I'm Alp
I'm a Sophomore Computer Science student at New York University, and I build counterintuitive systems that solve real problems. I focus on full-stack platforms and real-time AI pipelines where clear architecture, modular design, and strong data flow matter more than buzzwords. My work centers on turning unconventional ideas into reliable, end-to-end solutions.
Featured Projects
Selected works that demonstrate full-stack & AI capabilities
CrisisTriageAI
Real-time multimodal triage platform that analyzes text, voice, and live phone streams to detect emotional distress and classify risk levels. Runs entirely locally with no external API dependencies.
Demonstrates production-grade ML engineering with privacy-first architecture and real-time streaming constraints.
What I Built
- Designed modular FastAPI backend with WebSocket streaming for real-time audio and text processing
- Implemented local Whisper ASR transcription and Librosa-based prosody extraction (pitch, speech rate, pauses)
- Built custom DistilBERT-based neural classifier for risk-level classification with 73+ pytest tests
- Developed Next.js dashboard with live triage view, session history, and analytics aggregation
- Architected privacy-first system: all inference runs locally, ephemeral data handling, no cloud dependencies
Sub-second inference latency on local hardware; zero external API calls for core triage functionality
Tech Stack
Facade Risk Analyzer
AI-powered building assessment platform that processes facade images to detect structural defects, assign risk scores, and generate condition reports with repair cost estimates.
End-to-end AI application with pluggable analyzers, automated reporting, and production-ready CI/CD.
What I Built
- Designed modular FastAPI pipeline with pluggable analyzers (OpenAI Vision, Mock, Replay) for flexible deployment
- Implemented SQLite-backed jobs engine with output versioning and automated PDF report generation
- Built Next.js frontend with job dashboards, analysis history, and shareable report links
- Created CI/CD workflows with GitHub Actions, reproducible fixtures, and CLI tools for local orchestration
- Delivered working platform that processes images end-to-end from upload to downloadable PDF report
Complete image-to-PDF pipeline in under 30 seconds; deterministic replay mode for testing and demos
Tech Stack
ML & Public Health Modeling
Work in progress research project analyzing fairness gaps in public-health AI systems. Using CDC PLACES and U.S. Census socioeconomic data, I help build and evaluate baseline ML models that predict diabetes and cardiac-disease prevalence at the ZIP-code level.
Research into whether health prediction models systematically underpredict risk in low-income communities.
What I Built
- Building baseline ML models for diabetes and cardiac-disease prevalence prediction at ZIP-code level
- Analyzing CDC PLACES public health data combined with U.S. Census socioeconomic indicators
- Evaluating model fairness across income brackets and demographic groups
- Investigating systematic underprediction patterns in low-income communities