Portfolio

This study used XGBoost and Neural Network models to predict the minimum inhibitory concentration (MIC) values for 13 antimicrobial agents. The XGBoost model selected 29 important features, which were then used as input for the Neural Network model.

A pipeline that comprises several Machine Learning models and image-processing steps. The pipeline allows users to input images of plates and accurately predict wells with growth. The primary objective of this project was to enhance a lab's capabilities, reduce wasted panels, and improve testing efficiency.

This study tried generating new bacterial DNA sequences and new antibiotic compounds. The goal was to see if future resistance can be simulated, and if the generation of new antibiotic compounds could also be simulated.

The project had two objectives. Firstly, to ascertain whether the resistance of a bacterial isolate to various antibiotics could be predicted based on gene expression or mutations (SNPs) in a specific set of genes. Secondly, to investigate whether specific mutations present in the genome of a bacterial isolate could provide resistance to an antibiotic.

This pipeline was built using Machine Learning and statistical analysis to identify bacterial isolates that were outliers during antimicrobial testing. These outliers could be from testing errors, undiscovered resistance mutations, or failed tests. By detecting these outliers, the lab can reduce costs associated with unnecessary testing and identify potential new resistance mutations with minimal human intervention.

A research study aimed to anticipate whether a bacterial isolate would be classified as susceptible or resistant to a specific antibiotic. To ascertain the optimal algorithm for predicting susceptibility, both Decision Trees and Dense Neural Networks were employed. Additionally, correlation analysis was conducted on demographic information to identify any demographics that could serve as input.

This study aimed to evaluate whether dividing the input data based on species, year, and/or continent would enhance the accuracy of Random Forest or K-Nearest Neighbors in predicting antibiotic resistance (MIC values).

A study was conducted to distribute the Lasso algorithm, an extreme Machine Learning technique, using the Message Passing Interface (MPI) on a High-Performance Computer.

A pipeline was built to capture annotations from a bacterial isolate against a known reference gene. This pipeline then allowed different data visualizations to analyze trends easier.

This website was designed to host numerous bioinformatics pipelines, providing Bioinformatics Scientists with fast and easy pipeline building, creation of new ones, and maintenance of existing ones. These pipelines operate on AWS, ensuring minimal downtime for lab personnel due to their large parallelism capabilities.

A pipeline that comprises several Machine Learning models and image-processing steps. The pipeline allows users to input images of plates and accurately predict wells with growth. The primary objective of this project was to enhance a lab's capabilities, reduce wasted panels, and improve testing efficiency.

This pipeline was built using Machine Learning and statistical analysis to identify bacterial isolates that were outliers during antimicrobial testing. These outliers could be from testing errors, undiscovered resistance mutations, or failed tests. By detecting these outliers, the lab can reduce costs associated with unnecessary testing and identify potential new resistance mutations with minimal human intervention.

For a Data Structures class, a pipeline was developed to automatically grade the Java homework of students. The students can work on their homework and periodically push it to a GitHub repository specifically assigned to them and the homework assignment. When the assignment is due, a Jenkins workflow will initiate automatically and grade all homework repositories.

This project was aimed at gathering comprehensive data on the spread of the West Nile virus, including the factors that influence its distribution and the impact it has on the surrounding region.

Extensive migration data on numerous bird species was gathered to study migration patterns with climate data.

Every day, this project scours a roster of websites, gathering user-generated text data for health research. The data is then analyzed in various ways to uncover insights into daily life with various illnesses.

A pipeline was built to capture annotations from a bacterial isolate against a known reference gene. This pipeline then allowed different data visualizations to analyze trends easier.

This website was designed to host numerous bioinformatics pipelines, providing Bioinformatics Scientists with fast and easy pipeline building, creation of new ones, and maintenance of existing ones. These pipelines operate on AWS, ensuring minimal downtime for lab personnel due to their large parallelism capabilities.

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