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Dylan Tang
Intern Software Developer and ML Researcher specializing in medical imaging and computer vision
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About
AI/ML practitioner with experience spanning audio/LLM applications (built "Iota" using Whisper, tiktoken, and a local Ollama-served LLM) and healthcare ML (Facemed.ai; UChicago Radiology). Demonstrates a production-oriented mindset—focus on data/model fit, deterministic field testing, and operational safeguards—and has improved research evaluation workflows via a hash-table-based concurrent model tracking approach.
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Key Strengths
- Built an audio-to-LLM application pipeline (Whisper transcription + tokenization + downstream LLM response)
- Hands-on experience adapting AI systems to real-world hardware/audio constraints (multi-channel mic configuration, 16 kHz processing)
- Strong understanding of why static training data/context windows limit real-world/embodied deployments (latency, resource constraints, dynamic environments)
- Applied ML to healthcare context; identified transferability issues from clean/preprocessed data to real patient tests (Facemed.ai)
- Improved research ML evaluation scalability by designing a hash-table-based tracking system enabling concurrent multi-model evaluation
- Production-minded ML approach: data/model fit, deterministic reliability, monitoring/logging, and rollback controls
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Publications
2 publications
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