I am a Ph.D. researcher in Computer Engineering at the University of South Florida, working on Medical AI with a focus on Machine Learning, Deep Learning, and Generative AI for real-world healthcare applications. I previously completed my M.S. in Data Science at Texas A&M University.
My work sits at the intersection of research and engineering — I design models, build reproducible pipelines, run rigorous evaluations, and deploy systems that move beyond notebooks. I am particularly interested in multimodal and language-centered learning, tool-augmented ML systems, and scalable ML infrastructure.
I approach problems using first-principles thinking, emphasizing clarity, measurable impact, and end-to-end ownership — from raw data to deployed models.
📬 Let's connect — feel free to reach out via email or LinkedIn.
📌 Currently seeking ML / AI Research Internships (2025–2027).
| Area | Capabilities |
|---|---|
| Medical AI | Vision-based ML for healthcare imaging and analysis |
| Machine Learning | Classical ML, Deep Learning, model optimization |
| Computer Vision | Image understanding, 3D reconstruction, multimodal vision |
| Generative AI | LLMs, diffusion models, representation learning |
| ML Systems | Data pipelines, training workflows, evaluation & ablations |
| Deployment & Scale | Dockerized workflows, cloud execution (AWS, HPRC) |
| Analytics | SQL-driven analysis, dashboards, reproducible notebooks |
- PhD research applying ML/DL and Generative AI to medical and healthcare domains
- Advanced topics in LLMs, multimodal learning, and generative modeling
- End-to-end ML projects using Python, PyTorch, SQL, Docker, and AWS
- Experimentation, benchmarking, and performance evaluation of ML models
- Open-source contributions and collaborative research in AI & Data Science
- Medical AI & Healthcare Applications
- Applied Machine Learning & Deep Learning
- Computer Vision & 3D Reconstruction
- Multimodal AI (Vision–Language Models)
- Natural Language Processing & LLMs
- Data Analytics, Visualization & Storytelling
- ML Model Deployment & Scalable Workflows
| Category | Tools |
|---|---|
| Core Programming & Version Control |    |
| ML / Deep Learning |     |
| Data, Databases & Analytics |    |
| MLOps, Deployment & Cloud |    |
| Development & Experimentation |   |
My Development Environment
- OS: macOS (Unix-based ML workflows)
- Editor: VS Code (Python, PyTorch, debugging & profiling extensions)
- Terminal: Zsh (Oh-My-Zsh, productivity-focused aliases)
- Experimentation: Jupyter, script-based experiments, reproducible configs
- Version Control: Git & GitHub (PR-based, documentation-first workflow)
- Visualization & Reporting: Matplotlib, Seaborn, lightweight dashboards
- First-Principles Thinking — break problems down to fundamentals, then rebuild with intent
- Build for Impact — favor solutions that deliver measurable, real-world value
- Clarity Over Cleverness — readable code, explainable models, clean system design
- End-to-End Ownership — from data ingestion and modeling to evaluation and deployment
- Long-Term Thinking — optimize for scalability, robustness, and sustainable growth
When I hit a dead end, I step back and re-derive the problem from first principles — more often than not, the solution appears faster than brute-forcing code. ☕✨
If you find my work useful, consider starring ⭐ a repository.
Always open to collaboration and impactful projects.