Omar Laurino

Smithsonian Astrophysical Observatory — Senior Software Engineer

Westford, MA · [email protected]

I'm a senior software engineer with the Chandra X-ray Center at the Harvard & Smithsonian Center for Astrophysics. Everyday I work on keeping the observatory’s software up do date. Earlier in my career I worked at complex data metamodeling effort in the context of the International Virtual Observatory Alliance, in an effort to improve interoperability of data and software across global astronomical institutions. I also helped launch astroinformatics projects such as DAME, CLaSPS, and the Weak Gated Experts method for photometric redshifts; those machine-learning and architectural efforts now inform how I design dependable tooling for today’s missions even though that research line is no longer my daily work.

• ORCID
• Google Scholar

Experience

• Senior Software Engineer · Chandra X-ray Center (SAO) Feb 2014 – Present

  HYBRID · Cambridge, MA

  • Led the Sherpa tool modernization, shifting the project off ClearCase, rebuilding packaging, and positioning Sherpa as a standalone open-source distribution alongside Astropy and other Astronomy ecosystems.
  • Went spelunking through decades-old C++/Python, carving out cleaner seams and adding gtest/pytest coverage so the team can refactor without holding its breath.
  • Turned a one-off helper script into Runpipes, a TypeScript web app that lets developers queue, inspect, and profile catalog pipelines from their laptop or a shared cluster node.
  • Spent time shoulder-to-shoulder with catalog scientists to design a QA workspace that organizes and launches their sessions, whether working remotely or locally.
  • Prototyped the CfA Nexus, a cloud-native, integrated data analysis platform for the Center for Astrophysics | Harvard & Smithsonian.
  • Wrote calibration helpers that collapsed multi-hour jobs into seconds.
• Vice Chair, Data Modeling Working Group · International Virtual Observatory Alliance (IVOA) Apr 2011 – May 2014

  HYBRID · Global

  • Co-led the IVOA Data Modeling Working Group to deliver interoperable data/metadata standards across global astronomical archives.
  • Coordinated cross-project specification reviews, aligning working groups so new standards shipped with shared implementation guidance and consensus.
  • Founded and led the VO-DML Tiger Team, serving as lead editor for the VO Data Modeling Language and its mapping specification to give the VO a modular, serialisable meta-model.
  • Worked hands-on with international observatories and data centres so VO-DML, time-series, and space-time coordinate models addressed operational use cases including multi-dimensional data cubes.
  • Delivered technical presentations, facilitated multi-institution reviews.
• Architect and Co-Lead Developer, Iris SED Analysis Tool · Smithsonian Astrophysical Observatory · Virtual Astronomical Observatory Feb 2011 – Aug 2014

  ON-SITE · Cambridge, MA

  • Architected the Iris desktop application for building, visualising, and modelling spectral energy distributions, integrating VOTable/SAMP standards with Sherpa’s fitting engine.
  • Designed the plug-in framework and SDK so third parties could add services and tools.
  • Co-authored the 2014 Astronomy & Computing paper and presented at a number of IVOA Interops.
• Research Fellow — Virtual Observatory Standards · INAF/OATs & INFN Trieste Sep 2009 – Feb 2011

  ON-SITE · Trieste, Italy

  • Designed and delivered VOdka, an asynchronous agent that monitors VO services, snapshots resources, and automates data harvesting for users.
  • Built VODance, a Django/GlassFish framework that let non-VO experts publish ConeSearch/SIAP services directly from MySQL tables with metadata mapping and policy controls.
  • Collaborated on VO standard drafts and prototypes, ensuring OATs and INFN implementations aligned with broader DAME/Iris astroinformatics tooling.
• Principal Engineer, DAME / DAMEWARE · University of Naples Federico II · INAF-OACN · Caltech Jan 2007 – Feb 2011

  ON-SITE · Naples, Italy

  • Led the architecture and core implementation of DAME/DAMEWARE, a web-based, Virtual Observatory–compliant astro-ML platform delivering multi-tenant workflows for classification, regression, and clustering at survey scale.
  • Designed service-oriented orchestration that bridged VO standards with grid/HPC backends such as S.Co.P.E., letting scientists compose reproducible knowledge-discovery pipelines across heterogeneous resources.
  • Implemented reusable model catalogs and dataset adapters so researchers could swap neural nets, SVMs, SOMs, and other algorithms without touching data plumbing.
  • Partnered with INAF, Caltech, and University of Naples teams to productionize science cases (AGN/galaxy classification, globular-cluster searches) and published the findings.
  • Designed the Weak Gated Experts method for photometric redshift estimation on SDSS galaxies and quasars, pairing unsupervised clustering with specialized regressors and per-object uncertainty estimates.
  • Validated the approach on quasar candidate catalogues, achieving competitive accuracy and efficiency that later informed astroinformatics publications and invited talks.
• System Administrator · INFN Sezione Napoli · University of Naples Federico II Jan 2006 – Dec 2007

  ON-SITE · Naples, Italy

  • Maintained INFN Naples high-performance compute, grid, and storage services supporting physics and astroinformatics workloads.

Projects

• Chandra Source Catalog Pipelines — Senior Software Engineer

  Wrote or rewrote astronomical pipeline tools for the Chandra Source Catalog.

  • Optimized legacy algorithms for performance and reduced runtime by orders of magnitude.
  • Worked in close collaboration with mission scientists to design and build new pipeline tools, turning science requirements into reality.
  • Modernized 30-year-old legacy codebases to use modern C++ and Python idioms.
• CfA Nexus Science Platform — Principal Engineer & Lead Architect

  Concept-to-architecture effort for a modular, multi-archive science platform spanning CfA data centers and literature services.

  • Prototyped a cloud-native platform that unifies access to cross-observatory Center for Astrophysics | Harvard & Smithsonian data within reproducible Jupyter environments.
  • Established GitOps-driven infrastructure-as-code using Pulumi with dependency-injected components, enabling repeatable deployments and modular extension points.
  • Evaluated and adapted Rubin Science Platform patterns to CfA needs, defining integration hooks for authentication, storage, TAP services, and collaborative compute workflows.
• Chandra Source Catalog QA Platform — Architect & Full-Stack Engineer

  Unified the quality assurance toolchain so scientists can review intermediate catalog products rapidly.

  • Built a reactive dashboard that responds to downloaded "QA"s, dispatches the appropriate web or desktop applications, and tracks file bookkeeping.
  • Streamlined QA work both remotely and locally with a flexible configuration framework.
  • Embraced the diversity of the QA tools and designed a smooth UX around it.
  • Rewrote some of the QA tools for a better UX, better performance, or both.
• Runpipes Pipeline Orchestrator — Principal Developer

  Cloud-ready workflow runner that schedules, profiles, and debugs Chandra pipelines end-to-end.

  • Grew a one-off script into a web service you can run on a laptop or a shared cluster without changing your workflow.
  • Added queueing, telemetry, and timing hooks that surfaced bottlenecks and drove fixes with the science team.
  • Gave developers a single place to run, debug, and compare pipelines, shrinking typical debug loops.
• Nextcast Toxicogenomics Workflow Suite — Cloud Workflow Architect & Contributor

  Modular software collection for building, orchestrating, and running toxicogenomics analysis pipelines end-to-end.

  • Co-designed the overall workflow architecture described in the published paper, aligning reusable analysis components with real-world toxicogenomics pipelines.
  • Led the design and coordination of a cloud user interface that lets scientists compose and launch Nextcast-based workflows without hand-writing orchestration code.
• Sherpa — Lead Engineer

  Turned a legacy ClearCase codebase into a first-class open-source package for X-ray astronomy analysis.

  • Moved the project into Git, rebuilt the build chain, and started shipping wheels that install cleanly on Linux, macOS, and in containers.
  • Layered in unit, integration, and regression tests with coverage checks so changes stop breaking long-trusted fitting routines.
  • Sat with mission scientists to turn notebook prototypes into features that feel natural in their daily analysis flow.
  • Worked closely with scientists to design and build new features, turning science requirements into reality.
• Iris SED Analysis Tool — Principal Engineer & Co-Lead Developer

  Extensible desktop application for assembling, visualising, and modelling spectral energy distributions across multi-band archives.

  • Integrated VO standards (VOTable, SED DM, SAMP) with Sherpa’s fitting engine so users could ingest, inspect, and model SEDs in one workflow.
  • Built the plug-in framework/SDK that let partners ship custom services and desktop tools seamlessly.
  • Harmonised heterogeneous photometry and spectra into a shared SED schema, easing data ingestion for VAO collaborators and future releases.
• DAME (DAta Mining & Exploration, 2011) — Principal Engineer

  Virtual Observatory-compliant, web-based machine-learning platform for large astronomical surveys.

  • Established a foundational science platform for data mining and exploration in Astronomy.
  • Built browser-driven workflows that let researchers compose clustering, classification, and regression experiments on remote resources.
  • Established an extensible framework for the addition of new models as well as the deployment of the platforms in different environments, from laptops to the GRID.
  • Supervised computer science students for their BA theses.
• Weak Gated Experts Photometric Redshifts (2009) — Method Co-author

  Hybrid machine-learning method combining clustering with specialized regressors to estimate galaxy and quasar photometric redshifts.

  • Delivered an efficient algorithm for photometric redshift estimation.
  • Achieved improved robustness of results by minimizing bias and variance of the algorithm's output.
  • Introduced deep learning concepts in the context of astronomical data analysis.

Skills

• Programming & Scripting: Python, TypeScript, Java, C / C++, Bash & Shell
• Cloud & DevOps: Kubernetes, Containers & Docker, GitLab CI/CD, Infrastructure as Code (Pulumi), GitOps (Helm, ArgoCD)
• Data Platforms & Analytics: Machine Learning Pipelines, JupyterLab & Notebook Workflows, Scientific Data Formats (FITS, HDF5), SQL / NoSQL Data Stores, Observability & Telemetry
• Architecture & Interoperability: Systems Architecture & Design, API & Service Design, Workflow Orchestration, Interoperability & Standards, Platform Extensibility
• Testing & Quality: Automated Testing (Pytest, GTest), Regression Harnesses, Coverage & Quality Gates, Performance Profiling
• Leadership & Collaboration: Technical Roadmaps, Mentoring & Coaching, Iterative Product Discovery, Cross-team Facilitation, Change Management

Publications

• Sherpa: An Open-source Python Fitting Package — H Cheer, A Fruscione, O Laurino · iopscience.iop.org · 2024
• sherpa/sherpa: Sherpa 4.14. 0 — D Burke, O Laurino, HM Günther, A Siemiginowska · ui.adsabs.harvard.edu · 2022
• Astroinformatics of galaxies and quasars: a new general method for photometric redshifts estimation — O Laurino, R D'Abrusco, G Longo · academic.oup.com · 2011
• Chandra Source Catalog Release 2.0-The State of the Art Serendipitous X-ray Source Catalog — JC Houck, J Lauer, O Laurino · ui.adsabs.harvard.edu · 2019
• Nextcast: a software suite to analyse and model toxicogenomics data — M Fratello, L Cattelani, A Federico, O Laurino · Elsevier · 2022
• Learning from FITS: Limitations in use in modern astronomical research — J Good, GB Berriman, S Kitaeff, J Fay, O Laurino · Elsevier · 2015
• Detecting and tracking bacteria with quantum light — G Spedalieri, L Piersimoni, O Laurino · APS · 2020
• VO-DML: a consistent modeling language for IVOA data models Version 1.0 — G Lemson, O Laurino, L Bourges · ui.adsabs.harvard.edu · 2018
• Iris: An extensible application for building and analyzing spectral energy distributions — O Laurino, J Budynkiewicz, R D'Abrusco · Elsevier · 2014
• Vodance: Vo data access layer service creation made easy — R Smareglia, O Laurino · adsabs.harvard.edu · 2011

Talks

• CfA Nexus Science Platform — IVOA Interop - Bologna 2023 · Oct 2023
• VODML Mapping: Ongoing Interoperable Implementations — IVOA Interop - Victoria 2018 · May 2018
• Iris 3.0beta1 — IVOA Interop - Trieste 2016 · Oct 2016
• Implementing the VO-DML Mapping Draft — IVOA Interop - Trieste 2016 · Oct 2016
• Sherpa, Python, and Astronomy - A successful co-evolution — ADASS XXVI Trieste · Oct 2016
• VODML Progress Report — IVOA Data Modeling Working Group - Banff 2015 · Oct 2015
• VODML - Status update — IVOA Interop - Sesto 2015 · Jun 2015
• Iris 2.1 beta — IVOA Interop - Sesto 2015 · Jun 2015
• Spectral DM 2.0 status — IVOA Interop - Sesto 2015 · Jun 2015
• Reference Implementation of Data Models — IVOA Interop - Sesto 2015 · Jun 2015
• UTYPEs Tiger Team Plenary Update — IVOA Interop - Heidelberg 2013 · May 2013
• Knowledge Discovery in Databases for the VO — IVOA Knowledge Discovery Interest Group - Victoria 2010 · May 2010
• Astroinformatics of Galaxies and Quasars (Master's Thesis Defense) — University Federico II of Naples - Department of Physics and Astronomy · Jul 2009

Education

• MSc Physics — University of Naples Federico II

  1997-09-01 – 2009-06-11

  • Specialized in astroinformatics and statistical learning.
  • Thesis “Astroinformatics of Galaxies and Quasars” delivering machine-learning pipelines that later informed the DAME/DAMEWARE platform.