Data Engineer · West Haven, Connecticut · MS Computer Science, University of New Haven
Building the pipelines that carry data, and the models that read it.
Spark · Airflow · dbt · Kafka · Snowflake — and applied ML across green computing, industrial prognostics, financial risk and medical imaging.
I move data for a living. Batch and streaming ingestion, ETL and ELT, warehouse and lakehouse design, orchestration, and the governance that keeps all of it trustworthy — at Capital One on regulatory and risk reporting, at Mindtree on large historical datasets, and now at SimanPro.
Alongside that I co-author applied machine learning research. The two are less separate than they look: most of what goes wrong in a published model is a data problem, and most of what I fix at work is a data problem.
Reference implementations of methods from papers I have co-authored. Each one ships an evaluation protocol that does not cheat and tests that fail when it does. None of them quote an accuracy figure, because none of them shipped with the dataset that produced one.
| Repository | What it is |
|---|---|
| green-anomaly-detection | Anomaly detection where every model reports its energy and carbon cost beside its detection score. The one repository here with numbers, because scripts/benchmark.py measures them. |
| equipment-failure-prediction | Hybrid CNN-LSTM predictive maintenance, with an asymmetric loss — predicting failure late costs far more than early — and cost-optimal alert thresholds. |
| adaptive-financial-risk | Value-at-Risk that notices the regime changed. Drift detection, walk-forward backtests, Kupiec and Christoffersen. |
| seattnfusionnet-throat | Squeeze-excitation attention fusion for pharyngitis screening, with the attention weights exposed and tested rather than assumed. |
| hybrid-brain-tumor-mri | Brain tumour MRI classification, shipped with the leakage audit that decides whether any accuracy figure from it means anything. |
| onco-imaging-toolkit | The preprocessing, imbalance and Grad-CAM layer the two medical studies share — including the randomisation check that says when a heat map is decoration. |
| Repository | What it is |
|---|---|
| streamforge | Kafka-to-lakehouse streaming: watermarks, late-data policies, exactly-once effect, schema evolution that explains why a change is unsafe. |
| lakehouse-dbt-models | A dimensional model that actually runs — DuckDB, so the whole warehouse builds in CI with no credentials. Type 2 SCDs with enforced invariants. |
| dataguard | Data contracts that fail the build, column-level lineage, and PII classification that propagates downstream instead of going stale. |
| airflow-patterns | Idempotent backfills, the data-interval trap, and sensors that do not starve the worker pool — all testable without importing Airflow. |
| Retail-data-engineering-pipeline | End-to-end retail ETL: Python, PostgreSQL, Airflow, Docker. |
Voice impersonation detection (undergraduate thesis, LSTM + LIME, BRAC University) · Mobile Exchange Store (Flutter) · Line Follower Robot (Webots) · Daily Expense Tracker (PHP)
Every repository here is MIT licensed and runs its tests on Python 3.10, 3.11 and 3.12.
No performance number appears in any of them unless a script in that repository measured it.