Diego Ignacio Lopez, Machine Learning Developer in Bedford, United Kingdom
Diego Ignacio Lopez

Machine Learning Developer in Bedford, United Kingdom

Member since January 28, 2022
Diego is a specialist machine learning developer who is currently conducting academic research on advanced, data-driven reduced-order modeling techniques. His experience includes working with Fortune 500 companies in the aerospace industry, developing models for statistical learning, optimization, uncertainty quantification, and characterization of complex systems.
Diego is now available for hire

Portfolio

Experience

Location

Bedford, United Kingdom

Availability

Part-time

Preferred Environment

Python 3, Pandas, TensorFlow, Keras, NumPy, Scikit-learn, SciPy, Data Science, Linux, PyCharm

The most amazing...

...thing I've created is an AI-assisted global optimization methodology. An article about it was nominated for the best paper by the Journal of Turbomachinery.

Employment

  • Data Scientist

    2022 - PRESENT
    Bank of England
    • Worked as a data scientist to support the data and analytics needs of the Bank of England, UK's central bank.
    • Develop reproducible analytical pipelines and dashboards using python and R.
    • Support team members from various departments to develop their data analytics skills.
    Technologies: Python 3, Tableau, R, Dashboards
  • Data Scientist

    2021 - PRESENT
    Toptal Client
    • Worked individually and with the permanent staff to implement ETL and data pipelines. Analyzed and developed results through statistical learning for a variety of companies.
    • Performed statistical data analysis and data visualization and developed an interactive dashboard to report findings.
    • Performed mixed-integer linear programming for optimizing the roster configuration and line-up for a major basketball team.
    • Developed a scraper to obtain product information and customer reviews from the web. Implemented rotating proxies and randomised delayed requesting to overcome blocks.
    Technologies: Data Science, Data Engineering, APIs, ETL, Data Pipelines, Data Analysis, Python, Data Visualization, English, Jupyter Notebook, Data Extraction, Web Scraping, Linear Regression, Clustering, Scrapy, SQL, Amazon S3 (AWS S3), Dashboards
  • Visiting Researcher

    2021 - 2022
    Cranfield University
    • Developed a 3D variational autoencoder application for parametrizing the geometry of high-pressure compressor blades.
    • Developed a Python library for efficiently training AI-enabled active subspaces, available openly via the preferred installer program (PIP).
    • Performed a statistical analysis on the effect of component re-design on the safety and stability of axial fans and compressors.
    Technologies: Data Analysis, Statistical Learning, Artificial Intelligence (AI), NumPy, SciPy, Research, Statistical Methods, Machine Learning, Data Science, Linux, PyCharm, Engineering, Uncertainty Modeling, Dimensionality Reduction, Optimization, Python, Python 3, Mathematics, Numerical Analysis, Data Visualization, Data Engineering, Data Analyst, Data Analytics, Statistical Data Analysis, Statistics, Uncertainty Quantification, English, Jupyter Notebook, Data Extraction, Linear Regression, Clustering, Scrapy
  • Researcher

    2018 - 2022
    Rolls-Royce
    • Developed a high-fidelity computational model for characterizing the behavior of next-generation aircraft turbofans.
    • Developed software for creating computerized versions of manufactured components known as "digital twins," through which the manufacturing system analysis can be performed.
    • Conducted statistical analysis on manufacturing variability of gas turbine components focused on recovering performance deficits.
    • Performed the wing and fuselage aerodynamic design optimization for the Rolls-Royce electric vertical take-off and landing (eVTOL) concept.
    • Discovered issues in quality-design features in low-pressure system stators and proposed means to reduce stage losses.
    Technologies: Python 3, Optimization, Artificial Intelligence (AI), NumPy, Scikit-learn, SciPy, Research, Data Analysis, Statistical Methods, Machine Learning, Data Science, Linux, PyCharm, Engineering, Statistical Learning, Uncertainty Modeling, Dimensionality Reduction, Python, Mathematics, Numerical Analysis, Data Visualization, Data Engineering, Data Analyst, Data Analytics, Statistical Data Analysis, Statistics, English, Jupyter Notebook, Linear Regression, Clustering, Scrapy
  • Principal Developer

    2020 - 2021
    Freelance
    • Developed an Android app that tracks pregnancy day-by-day, offering detailed information relevant to the user's pregnancy stage.
    • Created the UX design for the application using Adobe XD.
    • Developed the back-end system through Firebase, implementing a user authentication system and real-time database.
    • Implemented an app monetization system based on AdMob and Facebook Ads.
    Technologies: Java, Android, Firebase, Back-end, Front-end, UX, Adobe Experience Design (XD), Google AdMob, Back-end Development, Data Scraping, Web Scraping, Scraping, Data Science, English, Jupyter Notebook, Firebase Analytics, Google Analytics, Mobile Analytics, SQL

Experience

  • Global Optimization of a Transonic Fan Blade Through AI-enabled Active Subspaces
    https://asmedigitalcollection.asme.org/turbomachinery/article/144/1/011013/1115759/Global-Optimization-of-a-Transonic-Fan-Blade

    This methodology predicts the outcome of computational fluid dynamics (CFD) simulations through artificial neural networks. The input data is high-dimensional (tens to hundreds of parameters), and each data point is computationally expensive, taking hours to days to generate. The methodology employs advanced dimensionality reduction algorithms and function reformulation to efficiently train a neural network on a low-dimensional space that is then exploited for prediction purposes on unseen data.

  • Gradient-enhanced Least-square Polynomial Chaos Expansions for Uncertainty Quantification
    https://dspace.lib.cranfield.ac.uk/bitstream/handle/1826/17514/Lopez_AIAA-2021-3073.pdf?sequence=1&isAllowed=y

    This methodology helps exploit gradient information when constructing polynomial chaos expansions for uncertainty quantification. The sampling requirement is significantly reduced, enabling higher accuracy models. Exploited the method to perform robust design optimization of an axial fan for an aircraft engine.

  • BabyClub: Pregnancy Tracker App
    https://play.google.com/store/apps/details?id=com.babyclub&hl=en_GB&gl=US

    BabyClub is an Android application for stage-by-stage pregnancy tracking, including useful utilities like weight management, contraction timer, and to-do lists. User management is handled through Google Firebase and the app monetization is implemented through AdMob.

  • Extending Highly Loaded Axial Fan Operability Range Through a Novel Blade Design
    https://doi.org/10.1115/1.4055350

    My academic work focused on using data-centric reduced-order machine learning modeling to characterize the behavior of complex multidimensional systems. The work employed AI coupled with statistical learning to optimize the performance of an aerospace engine component.

Skills

  • Languages

    Python, Python 3, SQL, Java, R
  • Libraries/APIs

    Pandas, NumPy, Scikit-learn, SciPy, TensorFlow, Keras
  • Paradigms

    Data Science, Mechanical Design, ETL
  • Platforms

    Jupyter Notebook, Linux, Android, Firebase
  • Other

    Data Analysis, Machine Learning, Statistical Learning, Optimization, Data Engineering, Data Analyst, Data Analytics, Web Scraping, Statistical Data Analysis, English, Linear Regression, Engineering, Simulations, Statistical Methods, Research, Artificial Intelligence (AI), Uncertainty Modeling, Mathematics, Numerical Analysis, Data Visualization, Data Scraping, Scraping, Statistics, APIs, Data Extraction, Clustering, Dimensionality Reduction, Modeling, 3D CAD, Assembly Drawing, Uncertainty Quantification, Google AdMob, Back-end, Front-end, UX, Back-end Development, Mobile Analytics, Dashboards
  • Frameworks

    Scrapy
  • Tools

    PyCharm, Adobe Experience Design (XD), Firebase Analytics, Google Analytics, Tableau
  • Storage

    Data Pipelines, Amazon S3 (AWS S3)

Education

  • PhD in Mechanical Engineering
    2019 - 2022
    University of Cagliari - Cagliari, Italy
  • Master's Degree in Aerospace Engineering
    2017 - 2019
    Kingston University - London, United Kingdom
  • Engineer's Degree in Mechanical Engineering
    2012 - 2017
    National University of Rosario - Rosario, Argentina

Certifications

  • Machine Learning, Modelling and Simulation
    DECEMBER 2020 - PRESENT
    Massachusetts Institute of Technology
  • SolidWorks Mechanical Design Professional Certificate
    MARCH 2018 - PRESENT
    Dassault Systèmes

To view more profiles

Join Toptal
Share it with others