David Galera

01

Real Time Chat web application

Fullstack Real time Chat application built with React + Material UI, Apollo GraphQL server running on NestJS and MongoDB as persistence layer. Implementing GraphQL websocket subscription for real-time communication, managing Apollo client to cache GraphQL queries on the React app, leveraging MongoDB aggregation pipelines to build GraphQL aggregated entities using a single MongoDB collection as well as chats list and messages pagination, JWT based authentication and more.

02

NeuralNet for manufacturing control data

Kaggle competition project. Given a balanced dataset, consisting of 900.000 instances with 33 features from manufacturing control data, I built a neural network using keras to predict the binary output. I applied feature engineering and feature interaction to spot patterns and transform the data to achieve 97.4% accuracy in the competition.

03

Computer vision applied to CIFAR-10

Image classification of the CIFAR-10 dataset using deep learning and the fastai library. I used transfer learning with resnet34 and resnet50 pretrained CNNs, applying data augmentation techniques such as mixup.

04

Movie recommender engine

Recommender system on the movielens dataset implemented in Python. I implemented 4 recommendation approaches:

• - Non personalized
• - Content based, a Naive Bayes spam filter is also implemented.
• - Colaborative filtering, item-item and user-user.
• - Colaborative filtering using the Python surprise library.

05

Density estimation

This project consists of 2 parts. In the first one, I estimate the probability density function of univariate data using kernel methods (non-parametric) . I fit the kernel bandwidth using 2 approaches: MISE and K-Fold cross-validation.
In the second part, I fit a Gaussian Mixture to multivariate data from diabetes patients, tunning the covariance type and number of components with K-Fold CV.

06

Labyrinth

The goal of this project is to design an efficient algorithm that returns the minimum number of moves required by a rod to traverse a rectangular input grid, from the top left corner to the bottom right corner, cells can be blocked and the rod is composed of 3 adjacent cells that can rotate by its center cell if the rotation area does not contain blocked cells. Only Python builtin classes are allowed.

07

MaxSAT optimal correlation clustering

CORRCLUSTERING ∈ NP
In this project, I created an instance of the CORRCLUSTERING problem and performed a polynomial-time reduction to SAT. I obtained a propositional formula by reducing the problem to the HARD and SOFT clauses described in the paper published in 2013 by Jeremias Berg and Matti Järvisalo titled Optimal Correlation Clustering via MaxSAT. Finally, I applied a MaxSAT Solver to obtain a Maximum SATisfiability resulting clustering.