Interested in solving real world problems relating to Artificial Intelligence through ML/DL in order to improve and empower the lives of others.
Experienced in predictive modelling, software engineering, full-stack programming, and algorithmic optimisation.
I am keen to apply my passion, perseverance, technical knowledge, and detail-oriented skills to build and improve applications which target solving complex problems.
Currently looking for a new graduate role.
I worked at CERN for three months, at the CMS Experiment lab, working specifically on the 'CMS-GEM' collaboration, which involved changing particle detector’s technology to Gas Electron Multiplier (GEMs).
Attempted to a develop model which is able to learn to play Flappy Bird, and surpass human level scores by using Reinforcement Learning techniques. Specifically investigated Deep Q-Learning networks to develop an overview of the problem and deeper understanding on reinforcement learning techniques. Wished to showcase how computer vision and deep neural networks such as convolutional neural networks can be used in the context of reinforcement learning as well.
Attempted to solve a Kaggle competition in a group of three to the best of our abilities. Specifically strove for implementations beyond the exsiting classical ones, and attempted to develop a model which is well-adapted and fine tuned to the specific problem at hand. Implemented a Naive-Bayes Bag of Words model, Random Forest, Extra Trees, and compared their results with the Log Regression, Convolutional Neural Network, and Long Short-Term Memory Recurrent models.
Used Bayesian modelling methods, specifically Hamiltonian Monte Carlo, to approximate Gaussian posterior distributions on a multivariate regression task to derive a good predictor from the dataset, and estimate which of the input variabels are relevant for prediction.
Attempted to summarise Jules Verne's 20,000 leagues under the seas' by training a classifier that indicates which of the part of speech tags each word is. The approach was based on Identifying Relations for Open Information Extraction (Fader, Soderland & Etzioni). To this end, Glove word vectors were employed to implement a logistic one vs all kitchen sink model, and attempted speech tagging on word and sentence levels, named entity resolution and relation extraction.
Applied modern Deep Learning techniques on the MNIST dataset, while investigating the effects of choosing various (hyper-)parameters on different architectures and types of networks such as Multi-Layered Perceptrons, Convolutional Neural Networks and Auto-Encoders.
Used a collaborative filtering approach to make individual recommendations to users based on MovieLens database using K-NN algorithm. Improved model performance by finding optimal value of k.
Used Naive-Bayes algorithm to implement a logistic sentiment classifier which sorted tweets based on its sentiment. Observed metrics with Confusion Matrices, then attempted to improve performance by developing Linear SVC n-gram models.
Projects involving most of the classical ML techniques, such as Regression Forests, Decision Trees, K-Nearest Neighbours, Naive-Bayes, Gaussian Processes, Linear Regression and many more. It would be too cumbersome to include each one of them seperately.
Wrote a program using the Java LeJOS framework that enables a robot to explore the arena which contains a small number of obstacles, placed at random locations. There was a single coloured sheet of paper which the robots had to be able to detect using the colour sensor which also signifed the end location, to which the robot had optimally navigate back to the ending position.
Wrote a program using the Java LeJOS framework allowing a robot to determine it's starting location in the arena, and optimally work its way to the pre-determined ending position using scout and doctor agents while avoiding the possible obstacles.
Scran is a user-oriented application that aids in the decision-making process when choosing a restaurant, and more specifically a dish. Scran will maintain, search and track user and restaurant data to help its users to choose the dish they didn’t know they wanted.
Wrote C++ in Xcode to generate random plot and noise values of a sinusoidal function using signal characteristics as parameters, which would then be handled by the designed event driven panels and data structures in LabVIEW, and subsequently transferred to Matlab to be displayed in both filtered and unfiltered states.
Developed iOS app with first generation Swift on xCode.
"Arriving wide-eyed at the main lab on the first day, I discovered that I was among twenty other excited students, from all over the world, ranging from Thailand, Brazil, France, US, India, Italy and many others, all of whom had arrived at different moments during the summer, meaning there was little time for individual introductions to the lab and explanations of the various hardware components and the software code base."
Selected to present on stage my research project on Exoplanets at the Colloque Transfrontalier: La Science en Partage (a public ‘Science Sharing’ event) at CERN’s Universe of Particles museum.