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Ronan Doorley, Regression Developer in Dublin, Ireland
Ronan Doorley

Regression Developer in Dublin, Ireland

Member since December 4, 2018
Ronan has several years of experience in data science and machine learning with Python, R, and MATLAB. He has a passion for developing validated models of human behavior and complex systems. Currently, he works with the City Science group at the MIT Media Lab where he develops predictive models of individual behavior such as activity scheduling, location choices, and modes of transportation.
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Portfolio

Experience

Location

Dublin, Ireland

Availability

Part-time

Preferred Environment

GitHub, Jupyter, Spyder, Anaconda, MacOS

The most amazing...

...model I’ve developed simulates a human population from an urban plan and predicts their daily activity schedules and transport mode for each activity.

Employment

  • Mobility Modeling Engineer

    2016 - PRESENT
    MIT Media Lab
    • Developed AI for agents in urban simulations using a cascade of Bayesian Network and Random Forest models calibrated with survey data.
    • Built a Bayesian network model in Python for generating traffic predictions in the country of Andorra; it was based on a combination of cell phone records and traffic counter data.
    • Created also the front-end visualization for the traffic project using Web Sockets and Mapbox GL.
    • Developed a Poisson process model in R to explain the formation of dense clusters of social activity in cities based on the physical urban features.
    • Deployed a Python Flask API which uses a pre-calibrated discrete choice logit model to predict mobility patterns in response to an input describing changes to land use.
    • Used the spatiotemporal data from app usage to develop a discrete choice model in Python to describe how people choose which amenities to visit.
    • Implemented neural networks in Python Keras to model proxy metrics for urban vibrancy based on Google Street View images.
    Technologies: Bayes Net Toolbox (BNT), Random Forests, Analysis, Classification, Scikit-learn, Flask, Python

  • Doctoral Researcher | Teaching Assistant

    2013 - 2016
    Trinity College Dublin
    • Developed a mathematical framework for quantifying the benefits and risks of walking and cycling for transport in urban environments.
    • Designed and built a mobile pollution sensing node on the Arduino platform and used it to characterize the exposures of pedestrians and cyclists to various pollutants in Dublin.
    • Constructed a game theoretical model in MATLAB describing how people change their transportation behaviors in response to changes in the cycling infrastructure.
    • Created a genetic algorithm in MATLAB to find the optimal design of a cycle network, considering the expected behavioral responses and the resulting health and environmental impacts.
    Technologies: Arduino, R, Python, MATLAB

  • Analyst

    2010 - 2011
    Accenture
    • Created functional designs and tested web applications for clients in the financial sector.
    • Worked closely with client resources to determine and document their requirements in areas such as data capture, system functionality, pricing, risk acceptance criteria, automatic document generation, and more.
    • Developed product prototypes using Axure and led teams of about ten developers in building fully functional web applications based on these prototypes.
    • Thoroughly tested the product during development, mainly using automatic testing scripts.
    Technologies: Selenium, Axure

Experience

  • CityScope MoCho
    https://www.media.mit.edu/projects/mobcho/overview/

    This project produced a tool which allows urban planners and community members to interact with a physical model of a city district and see how different urban designs would influence the mobility patterns.

    I built a Flask app which reads the state of the district design from an API and uses a discrete choice logit model to predict the changes in travel patterns and resulting environmental impacts at a regional level. The analysis results are exposed through the Flask API.

  • Reversed Urbanism

    This project developed a regression model to explain and predict where dense clusters of urban activity form, based on the physical characteristics of the area. I first analyzed geolocated telecom data, which was obtained in the country of Andorra, to identify clusters of activity characterized in terms of their size, persistence, and diversity. I then built a lasso regularized multivariate linear regression model in R to order to identify associations between the formation of these clusters and various discrete urban features.

  • Dynamic Traffic Prediction in Andorra: a Bayesian Network Approach
    https://www.media.mit.edu/projects/traffic-andorra/overview/

    In this project, I built a Bayesian network in Python to predict trips made and traffic congestion in Andorra based on a combination of geolocated telecoms data and a small sample of traffic counts.

Skills

  • Languages

    Python 3, R, Python, JavaScript, HTML, C++

  • Libraries/APIs

    Scikit-learn, NumPy, SciPy, Pandas, Matplotlib, Mapbox GL, NetworkX, PySpark, D3.js, LeafletJS, jQuery, Keras

  • Tools

    MATLAB, Spyder, GitHub, Bayes Net Toolbox (BNT), Jupyter, Git

  • Other

    Machine Learning, Research, Statistics, Regression, Classification, Software Development, Probability Theory, Probabilistic Graphical Models, Neural Networks, Axure, Analysis, Bokeh, WebSockets, HTTP, Ajax, Experimental Design, Random Forests

  • Frameworks

    Flask, Selenium, RStudio Shiny

  • Paradigms

    REST, Data Science

  • Platforms

    MacOS, Anaconda, Arduino

  • Storage

    MySQL

Education

  • Fulbright Scholar in City Science
    2016 - 2017
    Massachusetts Institute of Technology | MIT - Cambridge, MA, USA
  • PhD Degree in Civil, Structural and Environmental Engineering
    2013 - 2016
    Trinity College Dublin - Dublin, Ireland
  • Bachelor's Degree in Mechanical Engineering
    2006 - 2010
    Trinity College Dublin - Dublin, Ireland

Certifications

  • Data Manipulation at Scale: Systems and Algorithms (MOOC)
    SEPTEMBER 2015 - PRESENT
    University of Washington via Coursera
  • The Data Scientist's Toolbox (MOOC)
    JULY 2014 - PRESENT
    Johns Hopkins University via Coursera
  • Statistical Inference (MOOC)
    JULY 2014 - PRESENT
    Johns Hopkins University via Coursera

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