Diego Ignacio Lopez, Developer in Bedford, United Kingdom
Diego is available for hire
Hire Diego

Diego Ignacio Lopez

Verified Expert  in Engineering

Machine Learning Engineer and Developer

Location
Bedford, United Kingdom
Toptal Member Since
January 28, 2022

Diego is a specialist machine learning engineer 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.

Portfolio

Bank of England
Python 3, Tableau, R, Dashboards, SQL, Data Matching, Data Scientist...
Toptal Client
Data Science, Data Engineering, APIs, ETL, Data Pipelines, Data Analysis...
Cranfield University
Data Analysis, Statistical Learning, Artificial Intelligence (AI), NumPy, SciPy...

Experience

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.

Work Experience

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, SQL, Data Matching, Data Scientist, Data-driven Dashboards, Reports, Heatmaps, Data Scraping, Data Structures, Data Cleansing, Web Applications, RStudio Shiny, Teaching, Team Mentoring, Metrics, CSV, FastAPI, Azure, Microsoft Azure, Time Series, Time Series Analysis, XGBoost

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, Linear Programming, Mathematical Modeling, Data Matching, Data Scientist, Data-driven Dashboards, Reports, Heatmaps, Data Scraping, Data Structures, Data Cleansing, Web Applications, RStudio Shiny, Metrics, CSV, FastAPI, Azure, Microsoft Azure, Time Series, Time Series Analysis, XGBoost

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, Modeling, Dimensionality Reduction, Optimization, Python, Python 3, Mathematics, Numerical Analysis, Data Visualization, Data Engineering, Data Analytics, Statistical Data Analysis, Statistics, English, Jupyter Notebook, Data Extraction, Linear Regression, Clustering, Scrapy, Linear Programming, Mathematical Modeling, Data Matching, Data Scientist, Reports, Heatmaps, Data-driven Dashboards, Data Structures, Data Cleansing, Teaching, Metrics, CSV, XGBoost

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, Modeling, Dimensionality Reduction, Python, Mathematics, Numerical Analysis, Data Visualization, Data Engineering, Data Analytics, Statistical Data Analysis, Statistics, English, Jupyter Notebook, Linear Regression, Clustering, Scrapy, Linear Programming, Mathematical Modeling, Data Matching, Data Scientist, Reports, Heatmaps, Data-driven Dashboards, Data Structures, Data Cleansing, Teaching, Metrics, CSV, XGBoost

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, User Experience (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, Reports, RStudio Shiny, Metrics, CSV

Global Optimization of a Transonic Fan Blade Through AI-enabled Active Subspaces

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

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

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.
2019 - 2022

PhD in Mechanical Engineering

University of Cagliari - Cagliari, Italy

2017 - 2019

Master's Degree in Aerospace Engineering

Kingston University - London, United Kingdom

2012 - 2017

Engineer's Degree in Mechanical Engineering

National University of Rosario - Rosario, Argentina

DECEMBER 2020 - PRESENT

Machine Learning, Modelling and Simulation

Massachusetts Institute of Technology

MARCH 2018 - PRESENT

SolidWorks Mechanical Design Professional Certificate

Dassault Systèmes

Libraries/APIs

Pandas, NumPy, Scikit-learn, SciPy, XGBoost, TensorFlow, Keras

Tools

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

Frameworks

RStudio Shiny, Scrapy

Paradigms

Data Science, Linear Programming, Mechanical Design, ETL

Languages

Python, SQL, R, Python 3, Java

Platforms

Jupyter Notebook, Linux, Azure, Android, Firebase

Industry Expertise

Teaching

Storage

Data Pipelines, Amazon S3 (AWS S3)

Other

Data Analysis, Machine Learning, Statistical Learning, Optimization, Data Engineering, Data Analytics, Data Scraping, Web Scraping, Statistical Data Analysis, English, Linear Regression, Data Scientist, Data-driven Dashboards, Reports, Data Structures, Data Cleansing, Metrics, CSV, FastAPI, Time Series, Time Series Analysis, Engineering, Simulations, Statistical Methods, Research, Artificial Intelligence (AI), Mathematics, Numerical Analysis, Data Visualization, Scraping, Statistics, APIs, Data Extraction, Clustering, Dashboards, Mathematical Modeling, Data Matching, Heatmaps, Web Applications, Team Mentoring, Microsoft Azure, Dimensionality Reduction, Modeling, 3D CAD, Assembly Drawing, Google AdMob, Back-end, Front-end, User Experience (UX), Back-end Development, Mobile Analytics

Collaboration That Works

How to Work with Toptal

Toptal matches you directly with global industry experts from our network in hours—not weeks or months.

1

Share your needs

Discuss your requirements and refine your scope in a call with a Toptal domain expert.
2

Choose your talent

Get a short list of expertly matched talent within 24 hours to review, interview, and choose from.
3

Start your risk-free talent trial

Work with your chosen talent on a trial basis for up to two weeks. Pay only if you decide to hire them.

Top talent is in high demand.

Start hiring