Le projet vise a developper des strategies permettant de combiner plusieurs modeles d’intelligence artificielle (IA) etudies au sein de l’ecosysteme d’intelligence artificielle de l’equipe de prevision de la demande de !’unite prevision des apports et de la demande d’Hydro-Quebec. Ces modeles combines permettront d’obtenir de meilleures predictions a court terme de la charge sur le […]

Prostate cancer is the third deadliest cancer in men and early detection is crucial. PSMA is a protein that is highly present in prostate cells, making it a promising target for imaging and treatment. Total metabolic tumor volume (TMTV) is a measure of tumors’ characteristics, but it is currently not measured in clinical settings due […]

Understanding the current panorama of threat actor groups worldwide is critical to building efficient cybersecurity programs. Information about threat actor groups’ motivations, tools, tactics and techniques they use to attack, and the type of targets they have in their sights provide valuable information to cybersecurity teams. To achieve this goal is essential to generate intelligence […]

As cyber adversaries are becoming more creative, analysts are required to figure out more innovative ways to detect them to be able to respond before it’s too late. To detect any underlying threat inside a system, data logs are collected showing events and activities occurring inside the system. Adversaries nowadays are capable of evading detection […]

As generative models become increasingly prominent in machine learning, the need for accurately detecting out-of-distribution data has become crucial. The primary objective of this research is to develop an approach that can identify when the program encounters data that is vastly different from what it was trained on. In machine learning, programs may make errors […]

This research project focuses on developing an automated system to search and analyze time-series tabular features in the financial institution’s machine learning pipeline. The goal is to identify relevant features and improve efficiency in the decision-making process. The project will begin by prototyping a system to support automated feature search patterns and researching feature search […]

PUBLIC (est. 1988) is a not-for-profit print periodical exploring the intersection of art, design, technology, and culture. This research project advances that history by looking at the future of print culture as today’s artists are increasingly making and exhibiting work in online spaces. How can PUBLIC promote engaging and accessible conversations around artists working exclusively […]

The COVID-19 pandemic has forced children to quickly adapt to home-based or virtual learning; however, a number of researchers have identified challenges and difficulties with applying and using technology. More importantly, there has been a significant rise in the rates of mental illness occurring as a result of COVID-19 and children are now experiencing increased […]

Machine learning attempts to model high-level abstractions in data using multiple processing layers with complex structures or non-linear transformations. Federated learning is a distributed machine learning approach that allows multiple parties to collaborate on training while preserving user data privacy. However, the data from each party is typically non-independent and identically distributed (Non-IID), which can […]

A new curing light, PinkWave (Apex, Racine, WI) was introduced last year (https://vistaapex.com/pinkwave). This light is unique because it has four distinct wavelength bands that deliver red (625 – 750 nm), infrared (800 – 900 nm), blue, and violet light. The manufacturer also claims that this ‘quad-wave’ light can reduce polymerization shrinkage stress.[13] Initial reports […]

The demand for eCommerce and online orders has risen rapidly in recent years, this drives the need for highly efficient and automated item sortation systems. Kindred AI is a technology company with the objective to bring artificial intelligence and robotic technologies into the workforce of eCommerce, parcel and order fulfillment. As a part of the […]

The proposed research aims at learning better representations for multivariate time series (MTS) data, which can be applied to various important real-life applications such as weather, traffic, and electricity forecasting. Better forecasting accuracies for these tasks could help with efficient risk aversion and decision making, and save costs for decision makers. The proposed research will […]