Computer Science and Biomedical Sciences student at UCF with research experience in AI safety for generative biology and full-stack development of healthcare AI applications. Developed novel methodology achieving 28% higher efficacy than state-of-the-art approaches in bio-foundation model safety evaluation.
View Work →Designing end-to-end evaluation workflows and testing protocols for bio-foundational AI models. Developed methodology that bypassed existing safety protocols in Evo2 with 28% higher success rate than state-of-the-art approaches.
Building AI-powered systems for clinical environments, from agentic administrative automation to on-device computer vision for patient assessment. Focused on HIPAA-compliant, offline-capable architectures.
End-to-end application development across web, mobile, and cloud infrastructure. Experienced with React, React Native, Flutter, Swift, and GPU-accelerated inference server deployment.
Agentic AI framework automating administrative workflows for private medical practices. Validated product-market fit through 50+ physician interviews across multiple specialties. Multi-agent orchestration with Light-RAG repository and LangGraph reduces prior authorization processing from an industry average of ~20 minutes to 2 minutes per request. End-to-end voice-note-to-EHR pipeline completes full documentation in 11 minutes per encounter, outperforming the 16-minute physician average. Custom integration pipeline enables practice-specific configuration for scheduling, billing inquiries, and documentation.
Mobile application combining computer vision and a small language model to assess patient walking ability and reduce fall risk using the PEER clinical framework. Computer vision model processes movement analysis at 30 FPS on-device for real-time gait assessment without cloud dependency. On-device language model delivers psychological reinforcement responses in 43 seconds with zero network latency for offline clinical usability. HIPAA-compliant backend on Google Cloud Services with React Native frontend optimized for geriatric patient accessibility.
End-to-end application using React (web), Swift (iOS), and a fine-tuned DreamOmni2 model with LoRA to generate estimated post-surgery facial outcomes for plastic surgery consultations. GPU-accelerated inference server processes patient photos from the mobile app and returns AI-generated predictions to a clinician-facing web dashboard. Safety guardrails restrict model input and output to facial imagery only, preventing misuse.
VQ-VAE-based compression system for whole slide pathology images, achieving 98.8% file size reduction (272 MB to 3.24 MB) while preserving diagnostic quality. Built in 36 hours at SWORL (Startup Weekend Orlando) as a proof of concept. Validated through interviews with 7 medical professionals who confirmed clinical need for long-term digital slide storage and sharing. React frontend with Flask API backend deployed on a cloud GPU instance (vast.ai L40S) for real-time compression and decompression.
Cross-platform Flutter application for campus event discovery. Translated Figma designs into responsive custom widgets ensuring consistent experience across Android and iOS. Optimized feed performance through lazy loading and infinite scroll pagination. Achieved app-wide accessibility compliance through Lighthouse audits and screen reader testing.