Michael Bennett

CV

Education

  • MA Economics

    Yale University, New Haven, CT

    2022 - 2024

    Grade: Honors

    Selected Modules: Microeconomics, Macroeconomics, Computational Economics, Econometrics, Algorithms, Deep Learning

    Example Project: Object detection of cars from satellite images using Deep Learning.

    • Trained, fine-tuned and evaluated YOLOv8n/s/m object detection models on aerial images.
    • Used remote GPU rented via vast.ai, initialised using custom Docker image, increasing training speed over 100x at cost under $10.
    • Wrote custom dataloaders in python to download large image datasets on remote servers at maximal speed, reducing GPU downtime cost.
  • BA Economics

    University of Cambridge, Cambridge, UK

    2017 - 2020

    Grade: First Class

    Selected Modules: Advanced Microeconomics, Advanced Macroeconomics, Advanced Econometrics, Mathematical Economics

    Dissertation: Consumer Preference and Animal Welfare: Variable Population Models of the Farming Sector

Professional Experience

  • Graduate Research Assistant

    Yale University, New Haven, CT

    Oct 2023 - June 2024

    Research software development for project on economic growth and clean energy.

    • Created UML package and object diagrams to visualise and then improve software architecture for better performance, usability, and maintainability.
    • Implemented improved architecture in Julia, upgrading user interface, data processing and first stage solution modules from MATLAB scripts to Julia command line application, leading to 50% runtime improvement and more transparent interface.
    • Used Documenter.jl and GitHub Actions to produce automatically updating website of code documentation, eliminating documentation duplication and improving ease of interaction.
    • Used Python to collect, visualise and analyse geospatial economic and climate data to quantify the empirical relationship between climate and productivity within geographical regions.
  • Genoeconomics Research Assistant

    National Bureau of Economic Research, Cambridge, MA

    Jul 2020 - Jun 2022

    Research software development in a cross-disciplinary statistical genetics and economics lab.

    • Developed two command line applications for genetic data analysis on large human genetic datasets using Python, enabling complex analyses to run in under one hour. Used just-in-time compilation via numba to achieve better performance on non-vectorisable operations.
    • Designed and implemented internal pipelines using Python, bash, and third-party CLI tools such as PLINK for processing of genetic datasets with sizes between 100GB and 1TB on remote high-performance machines.
    • Devised methodology to standardize measures of educational attainment across several national-level datasets. Authored the corresponding section in the resulting paper, published in Nature Genetics (2022) with 204 citations as of August 2024.
    • Developed visualisation software using Python and matplotlib to produce custom publication-ready graphs (including Manhattan plots) and tables, saving development time for other team-members and allowing rapid automated edits prior to submission and resubmission.
  • Operations Research Intern

    Department for Work and Pensions, HM Government, London, UK

    Jul 2019 - Sep 2019

    Researched the extent to which the demographic and socioeconomic backgrounds of working age benefit claimants could predict whether they would contact their administrative authority.

    • Worked with colleagues to gain access to source data, built a working database for analysis from source data using SQL, and automated and documented this process for use by future researchers.
    • Performed exploratory analysis, applied machine learning techniques to develop forecasting methodology and produced visualisations using R.

Leadership Experience

  • Project Manager & Treasurer

    Effective Altruism Cambridge, Cambridge, UK

    Mar 2019 - Feb 2020

    Student-run organisation at the University of Cambridge.

    • Chaired leadership committee meetings and managed a team of 10 other volunteers to plan and run 40 events, achieving 20% increase in attendance compared to the previous year.
    • Authored grant applications leading to £5000 in funding.

Publications

  • [Fifth Author] Okbay, A. et al. (2022). Polygenic prediction within and between families from a 3-million-person GWAS of educational attainment. Nature Genetics 54, 437-449.

  • [Sixth Author] Becker, J. et al. (2021). Resource Profile and User Guide of the Polygenic Index Repository. Nature Human Behaviour 5, 1744-1758.

Independent Projects

  • raytracer

    A C++ ray tracing command line program that implements the ray tracing algorithms from the books "Ray Tracing in One Weekend" and "Ray Tracing: The Next Week" by Peter Shirley. Also adds extra features: new shapes including triangles, and adaptive ray sampler, multithreading, support for diagnostic images, and a robust command line interface.

  • A daily web game that tests users' pro cycling knowledge. Built using Typescript and Next.js (frontend), Go (backend), Postgres + PostGIS (database), AWS (hosting) and Cloudflare (DNS).

  • My personal website, built as a static site using Next.js, Tailwind CSS, and TypeScript, hosted on Github Pages.

  • Customisable chess clock GUI (using iced-rs) and CLI (using termion) application implemented in Rust, backed by a zero-dependency library providing a chess clock API. Built as a quick rust exercise to practice the language and a slightly lengthier exercise to learn one method for building a GUI application.

  • A Python package that provides a very simple interface for reading and performing simple operations on human genetic data in bed/bim/fam format, documented using Sphinx.

  • A Julia package which provides methods for solving dynamic programming problems of one choice variable using Value Function Iteration or the Endogenous Grid Method.

  • Provides custom pure-julia numerical methods for the differentiation, interpolation, minimisation and root finding of univariate and multivariate functions.

  • A static website that presents a piece of graduate school Economics coursework and some interactive 3d graphs produced for it in an attractive and approachable manner.

  • Aerial Image Object Detection

    A Deep Learning project that trains the YOLOv8-obb object detection model on the DOTA and VALID datasets to assess the potential for transfer learning between synthetic and real aerial images.

Skills

Programming Languages

TypeScript

React

Go

Python

SQL

C++

Rust

Julia

Haskell

Bash

Infrastructure and Tools

Docker

Git

AWS

Linux

PostgreSQL

HTML

CSS

Next.js

Tailwind

Engineering

Software Design

Software Testing

Continuous Integration

Continuous Deployment

High Performance Computing

Quantitative

Statistics

Econometrics

Machine Learning

Deep Learning

Data Visualisation

Soft Skills

Leadership

Communication

Problem Solving

Research

Organisation

Awards

  • Adam Smith Dissertation Prize
    2020

    Awarded to the best dissertation among all University of Cambridge Economics students.

  • Forethought Foundation Dissertation Commendation Award
    2020

    Awarded to dissertations that communicate insights relevant to the problem of global priority-setting.

  • Wright Prize
    2019

    Awarded for a First Class Examination result of special merit in official University Examinations.

  • St John's College Pre-Admissions Prize
    2017

    Awarded for exceptional academic achievements prior to admission to St John's College, Cambridge.