MS CS @ Georgia State · software engineer · ML researcher
I build across the stack: from backend systems to on-device AI inference.
previously data analyst @ PreProd Corp
🍳 home cook · 🎸 guitarist ·
Languages Python · Java · C · TypeScript · Bash
ML/DL PyTorch · TensorFlow · HuggingFace · Transformers · scikit-learn
Data pandas · NumPy · PostgreSQL · Redis · MariaDB · KNIME
Backend FastAPI · REST APIs
Infra Linux · Docker · Git · GitHub Actions · MLflow
Practices OOP · CI/CD · MLOps · system design
Workflow GitHub · Jira
- MS CS @ Georgia State — GPA 4.15 / 4.30
- research: distributed AI · efficient inference · computer vision
- open to summer 2026 internships — SWE, ML, or data
- 📍 Atlanta, GA
⚡ PADQ — Prompt-Aware Dynamic Quantisation
Zero-shot routing system that dynamically switches between INT4 and FP32 LLM inference based on prompt complexity — optimised for edge deployment.
Achieved 72.6% energy reduction, 39.1% latency improvement, and 1.5–2.2× throughput gains on mobile hardware.
TinyLlama · Qwen3-0.6B · iPhone A16/A18 · on-device inference
🔬 RobustCAM — Faithful Grad-CAM for Lung CT Classification
ResNet50 fine-tuned on IQ-OTH/NCCD (normal / benign / malignant), achieving val_acc=0.8813 vs. published baseline of 0.85. Fuses Grad-CAM across augmented views and cross-validates with LIME and SHAP using a 9-metric faithfulness suite.
ResNet50 · Grad-CAM · LIME · SHAP · MLflow · IQ-OTH/NCCD
I came into CS through data — spent time as a data analyst before moving into research and engineering. That path shaped how I think: I care about whether systems are measurable, reproducible, and actually useful outside a notebook. I like problems that sit at the boundary between research and deployment.
"building things that make sense on the edge"