About

I build production machine-learning and data systems: forecasting models, real-time pipelines, and the cloud platforms that run them. At CitiEU — CitiME in Milan I built and operate a traffic-forecasting platform end to end — a LightGBM system predicting travel times up to 4 hours ahead, a real-time ingestion layer processing ~27M rows/day into TimescaleDB, and LLM-generated reporting — deployed on GCP with CI/CD.

Before that, I researched public transit equity at Institut Polytechnique de Paris, published as first author at hEART 2022 and TRB 2023.

Experience

Transportation Data & Modelling Specialist

CitiEU — CitiME · Milan, Italy · Mar 2023 – Present

  • Designed and operate a hybrid ML forecasting system — a global LightGBM model trained on pooled route history (~1.5–3M observations) blended with per-route specialist models by recent accuracy — predicting route travel times 15 min to 4 h ahead: ~1.7 min MAE at 15-minute lead, 16–50% better than a typical-day baseline, with quantile + conformal-calibrated P15–P85 uncertainty bands (~80–85% empirical coverage).
  • Own the full ML lifecycle in production: nightly automated retraining, every forecast graded continuously against actuals per route, lead time, and model variant, and cold-start handling for new routes through the global model.
  • Built the platform's real-time ingestion layer: ~27M rows/day of per-minute traffic telemetry into PostgreSQL/TimescaleDB — hypertables with columnar compression (371 GB logical stored in 137 GB) and continuous aggregates that keep month-scale analytics off the raw data.
  • Run three GCP environments (production, staging, delivery) with GitHub Actions CI/CD: health-gated auto-deploys, PR tests against a real TimescaleDB service container (~75 Node test files), Secret Manager for all credentials, and idempotent SQL migrations on every deploy.
  • Integrated the Anthropic API for AI-generated traffic incident and trend reports, and built a bash multi-agent audit framework (Claude + Codex) running 8+ specialised review roles per script for code, UI/UX, and performance audits.
  • Migrated a legacy Cube Voyager transport model to CubePy, cutting end-to-end runtime by 40% on the Abu Dhabi Mobility model, and trained 50+ modellers on the new stack.

Research Assistant (Data Science)

Institut Polytechnique de Paris — LINCS Lab · Paris, France · Nov 2021 – Nov 2022

  • Built a Python pipeline ingesting multi-city GTFS feeds into MongoDB and computing accessibility metrics (Lorenz curves, Gini indices) entirely from open data.
  • Contributed to the French ANR MuTAS project (ANR-21-CE22-0025-01); output published at hEART 2022 and TRB 2023 as first author.

Projects

IntelligentMobilityHub

Production ML & data platform · CitiEU — CitiME

Traffic-forecasting platform run in production: per-minute telemetry (~27M rows/day) ingested into TimescaleDB, a FastAPI/LightGBM service forecasting travel times up to 4 h ahead with calibrated uncertainty bands, MapLibre GL live map, and Anthropic-API-generated reports — deployed on GCP with health-gated GitHub Actions CI/CD.

LightGBMTimescaleDBFastAPIGCPGitHub Actions

bikeflow

Personal project · live dashboard →

Open, end-to-end data pipeline collecting real-time bike-share availability for Milan and Paris (GBFS): Dockerized collector on a GCP VM, Airflow 3 orchestration (compaction, GCS backups, freshness alarms), CI, and an hourly-published public dashboard. Built as the open counterpart to my production work — LightGBM forecasting layer in progress.

AirflowDockerGCPParquetGitHub Actions

Abu Dhabi Mobility Model

Migration & performance · CitiEU — CitiME

Ported Voyager scripts to CubePy and resolved an ArcGIS Engine dependency blocking the model on newer environments; cut end-to-end runtime by 40% and trained 50+ modellers on the migrated stack.

CubePyCube VoyagerPython

Public Transit Equity Analysis

Research · IP Paris

Stored and queried multi-city GTFS feeds in MongoDB; computed Gini- and Lorenz-based equity scores at the transit-line level using open data only. Output: two first-author conference papers.

PythonMongoDBGTFS

Publications

Assessing Transportation Accessibility Equity via Open Data

Badeanlou, A., Araldo, A., Diana, M. · hEART Conference 2022

A fully automated methodology to measure transportation accessibility inequity using Lorenz curves and Gini indices, relying solely on open data — demonstrated on four cities and compared with existing approaches.

Equity Scores for Public Transit Lines from Open Data and Accessibility Measures

Badeanlou, A., Araldo, A., Diana, M., Gauthier, V. · Transportation Research Board (TRB) 2023

An equity scoring system for public transit lines aimed at reducing car dependency in underserved areas — measuring and improving accessibility equity across seven cities from open data, to guide large-scale transit optimization.

Skills

Machine Learning & AI
LightGBMQuantile regressionConformal calibrationFeature engineeringModel monitoringAnthropic APIClaude Code / MCP
Data Engineering
PostgreSQLTimescaleDBReal-time ingestionETL/ELTREST APIsMongoDBSQLite
Cloud & DevOps
GCP Compute EngineSecret ManagerBigQueryGitHub Actions CI/CDpm2Bash
Programming
PythonpandasNumPyGeoPandasSQLJavaScriptNode.jsFastAPI
Geospatial & Visualisation
MapLibre GLQGISArcGISGTFSDashboards
Transport Modelling (domain)
OpenPaths CUBECubePyAimsunFour-step modelling

Education & Honors

Politecnico di Torino

M.Sc. Civil Engineering — Infrastructures & Transportation Systems · Turin, Italy · 2019 – 2022

Sharif University of Technology

B.Sc. Civil Engineering · Tehran, Iran · 2015 – 2019

  • Rapid promotion — promoted to Abu Dhabi Mobility Transport Modeller within 12 months (typical track: 3 years).
  • Full M.Sc. scholarship — awarded for the duration of the Politecnico di Torino programme.
  • Research grants — funding secured from three independent sources for the Paris-based master's thesis.
  • Conference presentation — presented first-author work at hEART 2022.

Certifications