Focused on building intelligent systems that create real-world impact. I'm an MEng AI student at Duke University with experience in ML engineering, DevOps, and AI product development. I enjoy turning research ideas into practical solutions — whether that's a safety-critical CV system for search and rescue, a bias audit for transportation AI, or a multimodal fashion discovery engine. I bring a strong problem-solving mindset and love working at the intersection of AI and social good.
Duke Trust Lab
Jan 2026 — Present
BMW Group
Jan 2026 — Present
Duke University
Jan 2026 — Present
Assetmantle
Sep 2023 — May 2025
Hewlett Packard Enterprise
Jan 2023 — Jul 2023
95%
Python
85%
PyTorch
82%
TensorFlow
88%
FastAPI
85%
Docker
80%
Kubernetes
90%
AWS
75%
Google Cloud
85%
SQL
90%
Git
75%
Jenkins
85%
Linux
90%
Pandas
90%
NumPy
78%
JavaScript
72%
TypeScript
70%
React
68%
Next.js
92%
Jupyter
80%
MLflow
10 projects · hover to explore
Trained a lightweight RL policy network for multi-agent dialogue intervention using PyTorch and NLP. Reduced unnecessary interventions by 25% while maintaining task success rate, validated on a 10,000-dialogue simulation suite via A/B testing. Co-authoring a NeurIPS submission on safe agentic AI behavior under faculty supervision at Duke Trust Lab.
Fine-tuned Faster R-CNN on thermal SAR imagery with domain-specific augmentations (snow, smoke, sensor noise). Achieved 20% recall improvement under adverse conditions with <5% clean-data accuracy loss. Built end-to-end CV pipeline in PyTorch with custom data loaders and metrics optimized for safety-critical deployment.
Built and evaluated ML regression models (LightGBM, Ridge, MLP, Optuna-tuned TabularMLP) on real BMW dealer sales data to predict inventory turnover speed. Delivered explainable AI-driven inventory recommendations through a live REST API and interactive dealer dashboard, enabling data-informed spec decisions without requiring ML expertise.
Audited transportation AI allocation algorithms for racial and economic bias across Durham, NC — documenting that Black residents (32% of population) account for 47% of pedestrian/cyclist crash victims, driven by systematic undercounting of demand in low-income and high-minority census tracts. Built an interactive Leaflet mapping platform comparing AI-predicted vs. need-based infrastructure allocation using Census, NCDOT crash records, and OpenStreetMap data.
Built a reflective AI journaling companion using RAG over 8 psychological frameworks (CBT, IFS, NVC, Attachment Theory) to pattern-match emotional entries. Implemented cognitive distortion profiling across 4 distortion types and weekly self-awareness reports. Deployed full-stack Python/FastAPI on Railway with session persistence, mood trend tracking (1–10 daily score), and a trigger map logging recurring emotional patterns.
Built a film-to-fashion identification platform using FashionCLIP (512-dim embeddings) and FAISS GPU vector search over 20,000 Fashionpedia garment crops across 46 categories. Four-stage recommendation pipeline (FAISS → NeuMF → SASRec → diversity filter) with measurable NDCG@10 gains over a popularity baseline. Deployed FastAPI on Railway, Next.js on Vercel; evaluated on Precision@K, Recall@K, and MAP@K with 500 synthetic users × 30 interactions.
Built a SARIMAX inflation forecasting pipeline combining port traffic indicators with CPI data, reducing forecast error to 0.67–1.69% MAPE across major CPI categories. Validated model stability via 5-fold rolling cross-validation, achieving reliable predictive lift over CPI-only baselines.
Built a real-time, privacy-first Chrome extension estimating energy, carbon, and water footprint for AI prompts using model metadata and GitHub Models API. Implemented live footprint labels, heuristic + AI prompt optimization, and a client-side dashboard with daily impact summaries.
Built an EU AI Act compliance assessment system using TF-IDF + Logistic Regression, rule-based article evaluation (Articles 5, 6, 9, 10, 14), and automated remediation planning. Deployed full-stack ML workflow with MLflow, FastAPI, Docker, and Streamlit on Google Cloud Run, enabling explainable risk scoring and documentation audits.
Developed a semantic search engine for legal documents using embedding-based retrieval and citation-link modeling to surface semantically similar case law and statutes. Implemented vector search pipelines with passage ranking to support explainable legal research and faster knowledge discovery.
