Daniel Crawford

Current Role

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Founding Machine Learning Engineer, Dunedain

Working with the founding team the development of advanced machine learning and AI capabilities for a defense-focused startup. Building scalable ML pipelines, deploying real-time AI solutions, and collaborating closely with our highly talented team to deliver mission-critical technology for helping the Warfighter.

Selected MVP(-ish) Projects

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BookFinder AI

Built a real-time book identification pipeline using YOLOv8 for object detection, integrated webcam capture, and Google Books API retrieval to match physical book covers to online metadata with high precision.

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ELECTRA: Electrification Leveling and Efficiency Cost-Targeting ROI Analyst

Designed and built ELECTRA, an end-to-end platform for small-industrial electrification, combining ML-driven upgrade recommendations, interactive ROI simulations, and real-time energy profiling to empower data-backed decarbonization decisions.

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Sentinal RAG

Built a RAG-LLM pipeline for real-time military support, integrating Whisper, FAISS, and GPT-4 to transcribe and embed open-source video data, delivering mission-relevant context and verifiable citations to the warfighter.

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Real Time Anomaly Detection with a pyTorch Autoencoder

Developed a real-time anomaly detection system using a customizable PyTorch autoencoder pipeline, optimized for streaming data with threshold-based alerts and deployed on NYC taxi datasets for practical use-case benchmarking.

Former Ventures + Start-Up Experience

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Elfur, Inc: AI Resume Matching

Pre-Seed Founding LLM/NLP Engineer

Led the design of LLM-powered resume summarization pipelines at Elfur, integrating MongoDB, OpenAI, Hugging Face, and LLAMA APIs to automate data workflows and reduce model training time by 300%.

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Kero Sports

Post-Revenue-Pre-Profit ML Engineer

Deployed and optimized real-time event prediction models with 99.5% calibration accuracy, leading FastAPI-based GCP deployment that cut latency by 20% and boosted pipeline efficiency by 66%.

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Optimal Dynamics

Post-Revenue-Pre-Profit AI Engineer

Built automated ML workflows and co-developed a deep RL supply chain model that drove $10M+ in profit, reducing analytics delivery time by 85% and leading data efforts a 5-person client-facing sales engineering team.

Academic Projects + Papers

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Deep Learning for Satellite Orientation

Developed a deep reinforcement learning agent to autonomously and time-optimally orient satellites in 3D space using an Actor-Critic model trained in a simulated control environment.

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Lexical Access Prediction from Semantic Embedding

Built a semantic embedding model to predict whether speakers access complex words directly or compositionally, advancing NLP applications in lexical cognition and morphology.

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Multi-Indicator Computational Trading Portfolio Management

Built and optimized a multi-indicator machine learning strategy learner to outperform traditional benchmarks in stock trading, using adaptive window tuning and ensemble signal voting for dynamic portfolio decisions.

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A Discrete Optimization Approach to Airline Flight and Captain Scheduling

Developed a binary linear programming model to minimize flight crew costs by optimally assigning captains to flights under FAA constraints, enabling scalable, regulation-compliant airline scheduling.