David Azcona's Github

A repository for organizing my repositories, research and projects.

Latest

1. Memorability. Insight@DCU participation in the Memorability Challenge at MediaEval 2019. The task is to predict how memorable a video is to viewers. Techniques used: Traditional Machine Learning & Deep Learning with Embeddings for video captions, Transfer Learning w/ Neural Network activations as features and fine-tuning our own networks.

Hackathons

1. Prometheus, Web application, Real-time detection. Combination of auto-piloted drones & Computer Vision to detect wildfires while they are still in their early stages. Trained a Deep Neural Network using transfer learning technologies & AlexNet, CUDA, Microsoft CNTK & Azure, Docker. Partnered with Arizona State University & Arizona Fire Department. Microsoft Imagine Cup 2018 Awards: USA 1st. place in Artificial Intelligence & 4th overall. World Finals: Top-6 in AI & Semifinalists
2. Huawei Celebrity Hunt: Deep Learning Face Recognizer. Crawled the web for pictures, learned embeddings using CNN with CUDA and detected the celebrity using triplets & similarity learning. Huawei's Vision Challenge in Ireland: 4th place and 2nd fastest
3. BlindSpot AI: AI to help blind & visually-impaired people find objects using voice-activated phone scanning. Tensorflow & ImageNet Object Detection Android App. Special Mention award at 2018 Startup Weekend in Google Ireland
4. LoanDog AI: Adaptive Trust Score for Students using their social interactions (Twitter, LinkedIn, etc.) and banking transactions to build a digital footprint that enables Banks to risk assess them for Student Loans. Winners of the Ulster Bank Hackathon 2019 at Dogpatch Labs
5. ONbank: We use Computer Vision & face recognition with OpenCV to help people with low digital skills. Special Mention at Royal Bank of Scotland’s Fintech Hackathon in Edinburgh, United Kingdom
6. Sign2Text: Deep Learning and Computer Vision Sign Language real-time Translator to English using Drone technologies. Competed at 2018 Intel Atrovate AI Hackathon
7. Canary AI: ChatBot for miners that uses Computer Vision, AI & Data Analytics to achieve a zero-harm workplace. Runners up at Trinity Learnovate's Digital Education Hack organised by the European Commission
8. Hack the Crash: Machine Learning pipeline to predict accident severity and a ChatBot that provides bite-sized advice to drivers. Winners at HackUPC 2019 for McKinsey & Company's challenge in Barcelona, Spain
9. Earthquake Damage. Runners up at McKinsey & Company's CityCup 2019 in Madrid, Spain
10. LocoDrone: Collaborative drone flying. Winners of Most Inventive Hack & Hackers' Choice awards at Oxford Hack 2019, Oxford University, United Kingdom
11. Elephant AI. Create visual mind maps from text using NLP techniques, enabling better & quicker memory recall

Tools & APIs

1. bibtex.online: Convert your BibTeX bibliographies into text on the fly!
2. InclusiveAI: Deep Learning technology to unbias recruiting. We developed tools to remove biases in recruitment such us scrubbing any kind of personal information from resumes. We apply the latest Computer Vision techniques to remove gender, race and age from pictures and Natural Language Processing techniques to remove personal details.
3. Map4all: Find shops near you to spend your one4all vouchers.
4. Lezkairu

Boilerplates

1. Flaskerizer: Skeleton of a Flask app
2. Flask & Keras: Flask Boilerplate to consume a Machine Learning model by uploading a picture
3. DialogFlow & Flask: ChatBot Boilerplate using DialogFlow and a Flask webhook
4. Scikit-learn & Flask: Scikit-learn & Flask using the Zoo dataset
5. Flask & Azure: Python, Flask, Azure App Service Web
6. Docker & Flask: Flask, Docker & Gunicorn
7. Docker, Flask, Nginx & WSGI: Flask for production using Docker, Nginx & WSGI
8. Flask & Flask-Login: Flask Login Boilerplate to have a ready web app with user authentication
9. Flask & Google Login: Flask Login with Google Login Boilerplate to have a ready web app with user authentication provided by Google

Machine Learning

1. Predictive Modeling for Student Classification: Automatically detecting students at-risk of failing a computer-based examination in computer programming courses at Dublin City University
2. CNN: Dogs vs Cats: classification model to distinguishing images of dogs vs cats using Keras
3. CNN: Dogs vs Cats vs Pandas
4. GAN: MNIST: GAN developed with Keras & MNIST

Data Mining

1. Dublin City University's first-years: Analysis on 16K first-year students

Engagement Studies

1. Student Interventions: Adaptive feedback in computer programming modules at Dublin City University

Embeddings

1. user2code2vec: Embeddings for Profiling Students Based on Distributional Representations of Source Code. Full research paper presented at Learning Analytics & Knowledge 2019 Conference in AZ, USA (LAK 2019)
2. Irish Politics: Learning representations in Irish Politics. I authored a publication on the increasingly popular RTÉ Brainstorm in collaboration with Dublin City University's School of Law and Government

Data Visualization

1. My own Viz template: Python, MongoDB, Flask, Bootstrap, Javascript, D3, NVD3, NetworkX, JSNetworkX, Docker
2. Twitter: Python, Flask, Bootstrap, Javascript, Docker, Tweepy, TextBlob, Leaflet

Network Analysis

1. EdX Math MOOCs: Modelling Math Learning on an Open Access Intelligent Tutor
2. Sample Networks: Les Miserables, Karate and Enron email datasets

Chatbots

1. Code Assistant: Whatsapp Artificial Intelligence ChatBot to support learning of computer programming. Check out also its proof of concept

Reinforcement Learning

1. Unity's Obstacle Tower Challenge: the goal was to train an agent that traverse the floors of a procedurally generated tower and climb to the highest level possible

Projects for Companies

1. Panalpina: Web application for Panalpina to visualize 3D Reconstruction using Computer Vision

Thesis Template

1. My own template: Dublin City University's School of Computing LaTeX Template, published as an Overleaf's teamplate
2. My ACM Proceedings template: Template to develop programatically the proceedings of an ACM Conference using Python, LaTeX, PDFtk and more

Contributions

1. Keras tutorial: forked from K. Nathaniel Tucker's

Algorithms & Data Structures

1. Basic Algorithms

Website

1. My personal website: https://computing.dcu.ie/~dazcona/ using Jekyll, Jekyll-Scholar and Bootstrap.
2. dazcona.github.io

Other Resources

1. Fabric: Pythonic remote execution

Online presence

• Github
• Google Scholar
• Doras
• LinkedIn
• Twitter
• Facebook