contributing to QSAR modeling advancements in anti-malarial research.Drove measurable improvements in drug activity predictions through the development and implementation of custom analytical software solutions.Facilitated knowledge transfer by providing comprehensive training and ongoing support to students and faculty on custom analytical software tools.

Engineered cutting-edge analytical software, implementing novel algorithms for Scanning Tunneling Microscopy simulations, advancing computational chemistry research.Spearheaded applied molecular modeling projects from concept to publication, driving novel discoveries in computational chemistry research.Fostered interdisciplinary collaborations, integrating computational methods with experimental research to solve complex chemical problems.