🔭 Currently:
- Graduate Data Science student at the University of Arizona
- Researcher at the College of Cellular & Molecular Medicine, applying ML + LLMs to accelerate Drug Discovery & Repursposing
- Machine Learning Intern at OneData Software
🌱 Focus Areas:
- AI-driven biomedical research
- Large-scale data curation & reproducibility
- Building tools that move from nice-to-have → essential for researchers
🏆 Major Achievements:
->Successfully repurposed an FDA drug that can substitute for a novel candidate in a critical protein–protein interaction — reducing risk, improving reproducibility, and enabling faster translational research.
- Applied large language models (LLMs) and multimodal learning techniques to accelerate drug repurposing workflows.
- Built scalable pipelines for pre-training and distributed inference on biomedical datasets.
- Integrated molecular graphs and 3D structural data into LLM-driven models to capture complex biomolecular interactions.
- Leveraged post-training methods (reinforcement learning, contrastive learning, instruction tuning) to refine predictions.
-> Designed and deployed a mitochondrial DNA location classifier that cut experimental turnaround time from weeks to hours — enabling researchers to validate hypotheses in real-time.
👯 Looking to collaborate on:
Mission-driven biotech/AI startups tackling real-world data challenges
💬 Ask me about:
Machine learning engineering, LLMs, computational workflows, and turning technical prototypes into strategic impact
📫 Portfolio: gowthamg.info
😄 Pronouns: He/Him
⚡ Fun fact:
I get excited about the idea that large language models can learn not just text, but also molecular graphs, 3D structures, and even time-series data — meaning the same tech behind chatbots could one day help discover new medicines.
