Advancing Health
through Applied AI
PhD Candidate specializing in practical AI applications for clinical healthcare. Developing robust systems for real-world medical impact.
// CORE_COMPETENCIES
Research Interests
for Clinical Systems
Clinical AI Integration
Developing models that seamlessly bridge the gap between bench research and bedside clinical workflows. Focus on human-in-the-loop systems that enhance practitioner decision-making without disrupting established medical protocols.
Predictive Analytics
Leveraging deep learning for real-time patient risk stratification and preventive care optimization in acute care settings.
Medical Imaging
Designing robust computer vision systems for automated, high-precision diagnostic support across MRI, CT, and histopathology.
Real-World Practicality
Investigation of model robustness, interpretability, and ethical deployment in diverse hospital environments with heterogeneous data sources.
Featured Projects
ECHO PARSER / ECHO REPORT CLASSIFIER
A Python tool that parses echocardiogram PDF reports and applies automated classification logic based on ASE and BSE guidelines. Extracts LVEF, diastolic function, heart failure subtypes (HFrEF/HFmrEF/HFpEF), valve disease severity, and LV geometry — outputting an annotated Excel dataset ready for ML training.
CLARA / MEDICAL NUTRITION AI
A domain-specific medical nutrition AI built by fine-tuning Qwen2-1.5B-Instruct on Malaysia Ministry of Health maternal dietary guidelines using Unsloth with 4-bit QLoRA. Deployed as a FastAPI REST API with LangChain integration, designed to run on consumer-grade hardware (RTX 3050, 4GB VRAM).
SCHOLARSYNC / PAPER QUEUE MANAGER
A cross-platform scholarly reading queue manager with an iOS app (SwiftUI), Next.js web dashboard, and Manifest V3 browser extension — all sharing a Supabase backend. Scans DOI, ISBN, and arXiv IDs, auto-tags papers with capture location, and exports to Zotero in BibTeX/RIS/CSV formats.
SCRIBESTREAM / ADIME DIETITIAN AI SCRIBE
A fine-tuned AI scribe built for clinical dietitians, structured around the ADIME documentation framework (Assessment, Diagnosis, Intervention, Monitoring & Evaluation). Automates clinical nutrition charting to reduce documentation burden in hospital settings.
// SYSTEM_INFRASTRUCTURE
Homelab Setup
A personal research server running Ubuntu Server, used for AI model training, local inference, dataset storage, and self-hosting — all tunnelled securely to the internet via Cloudflare without port forwarding.
- RTX 3050 (4GB VRAM) / QLoRA fine-tuning & local inference
- 512GB + 1TB SSD / Dataset & model storage
- Cloudflare Tunnel / Zero-trust self-hosting, no exposed ports
// OPEN_SOURCE_MODELS
LLM Fine-tuning
Adapting Llama-3 and Mistral architectures for clinical reasoning using LoRA and QLoRA techniques on local hardware.
Medical Imaging
Training vision transformers (ViT) and CNNs for specific diagnostic tasks using open-source PyTorch frameworks and TorchVision.
Local Inference
Optimizing model deployments using vLLM and TensorRT-LLM for low-latency inference on consumer-grade hardware.
Check out my contributions
I regularly release quantized weights and training scripts for medical-domain adaptations of popular open models.
Let's Collaborate
Interested in healthcare AI research, practical clinical deployments, or speaking engagements? My terminal is always open.