Online application for problem-solving by individual and groups

Group and individual problem-solving play important roles in industrial innovation, product development, strategy formulation, and many other applications. Prof. Duimering’s lab at the University of Waterloo has conducted experiments investigating the behaviour of individuals and groups solving problems under various conditions. Studies have investigated the effects of problem complexity, individual vs. group incentives, and the […]

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Modeling dynamics of the lateral geniculate nucleus of the thalamus

The proposed project aims to advance computational neuroscience by enhancing the MouseNet model, a Convolutional Neural Network (CNN) designed to emulate the structure and function of the mouse visual cortex. By incorporating 3D convolutions and realistic temporal dynamics, the project seeks to model the lateral geniculate nucleus (LGN) dynamics more accurately. This enhancement will provide […]

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Combining non-invasive brain stimulation with machine learning techniques to predict cognitive-sensorimotor interactions during skilled behaviours in healthy and clinical populations

The project aims to enhance our understanding and ability to predict the dynamics of cognitive and sensorimotor functions in skilled behaviors. Through the integration of non-invasive brain stimulation (like TMS) and machine learning algorithms, this research seeks to develop models that can foresee cognitive-sensorimotor interaction outcomes both in individuals with neurological conditions and healthy subjects. […]

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The impact of user and virtual agent dominance in collaborative work

Our research project aims to explore two interconnected objectives: understanding the impact of dominance in the collaboration between virtual assistants and human operators, and examining how an individual’s level of dominance affects their perception of a virtual assistant’s dominance during initial interactions. To achieve these goals, we will conduct a user study where participants, interacting […]

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Measuring Structural Diversity using Lacunarity

Forests are important for a variety of ecological, social, and economic values. With climate change, forest ecosystems are globally impacted. More diverse, complex forests are thought to be resilient to climate change impacts. Forest complexity is a well established concept , yet poorly quantified. For forest managers and conservation biologists to make informed decisions, quantitative […]

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Multimodal RAG Explainability

This project “Multimodal RAG Explainability (MRAGE)” aims to develop an interactive tool for explaining Large Multimodal Models (LMMs) augmented with retrieval capabilities. LMMs represent a significant advancement in AI, capable of understanding and generating content across multiple modalities like text and images. By employing the retrieval-augmented generation (RAG) methodology, this project queries external knowledge sources […]

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Developing Data Strategies to Enable Healthcare Machine Learning

The project “Developing Data Strategies to Enable Healthcare Machine Learning” aims to develop effective strategies to collect, curate, and maintain data in healthcare. A data strategy which enables Artificial Intelligence (AI)/Machine Learning (ML) models plays a pivotal role in building response healthcare solutions. The project is focused on understanding the fairness aspects of care quality […]

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Evaluating LLMs for Sentence Encoding and Clustering to support Thematic Analysis in Qualitative Research

The project “Evaluating LLMs for Sentence Encoding and Clustering to support Thematic Analysis in Qualitative Research” aims to provide qualitative researchers with advanced machine learning techniques for social media data analysis while maintaining autonomy and ownership of their data analysis. By benchmarking modern Language and Large Language Models against established metrics such as coherence and […]

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Exploring Age-Related Changes in Multisensory Integration using Machine Learning tools

The project “Exploring Age-Related Changes in Multisensory Integration using Machine Learning tools” aims to investigate how changes in neurotransmitter concentrations influence the way young and older adults integrate multisensory information and perceive time. Its main method of investigation involves applying machine learning tools to analyze the extensive dataset collected from behavioral tasks, Magnetic Resonance Spectroscopy, […]

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Abnormality Detection Using Variational Autoencoder: An Application in Banking System

This internship project focuses on Variational Autoencoders (VAEs) for the precise identification of fraudulent activities within the banking sector, notably on check fraud detection. The literature shows that VAEs are very powerful in extracting principal features and components of a given dataset. This project meticulously outlines a comprehensive methodology where VAE is utilized for analyzing […]

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