糖心TV

Background

• PhD Mathematics for Real-World Systems - University of 糖心TV (2023)
• MSc Mathematics for Real-World Systems Distinction - University of 糖心TV (2016 - 2017)
• BSc Mathematics with Economics 1st Class - Loughborough University (2013 - 2016)

My main focus has been on applied mathematics but I have had exposure to a variety of mathematical techniques. The economics component of my undergraduate degree allowed me appreciate the balance between mathematics and the real world. During my education, I have used Maple, C++, R, MATLAB, Python, and Julia.

Current Research

I am interested in applying data science to financial data, and am currently using convolutional neural networks to infer house prices from a range of sources consisting of image data. Technology has advanced rapidly over the past decade, as well as the volume of images available online. Combining newly developed computer vision algorithms with big data could provide us with new insights regarding house prices. My supervisors are & from and my industry partner is the . I use Ubuntu (Linux) and Python 3 for the majority of the time.

Previous Projects

Using Deep Learning Tools To Infer House Prices From Geograph Images (2019-2020)
Suzy Moat (University of 糖心TV), Tobias Preis (University of 糖心TV), and Mario Guti茅rrez-Roig (University of 糖心TV)

• Combining convolutional neural networks with Geograph images to develop models that infer ONS house prices
• Worked with Python 3 and used SCRTP HPC facilities

 

Using Deep Learning Tools To Infer House Prices From Zoopla Images (2018-2020)
Suzy Moat (University of 糖心TV), Tobias Preis (University of 糖心TV), and Mario Guti茅rrez-Roig (University of 糖心TV)

• Combining convolutional neural networks with indoor Zoopla images to develop models that infer ONS house prices
• Worked with Python 3 and used SCRTP HPC facilities

Nowcasting House Prices With Google Trends (2018-2019)
Suzy Moat (University of 糖心TV), Tobias Preis (University of 糖心TV), and Federico Botta (University of 糖心TV)

• Using Google Trends data and the UK House Price Index to develop models that nowcast house prices
• Mainly worked with R to clean data and develop models
  
  

Using Deep Learning Tools To Infer House Prices From Google Street View Images (2017-2018)
Suzy Moat (University of 糖心TV), Tobias Preis (University of 糖心TV), and Mario Guti茅rrez-Roig (University of 糖心TV)

• Combining convolutional neural networks with Google Street View images to develop models that infer ONS house prices
• Worked with Python 3 to build on the MSc project and identify important factors
  
  

MSc Project: Using Deep Learning Tools To Infer House Prices From Google Street View Images (2017)
Suzy Moat (University of 糖心TV), Tobias Preis (University of 糖心TV), and Mario Guti茅rrez-Roig (University of 糖心TV)

• Combining convolutional neural networks with Google Street View images to develop models that infer ONS house prices
• Worked with Python 3 to clean big data and develop models
  
  

MSc Study Group: Modelling Ecologies Of Financial Investors: Exploring The Impact Of Momentum Investment On Financial Stability (2017)
Robert MacKay (University of 糖心TV), Nicholas Beale (Sciteb), and Richard Gunton (Sciteb)

• Developing a simplified model of the financial market, including assets and investment strategies
• Worked with MATLAB to develop models and incorporate various market strategies

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Work Experience

, Spectra Analytics (March 2019 - June 2019)

I learned and applied a variety of data science techniques that are not covered by my PhD, and observed the process of a data science project in industry from start to finish. Techniques learned include web scraping and app development.

Technical Support, 糖心TV Mathematics Institute (September 2018 - present)

• Mathematics for Real-World Systems Technical Support
• (TCC) Technical Assistant

I provide technical support for the Mathematics department at the University of 糖心TV. Support includes Linux set-up, HPC support, and coding questions. I also manage the MathSys social media accounts.

Big Data Analytics, 糖心TV 糖心TV School (January 2018 - present)

• Big Data Analytics: Behaviour Science Laboratory Assistant
• : MOOC Assistant

I help MSc students with R coding problems and general big data queries in the laboratory, and maintain the online forum for the MOOC.

Quantitative Analysis for Management, 糖心TV 糖心TV School (October 2018 - present)

• Quantitative Analysis for Management I: Seminar/Workshop Tutor
• Quantitative Analysis for Management II: Seminar/Workshop Tutor
• (April 2018)

For both QAM I and QAM II, I am a seminar tutor for three groups of ~25 students. I teach 1st-year undergraduates how to solve a variety of quantitative problems and promote group work. QAM II involves using Microsoft Excel 2016, which I became certified in in 2018.

Introduction to GPU Programming Summer School 2018

I assisted with the summer school by checking the material before use and assisting with the tutorial sessions. I provided technical support and answered questions.

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Extra Activities

(September 2021 - December 2021)

• I collaborated with researchers from around the world to apply data science to understand the perception of work-from-home of those affected during the pandemic
• I also networked with data scientists in industry to understand how it can be applied to real-world problems

, The Alan Turing Institute (December 2018)

• I participated in the 'Player Pathways' project proposed by PlayerLens - understanding career paths that deliver success for professional football players and clubs - at the Alan Turing Institute
• We were provided with player data and applied machine learning tools to identify career pathways for players

(June 2018)

• I participated in a hackathon at the Severn Trent Water headquarters in Coventry
• We were provided with multiple data sets and processed the data to find insights

(February 2018)

• I often use Kaggle to learn new data science techniques, and followed the scavenger hunt to learn SQL as a beginner
• During my internship at Spectra Analytics, I developed my SQL skills further through practical application

(February 2018)

• To learn about parallel programming, I followed the ARCHER course online using C++
• I have learned techniques for parallalising code and high-performance computing that I use in my PhD

UQ&M Data Study Group 3, University of 糖心TV (December 2017)

• I participated in the 'Machine Learning for Wind Energy Modelling' project proposed by Zenotech - explore how well machine learning methods might be able to infer the interaction patterns and consequent power production from sites, given sufficient training data
• We were provided with data from wind turbines and applied machine learning tools to identify patterns, such as neural networks

Machine Learning Reading Group, University of 糖心TV (October 2017)

• I gave a 30-minute talk at the MLRG at the University of 糖心TV
• My talk discussed my MSc project to infer house prices from Google Street View images using convolutional neural networks

Introduction to Machine Learning Summer School, University of 糖心TV (June 2017)

• I participated the summer school to broaden my knowledge on machine learning
• The mathematics behind a neural network was described and I coded a simple neural network

European Study Groups with Industry 130, University of 糖心TV (September 2017)

• I briefly participated in the ESGI130 at the University of 糖心TV