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nabeegh-khan/README.md

Nabeegh Khan

Applied machine learning and research engineering. Master of Engineering student in Electrical & Computer Engineering at the University of Toronto, graduating April 2027.

Currently building two systems: a conversational agent that assesses code understanding from a student's own submissions, and a retrieval layer for clinical scale extraction that answers only where the transcript carries evidence and abstains otherwise. Alongside those, engineering education research at ISTEP.

Open to full-time roles from May 2027 in applied machine learning, research engineering, and software and data engineering.

Toolchain

Machine learning: PyTorch, Hugging Face Transformers, PEFT and LoRA fine-tuning, scikit-learn, statsmodels, ONNX Runtime and INT8 quantization, sentence-transformer embeddings

Retrieval and LLM systems: LangChain, ChromaDB, retrieval-augmented generation, semantic search, prompt engineering, BERTopic, spaCy, TF-IDF

Evaluation: RAGAS faithfulness and context-recall scoring, prompt-order bias testing, calibration and abstention, failure-mode diagnosis, LangSmith tracing, model-assisted coding validated against human assignment

Statistics: chi-square with permutation p-values, Fisher's exact, Mann-Whitney U, Kruskal-Wallis, ordinal and binary logistic regression, two one-sided tests for equivalence, Haberman post-hoc residuals, Benjamini-Hochberg and Bonferroni correction, Cramer's V and rank-biserial effect sizes

Data and infrastructure: Kafka, Spark Structured Streaming, DuckDB with dbt models and schema tests, Airflow, MLflow, Evidently drift monitoring, Docker, FastAPI with Pydantic validation, Databricks, Azure Synapse, Scala, SQL

Collection and linkage: REST API harvesting with cursor pagination and rate limiting, PDF text extraction, record linkage, deduplication, schema reconciliation across mismatched sources

A note on tooling

The projects here were built with substantial AI-assisted coding. Problem formulation, method selection, experimental design and interpretation are my own; AI was used for implementation scaffolding, and every result was validated against source. Each repository documents this.

ORCID · nabeegh.as.khan@gmail.com · Cambridge, Ontario · Canadian and UK citizen

Pinned Loading

  1. real-time-anomaly-mlops real-time-anomaly-mlops Public

    End-to-end streaming MLOps pipeline for time-series anomaly detection on the NAB benchmark: Kafka, Spark, MLflow, FastAPI, Airflow, and Evidently wired together across the full lifecycle.

    Jupyter Notebook

  2. 3gpp-rag 3gpp-rag Public

    Retrieval-augmented QA over 13 3GPP NR specs (4,493 pages), with cited answers and RAGAS evaluation. Ask a spec question, get an answer grounded in the source with the page cited.

    Jupyter Notebook 2

  3. 6g-lwm-beam-prediction 6g-lwm-beam-prediction Public

    LoRA fine-tuning of the LWM wireless foundation model for 64-beam mmWave prediction across three DeepMIMO scenarios. Rank-4 adapters (4.82% of parameters) match full fine-tuning within 0.3 points o…

    Jupyter Notebook

  4. ai-education-mixed-methods ai-education-mixed-methods Public

    Mixed-methods study of student AI perceptions, triangulating survey data (n=625) with 465 Reddit posts. Surveys rate AI highly while the academic-writing discourse is the most negative topic, a uti…

    Jupyter Notebook