Focusing on bringing AI deeper into drug discovery workflows: building agentic knowledge-discovery systems that integrate structured data, scientific literature, and experimental outputs; advancing multimodal transformer models for molecular property prediction; and delivering transparent interfaces and reproducible data/cheminformatics pipelines that make complex AI decisions interpretable.
📍 Austin, TX · M.S. CS, Texas A&M
| Languages | |
| AI / ML | |
| Backend | |
| Frontend | |
| DevOps & Cloud | |
| Philosophy |
| Year | Paper | Journal |
|---|---|---|
| 2026 | CAGE Fusion: Deep Learning for Nuisance Compound Detection Using a Gated Co-Attention Graph Embedding Model | Under Peer Review |
| 2025 | Integrating Chemical, Genetic, and Feasibility Assessments for Anti-Tubercular Target Validation | Under Peer Review |
| 2023 | DAIKON: A Data Acquisition, Integration, and Knowledge Capture Web App for Target-Based Drug Discovery | ACS Pharmacology & Translational Science |



