Software
Mapping brain activity
We utilized principles and knowledge of biostatistics to analyze brain imaging data obtained from healthy individuals and individuals with Autism Spectrum Disorder. The data was collected using functional magnetic resonance imaging (fMRI), a powerful research and clinical tool that allows for the measurement of brain activity by detecting changes in blood flow. Based on this information, I estimated the strength of communication between different brain regions
FIFA dataset analysis
The objective of this project was to conduct a statistical analysis of football data in order to make informed predictions about key aspects of the game, including player performance, team formations, and potential winners. We applied machine learning techniques to a variety of relevant features, such as the number of goals scored per team and the margins of victory. We also considered individual player performances based on awards won during the tournament. Our goal was to optimize the accuracy of our predictions through the use of these advanced analytical methods.
Exploring viral Genomes
Our project aims to provide researchers with a central source for high-quality bioinformatics tools. We have developed a custom Python class specifically designed for virus genome analysis, giving us full control over its design and implementation. This allows us to optimize the class for our specific needs and ensure that it is the most effective tool for the task. Unlike third-party libraries like Biopython, our class is tailored to the specific challenges of virus genome analysis. It can be used to perform genome analysis on FASTA files, a common file format for storing nucleotide or protein sequences.
The topological structure of the World Airline Network
In this project, I embarked on a journey of exploration to uncover the hidden structure of the World Airline Network (WAN). I applied different centrality metrics to rank the importance of different airports in the network, and I discovered that the clustering of airports is not solely dependent on geographical location. Through this process, I uncovered the community structure of the WAN and gained a deeper understanding of its overall macroscale organization. I was excited to delve into this complex dataset and see what it could reveal about the world of air travel
SIR model of covid spread in Botswana
In the early days of the COVID-19 pandemic, I decided to apply a commonly used epidemiological model to demonstrate the potential severity of the outbreak. I used a Susceptible-Infected-Recovered (SIR) model, which is designed to predict the number of individuals who are susceptible to infection, are actively infected, or have recovered from infection at any given time. This model allowed me to explore the dynamics of the epidemic and make informed predictions about its potential spread. I was excited to use my technical skills to gain a deeper understanding of this critical issue and help inform public health policy and decision-making.