www.braintank.ca

Category: Academic

Examples of academic projects.

  • Using Visual Transformers in Sequential Salience Prediction

    Using Visual Transformers in Sequential Salience Prediction

    Collaborators: Dr. Neil Bruce

    Job Title: USRA Recipient, Undergraduate Researcher

    University: University of Guelph

  • Using Self Supervised Learning for Annotation Reduction in Segmenting Beef Cuts

    Using Self Supervised Learning for Annotation Reduction in Segmenting Beef Cuts

    Collaborators: Dr. Dan Tulpan, Dr. Luiza Antonie

    Job Title: Undergraduate Thesis Writer

    University: University of Guelph

  • Using Machine Learning to Predict Antibacterial Resistance

    Using Machine Learning to Predict Antibacterial Resistance

    Collaborators: Dr. Dan Gillis, Dr. Theresa Bernardo, Matthew Kreitzer, Rashi Mathur, Xavier Ifill, Luc Dube

    Job Title: Undergraduate Data Scientist

    University: University of Guelph

    Program Overview

    Problem / Description

    The widespread and often unnecessary use of antibiotics in humans, livestock, and agriculture accelerates the evolution of bacteria, leading to the development of resistant strains, making antibacterial resistance progressively worse.

    Specifically, the overuse of antibacterials on companion animals is under researched.

    Research team of Dr. Theresa Bernardo and Matthew Kreitzer, is working with the class of CIS*4020 to create a dashboard prototype that vets can use to predict which drugs are resisted by which bacteria.

    We are given access to a private dataset of patient information, and infection information as features, and are tasked on predicting susceptibility test results.

    Method

    We designed an experiment to compare the efficacy of neural networks vs. k-clustering techniques on predicting antibacterial resistance.

    We find large gains in both of the models, by adding interdependent meta information from other entries in the dataset. (e.g. How do patients near you effect the diseases you can get? How does diseases change over time? How do repeat patients differ from one-time patients?)

    We will present our findings in a dashboard, and a presentation to the research team.

    Results

    Results are not yet public. Will be updated



  • Reward Augmented Expectations in Decision Making Problems for Long-Horizon Planning

    Reward Augmented Expectations in Decision Making Problems for Long-Horizon Planning

    Collaborators: Dr. Animesh Garg, Raeid Saqur

    Job Title: Undergraduate Research Assistant

    University / Lab: PAIR Labs, Vector Institute, University of Toronto

  • Analysing Siamese Neural Network Architectures for Computing Name Similarity.

    Analysing Siamese Neural Network Architectures for Computing Name Similarity.

    Collaborators: Dr. Luiza Antonie, Jeremy Foxcraft

    Job Title: Undergraduate Researcher

    University: University of Guelph