• Simulated Environments

  • Generative Models

  • Deep Learning

  • Data Analytics

  • High Performance Computing

About Me

Graduating with a First Class Honours in Computer Science and subsequently being awarded a full PhD scholarship for research in Deep Learning and Big Data analytics. An outgoing researcher who wishes to continue developing technical skills and new approaches while providing meaningful real-world applications. Extensive range of relevant technical ability developed throughout time in academia. Highly motivated and hardworking, consistently looking to better oneself and others. Driven by self-learning and the gripping desire for completion.

Throughout my research as a PhD student, I have taken an experimental stance, pushing for automated systems that can output results while I further my knowledge in topic areas. Creating a framework that can systematically process offshore satellite image data while utilising Convolutional Neural Networks to clean and classify with a continuous value correlated to the images sea height. I am currently in the process of submitting a conference paper with these experimental results, with several posters for upcoming internal and external poster conferences.

Programming

Python

Quick development and prototyping experiments. Extensive use for deep learning in particular applications of convolutional and multilayered neural networks, graphing and statistical analysis.

C++

Uses for highly optimized and niche programming solutions; such as high-performance computing and graphical simulations.

C#

Large time spent using C# at undergraduate, large modular programs utilising abstraction and polymorphic behaviour.

Java

Previous uses include androird mobile app development and game automation tools.

Javascript

Used in both website development and light-weight web driven game creation.

Expertise

Deep Learning

The main focus of my PhD work as present, with a direct interest in image processing through neural networks, both discrete and continuous outputs. Future work aims analyse procedural content and synthetic images using generative models.

High Performance Computing

Research internship generating frameworks to process data through The University of Hull's supercomputer, Viper. Large-scale processing of matrix operations using Cuda and multiple high-end GPUs for deep learning.

Simulations

Dissertation project for undergraduate creating a simulation with the ability to mimic real-world terrains. Providing a huge scale procedural planet which can be traversed in real time.

Data Mining and Analytics

Throughout projects involving large-scale data, consistent access and use of data analysis tools to justify, graph and prepare data sets for future work. Using various supervised and unsupervised techniques to find hidden representations in data sets. Readable visualizations of data through heat maps and 3D plots.

Publications

List of academic posters and paper publications; both first and co-authored

Deep Learning for Wave Height Classification in Satellite Images for Offshore Wind Access

14/09/2018

Summarising preliminary experiments and poster publications into a broader collection of writing on the methodologies involved with; processing large amounts of open source satellite data, cleaning and preparation of the data set and the continuous classification of a wave height for a given satellite image.





Deep Learning for Wave Height Classification in Satellite Images for Offshore Wind Access

11/05/2018

Exploring ability to use large-scale open source satellite imagery with convolutional neural networks for efficient and scalable sea height prediction.





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Utilising VIPER for Parameter Space Exploration in Agent-Based Wealth Distribution Models

01/09/2018

Developing a supercomputing framework with the ability to exhaust parameters in a model or simulation effectively.





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Toolkit Development for Parallelized Agent Based Wealth Distribution Models

21/11/2018

Practical application of previous parameter exploration framework tools, simulation of an agent-based model aiming to reenact how financial population segregation occurs.





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Events

List of formal conferences, talks, presentations and events attended as I pursue my PhD

Global Renewables Energy Conference - Manchester, UK

Poster - Deep Learning for Wave Height Classification in Satellite Images for Offshore Wind Access

19-20th June 2018

Gathering of current problem areas in offshore wind. Poster presented to a group of other technical experts working in the field

CSRG Symposium - Hull, UK

Poster - Scalable Deep Learning of Satellite Images, Preprocessing and Cleaning

7th June 2018

Collection of researchers and lectuers at the Unversirty of Hull, presenation and poster on current topic area. Preprocessing and cleaning of sattilite image data.

IGGI Conference - London, UK

11-13th September 2018

First IGGI conference, connecting with other students and understanding the current state of work within the research group. Multiple relevant and notable talks on procedural content generation (PCG) with applied deep learning techniques specifically Sebastian Risi's talk on Playing and Designing Games through Bio-Inspired AI, with some good takeaways from Charlie Ringer, applying deep learning to the visual outputs of game streams on Twitch. Lastly some good interest from industry with creators of "hellblade" expressing curiosity for deep learning to assist in content generation within games in particular large scale "interesting" terrain.

DARE Renewables Conference - Dublin, Ireland

Paper - Deep Learning for Wave Height Classification in Satellite Images for Offshore Wind Access

14th September 2018

Recorded presentation submitted alongside a technical paper for submission in Springer AI (LNAI)

Please email me with any enquiries ryan.spick@hotmail.co.uk