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About me

Hi! I'm Bastien and I am a final (4th) year MEng student at Imperial College London with an interest and specialty in the machine learning and data analytics/data engineering field.

Originally being born and raised in the Philippines, I moved to London where a range of engineering topics soon piqued my interest, from Power Electronics to Sustainability. As such, I eventually went on to study an MEng degree in Electrical and Electronic Engineering with Management at university. 

During my studies at Imperial College, I developed a particular interest in the various Machine Learning and Deep Learning modules, as well as the A.I. industry, in general. Having completed a range of internships and projects from software development and project management to data science and data engineering at different notable companies, I am especially interested and look forward to opportunities for a career where impactful change is driven through innovative use of data.

Outside of work, I also take part in a wide range of extracurricular activities, from cultural societies to sports, namely basketball, badminton and target shooting!

  • LinkedIn
  • GitHub
  • Medium


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Business Intelligence Engineering



April - September 2023

During my 6-month placement as a Business Intelligence Engineering (BIE) Intern, I had the opportunity to grow, develop and apply my skills in the Machine Learning, Data Analytics and Data Engineering space to real-world complex business problems.

Some of my most notable achievements include:

  • Taking full ownership of a particular defect in the EU Inbound Supply Chain organisation by:

    • Running office hours.​

    • Improving and aligning detection precision from 91% to 98%.

    • Developing and launching a full root-cause attribution bridge.

    • Liaising with software and vendor/carrier management teams to minimise Amazon-attributed sub-defects.

  • Achieving an all-time minimum defect rate with calculated savings of $315K over a 4-month period.​​

  • Creating Support Vector Machine (SVM) models for a holistic analysis of all the different factors contributing to another defect, to challenge payment policies for 52.7% of vendor deliveries.

  • Proposing and pushing code modifications to nullify up to a 76.7% data discrepancy between multiple tables in a pipeline, and optimising those queries to achieve a ~65% reduction in runtime.

  • Defining the mechanism and pipeline for a new defect, as well as the codebase to integrate into 3 other defects' pipelines.

Other achievements:

  • Achieving 2nd place globally in the intern ML challenge.

  • (Fun fact) Participating in the successful Guinness World Record attempt for "Most viewers of a Mental Health Awareness lesson, live stream on a bespoke platform".

The delivery of these results were made possible through determination to thoroughly dive deep into the data surrounding business problems from both a technical and business perspective.


Huawei Seeds for the Future



June - July 2022

  • Chosen among 30 U.K. scholars to participate in a course of studying AI, Data Science, 5G, Cloud Networking, IoT and Blockchain technologies.


  • Developed and pitched a public app centered around Computer Vision AI and GPS to verify eco-friendly actions to promote sustainable lifestyles nationwide.

  • The idea and presentation had the scalability potential to reduce 6.8M tonnes of CO2 from the U.K.'s carbon footprint.

  • Finalist group for U.K., Lebanon and Jordan programme.


Insight Programmes

Morgan Stanley, Schroders, OpenText

Insight Programmes (Spring/Summer)


Completed a range of Insight Programmes in various companies to gain exposure in topics from Investment Banking and Asset Management to FinTech and the use of AI in fraud detection.


Undergraduate Research 

   - AI and Sustainability

Imperial College London - RSM

Undergraduate Research Programme

May 2022 - April 2023

  • Academic research under the supervision of Prof. Matthew Piggott from the Royal School of Mines, Imperial College London.


  • Research topic: Investigating the use and potential of AI (including Machine Learning and Deep Learning) on the U.K.'s electricity grid network with a focus on the sustainability potential of the demand side.

  • Research includes reviews of current and industrial AI applications; testing and developing of different models and techniques, e.g. Different Stability Assessments (Transient, Small-signal, etc.), Fault Detection, Nonintrusive Load Monitoring (NILM) and Load Forecasting, etc.

  • Particular point of interest: Improvements to LSTM-based forecasting models using transfer learning from non-grid data (e.g., training data for calibration of financial stochastic models) and grid data from France and Texas to achieve higher accuracies in the forecasting grid load in the U.K., as well as traditional ARMA/SARIMA approaches.

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Project Management



July - September 2022

  • Led and participated in various sustainability-focussed technical projects, working with multiple teams across the world.


  • Software development projects involved formulating ML algorithms to optimise and reduce different control systems' energy usage and CO2 emissions.


  • Final project: Developed (from scratch) the full stack of a data mining and analytics web app/tool that was able to eliminate 15 metric tonnes of CO2 emissions, with a worldwide scalability.


  • Also conducted a "deep dive" into acquiring European customer data for the purpose of both:

    • training data for a neural network that computes optimal delivery routes with the minimum carbon emissions.

    • and to incorporate existing Sustainability-Tech teams in the U.S. to Europe from a commercial perspective.


Low Carbon Engineering & Research

Western Power Distribution (National Grid)


June - August 2021

  • Recipient of the Power Academy Engineering Scholarship.


  • Audited the business' progress to Net Zero over 5 years and quantified customer engagement after the launch of a Carbon Tracer app and a new scheme for the analysis of customers' energy demand.

  • Contributed to the planning of the new Price Control Plans for 2023-2028, which included replacing 79% of the commercial van fleet to be non-carbon vehicles, as well as contributing to the forecast of demand and cost estimation for the 5-year period across all 6 power generation technologies/areas.

  • Analysed the financial and carbon impact of remote working post-pandemic to design and propose a hybrid-working model that would reduce the business carbon footprint (BCF) by >13%.

  • Analysed the available resources across 1335 substations to verify a carbon insetting solution that would be implemented into 70% of all U.K. substations.




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Filipino Society

Imperial College London

President 2022-23

Social Events Coordinator 2021-22


Institute of Engineering and Technology (IET)

Young Professional Ambassador 2021-present

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Electrical Engineering Society (EESoc)

Imperial College London

Secretary 2022-23


Rifle & Pistol Club

Imperial College London & St. Mary's

Gallery Rifle Captain 2022-23

Notable Skills

  • C

  • C++

  • Python

  • Verilog

  • SQL

  • HTML


  • JavaScript

  • Julia

  • Shell/Bash

  • Spark

    • SparkSQL

    • Apache Spark

  • Machine Learning

  • Deep Learning

  • Artificial Intelligence

    • (Computer Vision and NLP)

  • Data Analysis

    • (Root cause and Statistical Analysis)​

  • Data Engineering

    • (ETL Pipelining and Data Warehousing)

  • Business Intelligence​

    • (AWS Quicksight, Tableau and Power BI)​

  • Project Management

Notable Awards

  • Power Academy Engineering Scholarship

  • Arkwright Engineering Scholarship 

  • Huawei Seeds for the Future Programme Finalist

  • Amazon Global Intern Machine Learning Challenge (2nd place)



Please feel free to get in touch using the contact form and I will reply as soon as possible!​

Alternatively, email:

  • LinkedIn
  • GitHub
  • Medium

Thanks for submitting!

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