AI-Powered Financial Document Intelligence: Transforming SMB & Enterprise Data Management

WizeWerks is developing a cutting-edge AI-powered platform to help businesses streamline financial document processing. Designed for SMBs and enterprises in transportation, logistics, and finance, the platform automates the extraction and organization of key financial data from unstructured documents like invoices and rate confirmations. Using a state-of-the-art Large Language Model (LLM)-agent, it enables users to interact […]

Read More
Development of an Augmented Reality Laparoscopic Training System for Gynecological Surgeries

This project aims to create a training system for laparoscopic procedures focused on gynecological surgeries using a 3D-printed torso phantom. The mannequin features apertures for inserting laparoscopic tools (trocars) and a camera, with visible 3D-printed organs inside. The goal is to develop a training module that integrates virtual anatomical models with real-world views using HoloLens […]

Read More
Technical Process Technician & Data Management Assistant

Bryan & Company LLP is a mid-size full-service law firm in Edmonton, Alberta, with a long history of community engagement dating back to 1928. The firm’s vision for its next 100 years is supported by a strategic plan designed to guide its operations from 2023 to 2028. This strategic plan (the first strategic plan implemented […]

Read More
OQULi online RAG service platform implementation

Integration of R&D efforts produced from the Dalhousie Computer Science team in 2024 on AI Comparison capabilities for the application CODiii Rogue. Successful individual will be compiling an integrated python backend to a newly design UI with the injection of results from previous AI findings. In addition to the work outlined above, this project will […]

Read More
Deep Learning Measurements to Model Ecosystems’ Response to Environmental Change

In many applications, Machine Learning (ML) predictions are used to make downstream decisions. Acting on ML predictions however can change the distribution of features that the ML model relies on for predictions. The implication is that such downstream decisions procedures implicitly expect the ML model to generalize outside of the observational distribution. Unfortunately, this is […]

Read More
New Product Integration – Seojin Woo

MedMe Health, a leader in pharmacy software solutions, is embarking on an innovative project to streamline patient data management through seamless integration with third-party Pharmacy Management Systems (PMS). This project focuses on creating a bidirectional integration between MedMe’s platform and PMS, enabling the automated transfer and synchronization of patient records. As the healthcare industry continues […]

Read More
Efficient Computational Methods for Understanding Back Move-ment and Pain from Dynamic Data Modeling

This project uses machine learning algorithms to better understand back movement and low back pain. We apply supervised learning time series algorithms to data collected from Backtracks’ wearable de-vice — which consists of a malleable think curve that reads data collected from the participants’ spine movements. At each time step, such movements are represented as […]

Read More
Adaptive ML-Driven Detection of Scheduled Task Anomalies and Automated Threat Attribution

As cyber threats grow more sophisticated, attackers increasingly exploit scheduled tasks to maintain persistence and evade detection. Traditional security measures struggle to distinguish between legitimate and malicious task executions, especially when attackers modify execution parameters. Additionally, identifying and attributing threats to known adversaries remains a complex and resource-intensive process, relying heavily on human analysts and […]

Read More
Efficient large scale quantum error characterization

Quantum computing holds great promise in solving some problems that are intractable to its classical counterpart; however, current quantum devices are prone to errors, especially as they scale up. Our project focuses on developing efficient tools to detect and analyze these errors; we will construct an error map that characterizes where the different errors occur […]

Read More
Minimally Invasive Machine Unlearning via Monosemantic Neural Activation Identification

In the age of artificial intelligence, machines learn from vast amounts of data to make predictions and decisions. But what happens when we need them to “unlearn” something—whether to protect privacy, correct biases, or comply with regulations? Existing approaches to machine unlearning can be effective at removing specific data from a model’s memory, but they […]

Read More
A machine learning based approach for supporting triage of HIV-related documents

The number of biomedical scientific publications available in multiple repositories is huge and rapidly growing. As of April 2014, PubMed, the largest knowledge source for biomedical and life science literature, comprises more than 23 million citations. Querying PubMed with the keyword HIV provides a list of almost three hundred thousand citations. Retrieving data of particular […]

Read More