machine learning engineer

gpa: 4.0 / 4.0

coursework: Machine Learning, Statistical Modeling, Web Mining & Recommender Systems, Efficient AI, ML Systems

gpa: 3.79 / 4.0

coursework: Neural Networks & Fuzzy Control, Linear Algebra, Probability & Random Processes

• —Deployed Neural Collaborative Filtering and a contrastive learning discovery engine to production, driving an 18% lift in recommendation CTR and 14% improvement in add-to-cart rate on the e-commerce platform.
• —Built customer scoring models using XGBoost and LightGBM on large-scale behavioral event data to predict purchase propensity and churn risk; outputs were integrated into CRM systems to trigger retention workflows, improving retention by 11%.
• —Rebuilt model training from single-node to distributed PySpark pipelines on Databricks, enabling production-grade modeling on datasets exceeding 500M rows and reducing training time by 85%.
• —Designed A/B testing infrastructure with K-Means user stratification to evaluate model changes in production; achieved a 6% NDCG@10 lift and 8% recall gain across live traffic, communicating results to product and engineering partners.
• —Designed and deployed MLWorks, a production model observability platform for real-time data drift, bias, and quality monitoring; reduced ML issue detection latency by 60%.

• —Built and deployed a production demand forecasting platform using time-series ensembles (FBProphet, ARIMA, SARIMA), achieving a 10% MAPE improvement and 8% inventory cost reduction across global markets.
• —Developed customer segmentation models using RFM analysis and K-Means clustering on transactional data, enabling targeted campaigns that improved conversion rates by 12% across high-value cohorts.
• —Automated global supply chain KPI reporting with Power BI dashboards, reducing executive reporting turnaround by 25%.

• —Built a Multi-Touch Attribution model using Markov Chains to quantify digital channel contributions, improving marketing budget efficiency by 15% across digital campaigns.

• —Built a personal film taste modeling system over 808 Letterboxd ratings, decomposing viewing preferences into 20 craft dimensions (narrative structure, pacing, cinematography, directorial lineage, etc.) via local LLM annotation; trained a 4-model ensemble selecting Random Forest as best performer with MAE 0.546 stars — 87.5% of predictions fall within ±1.0 star of actual post-watch ratings.
• —Engineered a 6-stage pipeline — TMDB enrichment → LangGraph annotation → NetworkX knowledge graph (3,505 nodes, 10,574 edges) → Qdrant vector indexing (768-dim embeddings) → React/TypeScript web UI — with Langfuse observability traces and a ground-truth logger for continuous prediction validation.

• —Built a hybrid collaborative filtering recommender on the Steam dataset (88,310 users, 32,135 games, 5.8M+ interactions) using bundle co-occurrence as an auxiliary signal alongside user–item interaction history; achieved a 79.5% Hit Rate@10, outperforming popularity baselines by 21 pp.
• —Evaluated across Precision@K, Recall@K, and NDCG on an 80/20 per-user split over 62,936 test users; tuned hybrid score blending via grid search and deployed an interactive Streamlit demo with precomputed recommendations.

• —Built a multi-agent RAG system with LangGraph orchestration, 2-hop citation-graph traversal (NetworkX), and GPT-4o synthesis with per-claim source attribution; demonstrated ≥10 pp citation recall improvement over vector-only RAG on a 50-question benchmark evaluated with RAGAS and DeepEval.
• —Engineered a LaTeX-aware chunking pipeline for equation-preserving ingestion, GPT-4o-mini entity annotation, Qdrant vector store with Cohere reranking, and Langfuse observability traces logging token cost and retrieval hops per query.

• —Conducted a 33-method empirical study of KV-cache compression and quantization for self-forcing video generation (Wan2.1), benchmarking peak VRAM, runtime, compression ratio, and VBench fidelity across methods.
• —Identified FlowCache-inspired soft-prune INT4 as optimal, achieving 5.4× compression and reducing peak VRAM from 19.3 GB to 11.7 GB while preserving generation quality in long-horizon inference.