Near downtown Montréal, the Little Burgundy neighbourhood reveals many contrasts. In the south, it touches the Lachine Canal, a beautiful 14-kilometre cycling and pedestrian pathway that sees millions of visitors every year. In the north, it is bordered by the busy and grey Ville-Marie Expressway. One of the most multicultural communities in the city, Little Burgundy is home to upscale restaurants and boutiques, but also to a vulnerable population that struggles with food insecurity.
Ciena is a Canadian company leader in engineering and manufacturing networking systems and devices. The company has around 5,000 operable products in its portfolio. The vast majority of Ciena products generate logs during the boot up and the mission mode operations from the various tasks running on their real time operating systems. The company wants thus to increase its software’s capabilities in order to be able to collect any type of log data generated in the production site and linked to other external information to extract actionable knowledge.
Twenty years ago, the first human genome was sequenced at a cost of 3 billion dollars. Today, this can be done in a day at a cost of approximately $1000. Despite this drastic reduction, the promise of personalized medicine, to customize therapy for each patient, has not yet been realized through next generation sequencing (NGS). While sequencing is becoming a commodity, the data analysis remains a significant challenge. Streamline Genomics addresses these challenges by providing clinicians with a powerful and user-friendly analysis platform.
The project is about developing a decision support tool to provide a personalized handling and treatment for Autistic Syndrome Disorder patients. This decision support tool will integrate machine learning modeling that will be used to suggest optimal and personalized guidelines for a very large spectrum of ASD patients, not available so far. Theses guidelines will be used by concerned parents, teachers and therapist that are in charge of these patients. The partner is a company that offer accompanying and training services to ASD patients’ parents and therapists.
This research project aims at creating a robust, efficient and reliable conversational agent for the banking domain that will offer a high level of performance in both key areas of conversational agent architecture: Natural Language Understanding and Response Generation. Natural language understanding approaches, retrieval-based models, as well as deep learning will be used to develop the architecture of the conversational agent in this specialized domain.
Insurance companies heavily fund marketing campaigns such as, for instance, customer retention or cross-sell initiatives. Uplift modeling aims at predicting the causal effect of an action such as medical treatment or a marketing campaign on a particular individual by taking into consideration the response to an action. Typically, the result of an uplift model is used to call customers for marketing some products based on important attributes of a customer.
Regional Climate Models (RCMs) allow generating climate-change projections into the future over a limited region of the globe at high spatial resolution. The production of large ensembles of simulations from a same RCM is an emerging field of research allowing to explore in detail the interaction between climate change, natural climate variability and extreme events, at the local scale where climate impacts occur.
The project is about developing a «smart store» system that will allow understanding customer and product behavior. This system will be based on Internet of Things (IoT) technologies allowing any object (product or person) to communicate automatically with its environment. Hence, our system will be used for tracking items and monitor consumer behavior in real time. In a retail environment, we will be able to answer questions such as (i) How many times an item has been picked up or tried by a customer? (ii) How long the item stayed off the shelve?
This project is dedicated to the development of a new business corpus as a novel data for the company’s business intelligence. It focuses on linguistic pre-processing for the business domain using two types of collected corpora: text and speech. An automatic annotation of the pre-processed business corpus will be completed using labels related to sentiment analysis and emotion mining technologies. Specific rules will be used to strengthen these labels. Last, a cognitive social analysis on human behaviors and team dynamics will be completed within a business meeting.